{"message":"Success","data":{"title":"NOC:Applied Multivariate Statistical Modeling","course_id":"111105091","is_public":true,"course_downloads":[{"title":"Lecture 01 : Introduction to Multivariate Statistical Modeling","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod01lec01.mp4","mimetype":"video/mp4","lesson_id":"1"},{"title":"Lecture 02 : Introduction to Multivariate Statistical Modeling: Data types, models, and modeling","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod01lec02.mp4","mimetype":"video/mp4","lesson_id":"2"},{"title":"Lecture 03 : Statistical approaches to model building","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod01lec03.mp4","mimetype":"video/mp4","lesson_id":"3"},{"title":"Lecture 04 : Statistical approaches to model building (Contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod01lec04.mp4","mimetype":"video/mp4","lesson_id":"4"},{"title":"Lecture 05 : Univariate Descriptive Statistics","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod01lec05.mp4","mimetype":"video/mp4","lesson_id":"5"},{"title":"Lecture 06 : Univariate Descriptive Statistics (Contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod01lec06.mp4","mimetype":"video/mp4","lesson_id":"6"},{"title":"Lecture 07 : Normal Distribution and Chi-squared Distribution","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod02lec07.mp4","mimetype":"video/mp4","lesson_id":"7"},{"title":"Lecture 08 : t-distribution, F-distribution, and Central Limit Theorem","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod02lec08.mp4","mimetype":"video/mp4","lesson_id":"8"},{"title":"Lecture 09 : Univariate inferential statistics: Estimation","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod02lec09.mp4","mimetype":"video/mp4","lesson_id":"9"},{"title":"Lecture 10 : Univariate Inferential Statistics: Estimation(Contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod02lec10.mp4","mimetype":"video/mp4","lesson_id":"10"},{"title":"Lecture 11 : Univariate Inferential Statistics: Hypothesis Testing","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod02lec11.mp4","mimetype":"video/mp4","lesson_id":"11"},{"title":"Lecture 12 : Hypothesis Testing(contd): Decision Making Scenarios","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod02lec12.mp4","mimetype":"video/mp4","lesson_id":"12"},{"title":"Lecture 13 : Multivariate Descriptive Statistics: Mean Vector","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod03lec13.mp4","mimetype":"video/mp4","lesson_id":"13"},{"title":"Lecture 14 : Multivariate Descriptive Statistics: Covariance Matrix","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod03lec14.mp4","mimetype":"video/mp4","lesson_id":"14"},{"title":"Lecture 15 : Multivariate Descriptive Statistics: Correlation Matrix","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod03lec15.mp4","mimetype":"video/mp4","lesson_id":"15"},{"title":"Lecture 16 : Multivariate Descriptive Statistics: Relationship between correlation","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod03lec16.mp4","mimetype":"video/mp4","lesson_id":"16"},{"title":"Lecture 17 : Multivariate Normal Distribution","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod04lec17.mp4","mimetype":"video/mp4","lesson_id":"17"},{"title":"Lecture 18 : Multivariate Normal Distribution(Contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod04lec18.mp4","mimetype":"video/mp4","lesson_id":"18"},{"title":"Lecture 19 : Multivariate Normal Distribution (Contd): Geometrical Interpretation","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod04lec19.mp4","mimetype":"video/mp4","lesson_id":"19"},{"title":"Lecture 20 : Multivariate Normal Distribution (Contd): Examining data for multivariate normal","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod04lec20.mp4","mimetype":"video/mp4","lesson_id":"20"},{"title":"Lecture 21 : Multivariate Inferential Statistics: Basics and Hotelling T - square statistic","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod05lec21.mp4","mimetype":"video/mp4","lesson_id":"21"},{"title":"Lecture 22 : Multivariate Inferential Statistics: Confidence Region","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod05lec22.mp4","mimetype":"video/mp4","lesson_id":"22"},{"title":"Lecture 23:Multivariate Inferential Statistics:Simultaneous confidence interval & Hypothesis testing","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod05lec23.mp4","mimetype":"video/mp4","lesson_id":"23"},{"title":"Lecture 24:Multivariate Inferential Statistics: Hypothesis testing for equality of two population","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod05lec24.mp4","mimetype":"video/mp4","lesson_id":"24"},{"title":"Lecture 25 : Analysis of Variance (ANOVA)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod06lec25.mp4","mimetype":"video/mp4","lesson_id":"25"},{"title":"Lecture 26 : Analysis of Variance (ANOVA): Decomposition of Total sum of squares","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod06lec26.mp4","mimetype":"video/mp4","lesson_id":"26"},{"title":"Lecture 27 : Analysis of Variance (ANOVA): Estimation of Parameters and Model Adequacy tests","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod06lec27.mp4","mimetype":"video/mp4","lesson_id":"27"},{"title":"Lecture 28 : Two-way and Three-way Analysis of Variance (ANOVA)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod06lec28.mp4","mimetype":"video/mp4","lesson_id":"28"},{"title":"Lecture 29 : Tutorial ANOVA","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod06lec29.mp4","mimetype":"video/mp4","lesson_id":"29"},{"title":"Lecture 30 : Tutorial ANOVA (Contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod06lec30.mp4","mimetype":"video/mp4","lesson_id":"30"},{"title":"Lecture 31: Multivariate Analysis of Variance (MANOVA): Conceptual Model","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod07lec31.mp4","mimetype":"video/mp4","lesson_id":"31"},{"title":"Lecture 32: Multivariate Analysis of Variance (MANOVA): (SSCP)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod07lec32.mp4","mimetype":"video/mp4","lesson_id":"32"},{"title":"Lecture 33: Multivariate Analysis of Variance (MANOVA):  (SSCP)(contd.)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod07lec33.mp4","mimetype":"video/mp4","lesson_id":"33"},{"title":"Lecture 34: Multivariate Analysis of Variance (MANOVA): Estimation and Hypothesis testing","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod07lec34.mp4","mimetype":"video/mp4","lesson_id":"34"},{"title":"Lecture 35: MANOVA Case Study","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod07lec35.mp4","mimetype":"video/mp4","lesson_id":"35"},{"title":"Lecture 36 : Multiple Linear Regression: Introduction","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod08lec36.mp4","mimetype":"video/mp4","lesson_id":"36"},{"title":"Lecture 37 : Multiple Linear Regression: Assumptions and Estimation of model parameters","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod08lec37.mp4","mimetype":"video/mp4","lesson_id":"37"},{"title":"Lecture 38 : Multiple Linear Regression: Sampling Distribution of parameter estimates","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod08lec38.mp4","mimetype":"video/mp4","lesson_id":"38"},{"title":"Lecture 39 : Multiple Linear Regression: Sampling Distribution of parameter estimates (contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod08lec39.mp4","mimetype":"video/mp4","lesson_id":"39"},{"title":"Lecture 40 : Multiple Linear Regression: Model Adequacy Tests","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod08lec40.mp4","mimetype":"video/mp4","lesson_id":"40"},{"title":"Lecture 41 : Multiple Linear Regression: Model Adequacy Tests(contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod08lec41.mp4","mimetype":"video/mp4","lesson_id":"41"},{"title":"Lecture 42 : Multiple Linear Regression: Test of Assumptions","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod08lec42.mp4","mimetype":"video/mp4","lesson_id":"42"},{"title":"Lecture 43 : MLR-Model diagnostics","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod08lec43.mp4","mimetype":"video/mp4","lesson_id":"43"},{"title":"Lecture 44 : MLR-case study","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod08lec44.mp4","mimetype":"video/mp4","lesson_id":"44"},{"title":"Lecture 45: Multivariate Linear Regression: conceptual model and assumptions","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod09lec45.mp4","mimetype":"video/mp4","lesson_id":"45"},{"title":"Lecture 46: Multivariate Linear Regression: Estimation of parameters","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod09lec46.mp4","mimetype":"video/mp4","lesson_id":"46"},{"title":"Lecture 47: Multivariate Linear Regression: Estimation of parameters (contd.)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod09lec47.mp4","mimetype":"video/mp4","lesson_id":"47"},{"title":"Lecture 48: Multiple Linear Regression: Sampling Distribution of parameter estimates","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod09lec48.mp4","mimetype":"video/mp4","lesson_id":"48"},{"title":"Lecture 49: Multivariate Linear Regression: Model Adequacy Tests","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod09lec49.mp4","mimetype":"video/mp4","lesson_id":"49"},{"title":"Lecture 50: Multiple Linear Regression: Model Adequacy Tests(contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod09lec50.mp4","mimetype":"video/mp4","lesson_id":"50"},{"title":"Lecture 51: Regression modeling using SPSS","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod09lec51.mp4","mimetype":"video/mp4","lesson_id":"51"},{"title":"Lecture 52 : Principal Component Analysis (PCA): Conceptual Model","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod10lec52.mp4","mimetype":"video/mp4","lesson_id":"52"},{"title":"Lecture 53 : Principal Component Analysis (PCA): Extraction of Principal components (PCs)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod10lec53.mp4","mimetype":"video/mp4","lesson_id":"53"},{"title":"Lecture 54 : Principal Component Analysis (PCA): Model Adequacy and Interpretation","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod10lec54.mp4","mimetype":"video/mp4","lesson_id":"54"},{"title":"Lecture 55 : Principal Component Analysis (PCA): Model Adequacy and Interpretation (contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod10lec55.mp4","mimetype":"video/mp4","lesson_id":"55"},{"title":"Lecture 56: Factor Analysis: Basics and Orthogonal factor