Learn · Dealing with materials data : collection, analysis and interpretation
Lecture 1 of 100Week 1
Lecture 1: Descriptive Statistics-I
Prof. M P Gururajan Prof. Hina Gokhale · IIT Bombay
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- Dealing with materials data : collection, analysis and interpretation, IIT Bombay (at NPTEL)
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All the lectures
- Lecture 1: Descriptive Statistics-I
- Lecture 2: Descriptive Statistics-II
- Lecture 3: Probability and Distribution
- Lecture 4: Random variable and Expectation I
- Lecture 5: Random variable and Expectation II
- Lecture 6: Random variable and Expectation III
- Lecture 7: Random variable and Expectation IV
- Lecture 8: Module: Introduction to R
- Lecture 9: R : Demos and getting help
- Lecture 10: R as calculator and plotter: Diffusivity, scaled temperatures
- Lecture 11: R as calculator and plotter: Diffraction, configurational entropy
- Lecture 12: Data in tabular form: Properties of elements
- Lecture 13: Tabular data in R: alternate methodology
- Lecture 14: Dataframe in R: Properties of elements
- Lecture 15: R libraries for plotting
- Lecture 16: Importing and plotting data
- Lecture 17: Property charts: Importing and plotting data
- Lecture 18: Introduction to R: Summary of the module
- Lecture 19: Descriptive statistics
- Lecture 20: Presenting experimental results: Data on conductivity of ETP copper
- Lecture 21: Property based reports, errors, significant digits
- Lecture 22: Dealing with distributions: Grain size data
- Lecture 23: Grain size data: Property and rank based reports
- Lecture 24: Case study: Grain size in a two phase steel
- Lecture 25: Grain size in a two phase steel: Descriptive statistics
- Lecture 26: Presenting experimental results: data with error bars
- Lecture 27: Errors and their propagation
- Lecture 28: Fitting experimental data to distributions
- Lecture 29: Combining uncertainties
- Lecture 30: Summary:Descriptive statistics
- Lecture 31: Special Random Variables I
- Lecture 32: Special Random Variables II
- Lecture 33: Special Random Variables III
- Lecture 34: Special Random Variables IV
- Lecture 35: Special Random Variables V
- Lecture 36: Probabilty Plots
- Lecture 37: Probability distributions
- Lecture 38: Properties of probability distributions
- Lecture 39: Bernoulli trials and binomial distributions
- Lecture 40: Atom probe technique and negative binomial distribution
- Lecture 41: Atom probe and hypergeometric distribution
- Lecture 42: Atom probe : analysis of error
- Lecture 43: Nucleation and Poisson distribution
- Lecture 44: Normal distribution
- Lecture 45: Normal distribution and error function
- Lecture 46: Probability scale
- Lecture 47: Sampling Distribution I
- Lecture 48: Sampling Distribution II
- Lecture 49: Sampling Distribution III
- Lecture 50: Parameter Estimation I
- Lecture 51: Parameter Estimator II
- Lecture 52: Parameter Estimator III
- Lecture 53: Parameter Estimator IV
- Lecture 54: Bayesian Estimation
- Lecture 55: Log normal distribution
- Lecture 56: Lorentz/Cauchy distribution
- Lecture 57: Lifetime and exponential distributions
- Lecture 58: Distributions from statistical mechanics
- Lecture 59: Uniform distribution and summary of probability distributions
- Lecture 60: Data processing: Introduction
- Lecture 61: Distribution function of a data series
- Lecture 62: Estimating mean and mean-square-deviation of data
- Lecture 63: Data with unequal weights
- Lecture 64: Robust estimates
- Lecture 65: From data to underlying distribution
- Lecture 66: Bootstrap method
- Lecture 67: Summary:Data processing
- Lecture 68: Hypothesis Testing I
- Lecture 69: Hypothesis Testing II
- Lecture 70: Hypothesis Testing III
- Lecture 71: Hypothesis Testing IV
- Lecture 72: Hypothesis Testing V
- Lecture 73: Hypothesis Testing VI
- Lecture 74: Graphical handling of data
- Lecture 75: Fitting and graphical handling of data: Introduction
- Lecture 76: Data transformable to linear
- Lecture 77: Data of known functional form
- Lecture 78: Calibration,Fitting,Hypotheses testing
- Lecture 79: Analysis of variance
- Lecture 80: Summary:Fittng and graphical handling of data
- Lecture 81: Regression Analysis - I
- Lecture 82: Regression Analysis - II
- Lecture 83: Regression Analysis - III
- Lecture 84: Regression Analysis - IV
- Lecture 85: Analysis of Variance-I
- Lecture 86: Analysis of Variance-II
- Lecture 87: Design of Experiment I
- Lecture 88: Design of Experiment II
- Lecture 89: Design of Experiment III
- Lecture 90: Design of Experiment IV
- Lecture 91: Summary of the course
- Lecture 92: Case studies: Introduction
- Lecture 93: Case study 1: Data smoothing I
- Lecture 94: Case study 1: Data smoothing II
- Lecture 95: Case study 2: Error analysis
- Lecture 96: Case study 3: Calibration
- Lecture 97: Case study 4: Design of experiment
- Lecture 98: Case study 5: Hypothesis testing
- Lecture 99: Course summary
- Live Session 26-03-2020