Modules / Lectures

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