models","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod11lec56.mp4","mimetype":"video/mp4","lesson_id":"56"},{"title":"Lecture 57: Factor Analysis: Types of models and key questions","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod11lec57.mp4","mimetype":"video/mp4","lesson_id":"57"},{"title":"Lecture 58: Factor Analysis: Parameter Estimation","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod11lec58.mp4","mimetype":"video/mp4","lesson_id":"58"},{"title":"Lecture 59: Factor Analysis: Parameter Estimation(contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod11lec59.mp4","mimetype":"video/mp4","lesson_id":"59"},{"title":"Lecture 60: Factor Analysis: Model Adequacy tests and factor rotation","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod11lec60.mp4","mimetype":"video/mp4","lesson_id":"60"},{"title":"Lecture 61: Factor Analysis: Factor scores and case study","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod11lec61.mp4","mimetype":"video/mp4","lesson_id":"61"},{"title":"Lecture 62 : Introduction to Structural Equation Modeling (SEM)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod12lec62.mp4","mimetype":"video/mp4","lesson_id":"62"},{"title":"Lecture 63 : Introduction to Structural Equation Modeling (SEM)(contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod12lec63.mp4","mimetype":"video/mp4","lesson_id":"63"},{"title":"Lecture 64 : Structural Equation Modeling (SEM): Confirmatory factor analysis (Measurement Model )","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod12lec64.mp4","mimetype":"video/mp4","lesson_id":"64"},{"title":"Lecture 65 : Structural Equation Modeling (SEM): Confirmatory factor analysis (contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod12lec65.mp4","mimetype":"video/mp4","lesson_id":"65"},{"title":"Lecture 66 : Structural Equation Modeling (SEM): Structural Model (Path model)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod12lec66.mp4","mimetype":"video/mp4","lesson_id":"66"},{"title":"Lecture 67 : Structural Equation Modeling (SEM): Structural Model(contd)","url":"https://media.dev.nptel.ac.in/content/mp4/111/105/111105091/MP4/mod12lec67.mp4","mimetype":"video/mp4","lesson_id":"67"}],"transcripts":[{"title":"Lecture 01 : Introduction to Multivariate Statistical Modeling","lesson_id":"1","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 02 : Introduction to Multivariate Statistical Modeling: Data types, models, and modeling","lesson_id":"2","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 03 : Statistical approaches to model building","lesson_id":"3","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 04 : Statistical approaches to model building (Contd)","lesson_id":"4","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 05 : Univariate Descriptive Statistics","lesson_id":"5","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 06 : Univariate Descriptive Statistics (Contd)","lesson_id":"6","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 07 : Normal Distribution and Chi-squared Distribution","lesson_id":"7","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 08 : t-distribution, F-distribution, and Central Limit Theorem","lesson_id":"8","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 09 : Univariate inferential statistics: Estimation","lesson_id":"9","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 10 : Univariate Inferential Statistics: Estimation(Contd)","lesson_id":"10","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 11 : Univariate Inferential Statistics: Hypothesis Testing","lesson_id":"11","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 12 : Hypothesis Testing(contd): Decision Making Scenarios","lesson_id":"12","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 13 : Multivariate Descriptive Statistics: Mean Vector","lesson_id":"13","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 14 : Multivariate Descriptive Statistics: Covariance Matrix","lesson_id":"14","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 15 : Multivariate Descriptive Statistics: Correlation Matrix","lesson_id":"15","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 16 : Multivariate Descriptive Statistics: Relationship between correlation","lesson_id":"16","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 17 : Multivariate Normal Distribution","lesson_id":"17","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 18 : Multivariate Normal Distribution(Contd)","lesson_id":"18","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 19 : Multivariate Normal Distribution (Contd): Geometrical Interpretation","lesson_id":"19","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 20 : Multivariate Normal Distribution (Contd): Examining data for multivariate normal","lesson_id":"20","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 21 : Multivariate Inferential Statistics: Basics and Hotelling T - square statistic","lesson_id":"21","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 22 : Multivariate Inferential Statistics: Confidence Region","lesson_id":"22","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 23:Multivariate Inferential Statistics:Simultaneous confidence interval & Hypothesis testing","lesson_id":"23","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 24:Multivariate Inferential Statistics: