Modules / Lectures
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noc20_mm11_assignment_Week_1noc20_mm11_assignment_Week_1
noc20_mm11_assignment_Week_10noc20_mm11_assignment_Week_10
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noc20_mm11_assignment_Week_9noc20_mm11_assignment_Week_9


Sl.No Chapter Name MP4 Download
1Lecture 1: Descriptive Statistics-IDownload
2Lecture 2: Descriptive Statistics-IIDownload
3Lecture 3: Probability and DistributionDownload
4Lecture 4: Random variable and Expectation IDownload
5Lecture 5: Random variable and Expectation IIDownload
6Lecture 6: Random variable and Expectation IIIDownload
7Lecture 7: Random variable and Expectation IVDownload
8Lecture 8: Module: Introduction to RDownload
9Lecture 9: R : Demos and getting helpDownload
10Lecture 10: R as calculator and plotter: Diffusivity, scaled temperaturesDownload
11Lecture 11: R as calculator and plotter: Diffraction, configurational entropy Download
12Lecture 12: Data in tabular form: Properties of elementsDownload
13Lecture 13: Tabular data in R: alternate methodologyDownload
14Lecture 14: Dataframe in R: Properties of elementsDownload
15Lecture 15: R libraries for plottingDownload
16Lecture 16: Importing and plotting dataDownload
17Lecture 17: Property charts: Importing and plotting dataDownload
18Lecture 18: Introduction to R: Summary of the moduleDownload
19Lecture 19: Descriptive statisticsDownload
20Lecture 20: Presenting experimental results: Data on conductivity of ETP copperDownload
21Lecture 21: Property based reports, errors, significant digitsDownload
22Lecture 22: Dealing with distributions: Grain size dataDownload
23Lecture 23: Grain size data: Property and rank based reportsDownload
24Lecture 24: Case study: Grain size in a two phase steelDownload
25Lecture 25: Grain size in a two phase steel: Descriptive statisticsDownload
26Lecture 26: Presenting experimental results: data with error barsDownload
27Lecture 27: Errors and their propagationDownload
28Lecture 28: Fitting experimental data to distributionsDownload
29Lecture 29: Combining uncertaintiesDownload
30Lecture 30: Summary:Descriptive statisticsDownload
31Lecture 31: Special Random Variables IDownload
32Lecture 32: Special Random Variables IIDownload
33Lecture 33: Special Random Variables IIIDownload
34Lecture 34: Special Random Variables IVDownload
35Lecture 35: Special Random Variables VDownload
36Lecture 36: Probabilty PlotsDownload
37Lecture 37: Probability distributionsDownload
38Lecture 38: Properties of probability distributionsDownload
39Lecture 39: Bernoulli trials and binomial distributionsDownload
40Lecture 40: Atom probe technique and negative binomial distribution Download
41Lecture 41: Atom probe and hypergeometric distributionDownload
42Lecture 42: Atom probe : analysis of errorDownload
43Lecture 43: Nucleation and Poisson distributionDownload
44Lecture 44: Normal distributionDownload
45Lecture 45: Normal distribution and error functionDownload
46Lecture 46: Probability scaleDownload
47Lecture 47: Sampling Distribution IDownload
48Lecture 48: Sampling Distribution IIDownload
49Lecture 49: Sampling Distribution IIIDownload
50Lecture 50: Parameter Estimation IDownload
51Lecture 51: Parameter Estimator IIDownload
52Lecture 52: Parameter Estimator IIIDownload
53Lecture 53: Parameter Estimator IVDownload
54Lecture 54: Bayesian EstimationDownload
55Lecture 55: Log normal distributionDownload
56Lecture 56: Lorentz/Cauchy distributionDownload
57Lecture 57: Lifetime and exponential distributionsDownload
58Lecture 58: Distributions from statistical mechanicsDownload
59Lecture 59: Uniform distribution and summary of probability distributionsDownload
60Lecture 60: Data processing: IntroductionDownload
61Lecture 61: Distribution function of a data seriesDownload
62Lecture 62: Estimating mean and mean-square-deviation of dataDownload
63Lecture 63: Data with unequal weightsDownload
64Lecture 64: Robust estimatesDownload
65Lecture 65: From data to underlying distributionDownload
