Learn · Engineering Statistics
Lecture 60 of 62week-12
Lecture 60: Generating random samples using Python, Hypothesis Testing using Python (Contd.)
Prof. Manjesh Hanawal · IIT Bombay
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- Engineering Statistics, IIT Bombay (at NPTEL)
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- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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All the lectures
- Lecture 1: Introduction to Probability
- Lecture 2: Consequences of Axioms
- Lecture 3: Interpretation of Probability
- Lecture 4: Total Probability law and Baye’s Theorem I
- Lecture 5: Total Probability law and Baye’s Theorem II
- Lecture 6: Random variables and Cumulative Density Function
- Lecture 7: Discrete and Continuous random variables I
- Lecture 8: Discrete and Continuous random variables II
- Lecture 9: Expectation and Variance
- Lecture 10: Function of Random variables
- Lecture 11: Generating RVs, Joint Distribution of RVs
- Lecture 12: Joint Distribution of RVs and Marginal densities
- Lecture 13: Covariance of Random variables
- Lecture 14: Moment Generating Functions
- Lecture 15: Conditional PMF and PDF
- Lecture 16: Law of Large numbers, Central Limit Theorem
- Lecture 17: Application of Central Limit Theorem I
- Lecture 18: Application of Central Limit Theorem II
- Lecture 19: Gamma and Chisquare distributions
- Lecture 20: Beta distributions and Exponential families
- Lecture 21: Random Sampling, Sample mean and Sample variance
- Lecture 22: Sampling from Gaussian distribution and t distribution
- Lecture 23: Student’s t distribution
- Lecture 24: Fdistribution and its properties
- Lecture 25: Convergence of Random variables and Consistency
- Lecture 26: Order statistics, Median and Percentiles
- Lecture 27: Generating random sample Direct method
- Lecture 28: Generating random sample Indirect method
- Lecture 29: Introduction to python
- Lecture 30: Python Loops and Numpy library
- Lecture 31: Sufficiency Principles and Sufficient Statistics
- Lecture 32: Sufficient Statistics and Characterizatio n of Sufficient Statistics
- Lecture 33: Characterizatio n of Sufficient Statistics and Factorization Theorem
- Lecture 34: Example of Factorization Theorem, Minimal Sufficient Statistics
- Lecture 35: Minimal sufficient statistics
- Lecture 36: Test for minimal sufficient statistics with examples, Ancillary Statistics
- Lecture 37: Likelihood Functions, Maximum Likelihood Estimator
- Lecture 38: Method of moments, Baye’s Estimator
- Lecture 39: Evaluating Estimator, Cramer Rao Bound, Fisher Information
- Lecture 40: Evaluating Estimator, Cramer Rao Bound, Fisher Information (Contd.)
- Lecture 41: Hypothesis Testing, Likelihood Ratio Test
- Lecture 42: Hypothesis Testing, Bayes Test
- Lecture 43: Type I and II errors, Power Functions
- Lecture 44: Type I and II errors, Power Functions (Contd.)
- Lecture 45: Calculations of Power Functions
- Lecture 46: Unbiased Test, Uniformly Most Powerful Test, Neyman Pearson Lemma, Interval Estimation
- Lecture 47: Interval Estimation
- Lecture 48: Interval Estimation (Contd.)
- Lecture 49: Constructiong Confidence Intervals from tests
- Lecture 50: Python numpy and pandas functions II
- Lecture 51: Tutology of tests and confidence intervals
- Lecture 52: Tutology of tests and confidence intervals (Contd.)
- Lecture 53: pvalue, ptest of significance of a statistical test
- Lecture 54: ttest & Ftest, ANOVA
- Lecture 55: Nonparametric test, Goodness of fit, Chi squared test
- Lecture 56: Distribution of Chisquared test statistics
- Lecture 57: Kolmogrov Smirnov test
- Lecture 58: Lilliefors’s test and Explorator Data Analysis, QQ Plot and P P Plot
- Lecture 59: Generating random samples using Python, Hypothesis Testing using Python
- Lecture 60: Generating random samples using Python, Hypothesis Testing using Python (Contd.)
- Live Session 19-03-2024
- Live Session 16-04-2024