Hypothesis testing for equality of two population","lesson_id":"24","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 25 : Analysis of Variance (ANOVA)","lesson_id":"25","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 26 : Analysis of Variance (ANOVA): Decomposition of Total sum of squares","lesson_id":"26","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 27 : Analysis of Variance (ANOVA): Estimation of Parameters and Model Adequacy tests","lesson_id":"27","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 28 : Two-way and Three-way Analysis of Variance (ANOVA)","lesson_id":"28","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 29 : Tutorial ANOVA","lesson_id":"29","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 30 : Tutorial ANOVA (Contd)","lesson_id":"30","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 31: Multivariate Analysis of Variance (MANOVA): Conceptual Model","lesson_id":"31","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 32: Multivariate Analysis of Variance (MANOVA): (SSCP)","lesson_id":"32","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 33: Multivariate Analysis of Variance (MANOVA):  (SSCP)(contd.)","lesson_id":"33","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 34: Multivariate Analysis of Variance (MANOVA): Estimation and Hypothesis testing","lesson_id":"34","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 35: MANOVA Case Study","lesson_id":"35","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 36 : Multiple Linear Regression: Introduction","lesson_id":"36","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 37 : Multiple Linear Regression: Assumptions and Estimation of model parameters","lesson_id":"37","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 38 : Multiple Linear Regression: Sampling Distribution of parameter estimates","lesson_id":"38","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 39 : Multiple Linear Regression: Sampling Distribution of parameter estimates (contd)","lesson_id":"39","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 40 : Multiple Linear Regression: Model Adequacy Tests","lesson_id":"40","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 41 : Multiple Linear Regression: Model Adequacy Tests(contd)","lesson_id":"41","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 42 : Multiple Linear Regression: Test of Assumptions","lesson_id":"42","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 43 : MLR-Model diagnostics","lesson_id":"43","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 44 : MLR-case study","lesson_id":"44","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 45: Multivariate Linear Regression: conceptual model and assumptions","lesson_id":"45","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 46: Multivariate Linear Regression: Estimation of parameters","lesson_id":"46","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 47: Multivariate Linear Regression: Estimation of parameters (contd.)","lesson_id":"47","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 48: Multiple Linear Regression: Sampling Distribution of parameter estimates","lesson_id":"48","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 49: Multivariate Linear Regression: Model Adequacy Tests","lesson_id":"49","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 50: Multiple Linear Regression: Model Adequacy Tests(contd)","lesson_id":"50","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 51: Regression modeling using SPSS","lesson_id":"51","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 52 : Principal Component Analysis (PCA): Conceptual Model","lesson_id":"52","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 53 : Principal Component Analysis (PCA): Extraction of Principal components (PCs)","lesson_id":"53","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 54 : Principal Component Analysis (PCA): Model Adequacy and Interpretation","lesson_id":"54","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 55 : Principal Component Analysis (PCA): Model Adequacy and Interpretation (contd)","lesson_id":"55","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 56: Factor Analysis: Basics and Orthogonal factor models","lesson_id":"56","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 57: Factor Analysis: Types of models and key questions","lesson_id":"57","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 58: Factor Analysis: Parameter Estimation","lesson_id":"58","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 59: Factor Analysis: Parameter Estimation(contd)","lesson_id":"59","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 60: Factor Analysis: Model Adequacy tests and factor rotation","lesson_id":"60","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 61: Factor Analysis: Factor scores and case study","lesson_id":"61","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 62 : Introduction to Structural Equation Modeling (SEM)","lesson_id":"62","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 63 : Introduction to Structural Equation Modeling (SEM)(contd)","lesson_id":"63","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 64 : Structural Equation Modeling (SEM): Confirmatory factor analysis (Measurement