66Lecture 66: Bootstrap methodDownload
67Lecture 67: Summary:Data processingDownload
68Lecture 68: Hypothesis Testing IDownload
69Lecture 69: Hypothesis Testing IIDownload
70Lecture 70: Hypothesis Testing IIIDownload
71Lecture 71: Hypothesis Testing IVDownload
72Lecture 72: Hypothesis Testing VDownload
73Lecture 73: Hypothesis Testing VIDownload
74Lecture 74: Graphical handling of dataDownload
75Lecture 75: Fitting and graphical handling of data: IntroductionDownload
76Lecture 76: Data transformable to linear Download
77Lecture 77: Data of known functional formDownload
78Lecture 78: Calibration,Fitting,Hypotheses testingDownload
79Lecture 79: Analysis of varianceDownload
80Lecture 80: Summary:Fittng and graphical handling of dataDownload
81Lecture 81: Regression Analysis - IDownload
82Lecture 82: Regression Analysis - IIDownload
83Lecture 83: Regression Analysis - IIIDownload
84Lecture 84: Regression Analysis - IVDownload
85Lecture 85: Analysis of Variance-IDownload
86Lecture 86: Analysis of Variance-IIDownload
87Lecture 87: Design of Experiment IDownload
88Lecture 88: Design of Experiment IIDownload
89Lecture 89: Design of Experiment IIIDownload
90Lecture 90: Design of Experiment IVDownload
91Lecture 91: Summary of the courseDownload
92Lecture 92: Case studies: IntroductionDownload
93Lecture 93: Case study 1: Data smoothing IDownload
94Lecture 94: Case study 1: Data smoothing IIDownload
95Lecture 95: Case study 2: Error analysisDownload
96Lecture 96: Case study 3: CalibrationDownload
97Lecture 97: Case study 4: Design of experimentDownload
98Lecture 98: Case study 5: Hypothesis testingDownload
99Lecture 99: Course summary Download

Sl.No Chapter Name English
1Lecture 1: Descriptive Statistics-IDownload
Verified
2Lecture 2: Descriptive Statistics-IIDownload
Verified
3Lecture 3: Probability and DistributionDownload
Verified
4Lecture 4: Random variable and Expectation IDownload
Verified
5Lecture 5: Random variable and Expectation IIDownload
Verified
6Lecture 6: Random variable and Expectation IIIDownload
Verified
7Lecture 7: Random variable and Expectation IVDownload
Verified
8Lecture 8: Module: Introduction to RDownload
Verified
9Lecture 9: R : Demos and getting helpDownload
Verified
10Lecture 10: R as calculator and plotter: Diffusivity, scaled temperaturesDownload
Verified
11Lecture 11: R as calculator and plotter: Diffraction, configurational entropy Download
Verified
12Lecture 12: Data in tabular form: Properties of elementsDownload
Verified
13Lecture 13: Tabular data in R: alternate methodologyDownload
Verified
14Lecture 14: Dataframe in R: Properties of elementsDownload
Verified
15Lecture 15: R libraries for plottingDownload
Verified
16Lecture 16: Importing and plotting dataDownload
Verified
17Lecture 17: Property charts: Importing and plotting dataDownload
Verified
18Lecture 18: Introduction to R: Summary of the moduleDownload
Verified
19Lecture 19: Descriptive statisticsDownload
Verified
20Lecture 20: Presenting experimental results: Data on conductivity of ETP copperDownload
Verified
21Lecture 21: Property based reports, errors, significant digitsDownload
Verified
22Lecture 22: Dealing with distributions: Grain size dataDownload
Verified
23Lecture 23: Grain size data: Property and rank based reportsDownload
Verified
24Lecture 24: Case study: Grain size in a two phase steelDownload
Verified
25Lecture 25: Grain size in a two phase steel: Descriptive statisticsDownload
Verified
26Lecture 26: Presenting experimental results: data with error barsDownload
Verified
27Lecture 27: Errors and their propagationDownload
Verified
28Lecture 28: Fitting experimental data to distributionsDownload
Verified
29Lecture 29: Combining uncertaintiesDownload
Verified
30Lecture 30: Summary:Descriptive statisticsDownload
Verified
31Lecture 31: Special Random Variables IDownload
Verified
32Lecture 32: Special Random Variables IIDownload
Verified
33Lecture 33: Special Random Variables IIIDownload
Verified
