Lecture 1: Descriptive Statistics-I

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Lecture 2: Descriptive Statistics-II

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Lecture 3: Probability and Distribution

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Lecture 4: Random variable and Expectation I

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Lecture 5: Random variable and Expectation II

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Lecture 6: Random variable and Expectation III

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Lecture 7: Random variable and Expectation IV

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Lecture 8: Module: Introduction to R

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Lecture 9: R : Demos and getting help

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Lecture 10: R as calculator and plotter: Diffusivity, scaled temperatures

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Lecture 11: R as calculator and plotter: Diffraction, configurational entropy

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Lecture 12: Data in tabular form: Properties of elements

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Lecture 13: Tabular data in R: alternate methodology

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Lecture 14: Dataframe in R: Properties of elements

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Lecture 15: R libraries for plotting

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Lecture 16: Importing and plotting data

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Lecture 17: Property charts: Importing and plotting data

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Lecture 18: Introduction to R: Summary of the module

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Lecture 19: Descriptive statistics

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Lecture 20: Presenting experimental results: Data on conductivity of ETP copper

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Lecture 21: Property based reports, errors, significant digits

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Lecture 22: Dealing with distributions: Grain size data

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Lecture 23: Grain size data: Property and rank based reports

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Lecture 24: Case study: Grain size in a two phase steel

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Lecture 25: Grain size in a two phase steel: Descriptive statistics

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Lecture 26: Presenting experimental results: data with error bars

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Lecture 27: Errors and their propagation

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Lecture 28: Fitting experimental data to distributions

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Lecture 29: Combining uncertainties

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Lecture 30: Summary:Descriptive statistics

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Lecture 31: Special Random Variables I

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Lecture 32: Special Random Variables II

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Lecture 33: Special Random Variables III

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Lecture 34: Special Random Variables IV

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Lecture 35: Special Random Variables V

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Lecture 36: Probabilty Plots

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Lecture 37: Probability distributions

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Lecture 38: Properties of probability distributions

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Lecture 39: Bernoulli trials and binomial distributions

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Lecture 40: Atom probe technique and negative binomial distribution

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Lecture 41: Atom probe and hypergeometric distribution

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Lecture 42: Atom probe : analysis of error

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Lecture 43: Nucleation and Poisson distribution

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Lecture 44: Normal distribution

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Lecture 45: Normal distribution and error function

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Lecture 46: Probability scale

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Lecture 47: Sampling Distribution I

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Lecture 48: Sampling Distribution II

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Lecture 49: Sampling Distribution III

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Lecture 50: Parameter Estimation I

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Lecture 51: Parameter Estimator II

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Lecture 52: Parameter Estimator III

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Lecture 53: Parameter Estimator IV

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Lecture 54: Bayesian Estimation

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Lecture 55: Log normal distribution

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Lecture 56: Lorentz/Cauchy distribution

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Lecture 57: Lifetime and exponential distributions

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Lecture 58: Distributions from statistical mechanics

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Lecture 59: Uniform distribution and summary of probability distributions

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Lecture 60: Data processing: Introduction

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Lecture 61: Distribution function of a data series

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Lecture 62: Estimating mean and mean-square-deviation of data

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Lecture 63: Data with unequal weights

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Lecture 64: Robust estimates

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Lecture 65: From data to underlying distribution

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Lecture 66: Bootstrap method

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Lecture 67: Summary:Data processing

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Lecture 68: Hypothesis Testing I

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Lecture 69: Hypothesis Testing II

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Lecture 70: Hypothesis Testing III

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Lecture 71: Hypothesis Testing IV

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Lecture 72: Hypothesis Testing V

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Lecture 73: Hypothesis Testing VI

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Lecture 74: Graphical handling of data

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Lecture 75: Fitting and graphical handling of data: Introduction

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Lecture 76: Data transformable to linear

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Lecture 77: Data of known functional form

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Lecture 78: Calibration,Fitting,Hypotheses testing

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Lecture 79: Analysis of variance

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Lecture 80: Summary:Fittng and graphical handling of data

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Lecture 81: Regression Analysis - I

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Lecture 82: Regression Analysis - II

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Lecture 83: Regression Analysis - III

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Lecture 84: Regression Analysis - IV

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Lecture 85: Analysis of Variance-I

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Lecture 86: Analysis of Variance-II

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Lecture 87: Design of Experiment I

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Lecture 88: Design of Experiment II

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Lecture 89: Design of Experiment III

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Lecture 90: Design of Experiment IV

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Lecture 91: Summary of the course

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Lecture 92: Case studies: Introduction

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Lecture 93: Case study 1: Data smoothing I

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Lecture 94: Case study 1: Data smoothing II

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Lecture 95: Case study 2: Error analysis

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Lecture 96: Case study 3: Calibration

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Lecture 97: Case study 4: Design of experiment

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Lecture 98: Case study 5: Hypothesis testing

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Lecture 99: Course summary

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