Model )","lesson_id":"64","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 65 : Structural Equation Modeling (SEM): Confirmatory factor analysis (contd)","lesson_id":"65","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 66 : Structural Equation Modeling (SEM): Structural Model (Path model)","lesson_id":"66","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 67 : Structural Equation Modeling (SEM): Structural Model(contd)","lesson_id":"67","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]}],"audio":[{"title":"Lecture 01 : Introduction to Multivariate Statistical Modeling","lesson_id":"1","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 02 : Introduction to Multivariate Statistical Modeling: Data types, models, and modeling","lesson_id":"2","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 03 : Statistical approaches to model building","lesson_id":"3","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 04 : Statistical approaches to model building (Contd)","lesson_id":"4","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 05 : Univariate Descriptive Statistics","lesson_id":"5","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 06 : Univariate Descriptive Statistics (Contd)","lesson_id":"6","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 07 : Normal Distribution and Chi-squared Distribution","lesson_id":"7","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 08 : t-distribution, F-distribution, and Central Limit Theorem","lesson_id":"8","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 09 : Univariate inferential statistics: Estimation","lesson_id":"9","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 10 : Univariate Inferential Statistics: Estimation(Contd)","lesson_id":"10","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 11 : Univariate Inferential Statistics: Hypothesis Testing","lesson_id":"11","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 12 : Hypothesis Testing(contd): Decision Making Scenarios","lesson_id":"12","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 13 : Multivariate Descriptive Statistics: Mean Vector","lesson_id":"13","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 14 : Multivariate Descriptive Statistics: Covariance Matrix","lesson_id":"14","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 15 : Multivariate Descriptive Statistics: Correlation Matrix","lesson_id":"15","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 16 : Multivariate Descriptive Statistics: Relationship between correlation","lesson_id":"16","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 17 : Multivariate Normal Distribution","lesson_id":"17","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 18 : Multivariate Normal Distribution(Contd)","lesson_id":"18","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 19 : Multivariate Normal Distribution (Contd): Geometrical Interpretation","lesson_id":"19","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 20 : Multivariate Normal Distribution (Contd): Examining data for multivariate normal","lesson_id":"20","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 21 : Multivariate Inferential Statistics: Basics and Hotelling T - square statistic","lesson_id":"21","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 22 : Multivariate Inferential Statistics: Confidence Region","lesson_id":"22","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 23:Multivariate Inferential Statistics:Simultaneous confidence interval & Hypothesis testing","lesson_id":"23","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 24:Multivariate Inferential Statistics: Hypothesis testing for equality of two population","lesson_id":"24","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 25 : Analysis of Variance (ANOVA)","lesson_id":"25","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 26 : Analysis of Variance (ANOVA): Decomposition of Total sum of squares","lesson_id":"26","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 27 : Analysis of Variance (ANOVA): Estimation of Parameters and Model Adequacy tests","lesson_id":"27","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 28 : Two-way and Three-way Analysis of Variance (ANOVA)","lesson_id":"28","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 29 : Tutorial ANOVA","lesson_id":"29","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 30 : Tutorial ANOVA (Contd)","lesson_id":"30","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 31: Multivariate Analysis of Variance (MANOVA): Conceptual Model","lesson_id":"31","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 32: Multivariate Analysis of Variance (MANOVA): (SSCP)","lesson_id":"32","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 33: Multivariate Analysis of Variance (MANOVA):  (SSCP)(contd.)","lesson_id":"33","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 34: Multivariate Analysis of Variance (MANOVA): Estimation and Hypothesis testing","lesson_id":"34","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 35: MANOVA Case Study","lesson_id":"35","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 36 : Multiple Linear Regression: Introduction","lesson_id":"36","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 37 : Multiple