34Lecture 34: Special Random Variables IVDownload
Verified
35Lecture 35: Special Random Variables VDownload
Verified
36Lecture 36: Probabilty PlotsDownload
Verified
37Lecture 37: Probability distributionsDownload
Verified
38Lecture 38: Properties of probability distributionsDownload
Verified
39Lecture 39: Bernoulli trials and binomial distributionsDownload
Verified
40Lecture 40: Atom probe technique and negative binomial distribution Download
Verified
41Lecture 41: Atom probe and hypergeometric distributionDownload
Verified
42Lecture 42: Atom probe : analysis of errorDownload
Verified
43Lecture 43: Nucleation and Poisson distributionDownload
Verified
44Lecture 44: Normal distributionDownload
Verified
45Lecture 45: Normal distribution and error functionDownload
Verified
46Lecture 46: Probability scaleDownload
Verified
47Lecture 47: Sampling Distribution IDownload
Verified
48Lecture 48: Sampling Distribution IIDownload
Verified
49Lecture 49: Sampling Distribution IIIDownload
Verified
50Lecture 50: Parameter Estimation IDownload
Verified
51Lecture 51: Parameter Estimator IIDownload
Verified
52Lecture 52: Parameter Estimator IIIDownload
Verified
53Lecture 53: Parameter Estimator IVDownload
Verified
54Lecture 54: Bayesian EstimationDownload
Verified
55Lecture 55: Log normal distributionDownload
Verified
56Lecture 56: Lorentz/Cauchy distributionDownload
Verified
57Lecture 57: Lifetime and exponential distributionsDownload
Verified
58Lecture 58: Distributions from statistical mechanicsDownload
Verified
59Lecture 59: Uniform distribution and summary of probability distributionsDownload
Verified
60Lecture 60: Data processing: IntroductionDownload
Verified
61Lecture 61: Distribution function of a data seriesDownload
Verified
62Lecture 62: Estimating mean and mean-square-deviation of dataDownload
Verified
63Lecture 63: Data with unequal weightsDownload
Verified
64Lecture 64: Robust estimatesDownload
Verified
65Lecture 65: From data to underlying distributionDownload
Verified
66Lecture 66: Bootstrap methodDownload
Verified
67Lecture 67: Summary:Data processingDownload
Verified
68Lecture 68: Hypothesis Testing IDownload
Verified
69Lecture 69: Hypothesis Testing IIDownload
Verified
70Lecture 70: Hypothesis Testing IIIDownload
Verified
71Lecture 71: Hypothesis Testing IVDownload
Verified
72Lecture 72: Hypothesis Testing VDownload
Verified
73Lecture 73: Hypothesis Testing VIDownload
Verified
74Lecture 74: Graphical handling of dataDownload
Verified
75Lecture 75: Fitting and graphical handling of data: IntroductionDownload
Verified
76Lecture 76: Data transformable to linear Download
Verified
77Lecture 77: Data of known functional formDownload
Verified
78Lecture 78: Calibration,Fitting,Hypotheses testingDownload
Verified
79Lecture 79: Analysis of varianceDownload
Verified
80Lecture 80: Summary:Fittng and graphical handling of dataDownload
Verified
81Lecture 81: Regression Analysis - IDownload
Verified
82Lecture 82: Regression Analysis - IIDownload
Verified
83Lecture 83: Regression Analysis - IIIDownload
Verified
84Lecture 84: Regression Analysis - IVDownload
Verified
85Lecture 85: Analysis of Variance-IDownload
Verified
86Lecture 86: Analysis of Variance-IIDownload
Verified
87Lecture 87: Design of Experiment IDownload
Verified
88Lecture 88: Design of Experiment IIDownload
Verified
89Lecture 89: Design of Experiment IIIDownload
Verified
90Lecture 90: Design of Experiment IVDownload
Verified
91Lecture 91: Summary of the courseDownload
Verified
92Lecture 92: Case studies: IntroductionDownload
Verified
93Lecture 93: Case study 1: Data smoothing IDownload
Verified
94Lecture 94: Case study 1: Data smoothing IIDownload
Verified
95Lecture 95: Case study 2: Error analysisDownload
Verified
96Lecture 96: Case study 3: CalibrationDownload
Verified
97Lecture 97: Case study 4: Design of experimentDownload
Verified
98Lecture 98: Case study 5: Hypothesis testingDownload
Verified
99Lecture 99: Course summary Download
Verified


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