Linear Regression: Assumptions and Estimation of model parameters","lesson_id":"37","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 38 : Multiple Linear Regression: Sampling Distribution of parameter estimates","lesson_id":"38","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 39 : Multiple Linear Regression: Sampling Distribution of parameter estimates (contd)","lesson_id":"39","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 40 : Multiple Linear Regression: Model Adequacy Tests","lesson_id":"40","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 41 : Multiple Linear Regression: Model Adequacy Tests(contd)","lesson_id":"41","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 42 : Multiple Linear Regression: Test of Assumptions","lesson_id":"42","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 43 : MLR-Model diagnostics","lesson_id":"43","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 44 : MLR-case study","lesson_id":"44","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 45: Multivariate Linear Regression: conceptual model and assumptions","lesson_id":"45","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 46: Multivariate Linear Regression: Estimation of parameters","lesson_id":"46","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 47: Multivariate Linear Regression: Estimation of parameters (contd.)","lesson_id":"47","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 48: Multiple Linear Regression: Sampling Distribution of parameter estimates","lesson_id":"48","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 49: Multivariate Linear Regression: Model Adequacy Tests","lesson_id":"49","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 50: Multiple Linear Regression: Model Adequacy Tests(contd)","lesson_id":"50","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 51: Regression modeling using SPSS","lesson_id":"51","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 52 : Principal Component Analysis (PCA): Conceptual Model","lesson_id":"52","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 53 : Principal Component Analysis (PCA): Extraction of Principal components (PCs)","lesson_id":"53","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 54 : Principal Component Analysis (PCA): Model Adequacy and Interpretation","lesson_id":"54","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 55 : Principal Component Analysis (PCA): Model Adequacy and Interpretation (contd)","lesson_id":"55","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 56: Factor Analysis: Basics and Orthogonal factor models","lesson_id":"56","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 57: Factor Analysis: Types of models and key questions","lesson_id":"57","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 58: Factor Analysis: Parameter Estimation","lesson_id":"58","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 59: Factor Analysis: Parameter Estimation(contd)","lesson_id":"59","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 60: Factor Analysis: Model Adequacy tests and factor rotation","lesson_id":"60","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 61: Factor Analysis: Factor scores and case study","lesson_id":"61","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 62 : Introduction to Structural Equation Modeling (SEM)","lesson_id":"62","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 63 : Introduction to Structural Equation Modeling (SEM)(contd)","lesson_id":"63","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 64 : Structural Equation Modeling (SEM): Confirmatory factor analysis (Measurement Model )","lesson_id":"64","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 65 : Structural Equation Modeling (SEM): Confirmatory factor analysis (contd)","lesson_id":"65","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 66 : Structural Equation Modeling (SEM): Structural Model (Path model)","lesson_id":"66","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]},{"title":"Lecture 67 : Structural Equation Modeling (SEM): Structural Model(contd)","lesson_id":"67","downloads":[{"language":"english","url":null},{"language":"bengali","url":null},{"language":"gujarati","url":null},{"language":"hindi","url":null},{"language":"kannada","url":null},{"language":"malayalam","url":null},{"language":"marathi","url":null},{"language":"tamil","url":null},{"language":"telugu","url":null},{"language":"punjabi","url":null},{"language":"odiya","url":null},{"language":"assamese","url":null}]}],"books":[{"title":"english","url":null,"mimetype":"application/pdf"},{"title":"bengali","url":null,"mimetype":"application/pdf"},{"title":"gujarati","url":null,"mimetype":"application/pdf"},{"title":"hindi","url":null,"mimetype":"application/pdf"},{"title":"kannada","url":null,"mimetype":"application/pdf"},{"title":"malayalam","url":null,"mimetype":"application/pdf"},{"title":"marathi","url":null,"mimetype":"application/pdf"},{"title":"tamil","url":null,"mimetype":"application/pdf"},{"title":"telugu","url":null,"mimetype":"application/pdf"},{"title":"punjabi","url":null,"mimetype":"application/pdf"},{"title":"odiya","url":null,"mimetype":"application/pdf"},{"title":"assamese","url":null,"mimetype":"application/pdf"}],"assignments":[]}}