Introduction and overview – Random Variables – Discrete Probability Distributions – Example Problems – Continuous Probability Distributions – Normal and Log Normal Probability Distributions – t-distribution – Chi-Square Distribution – F-distribution – Example Problems – Example Problems – Distribution of the random sample mean – Central Limit Theorem and its applications – Confidence Intervals – Maximum Likelihood Parameter Estimation – Example Problems – Formulation and Testing of Hypotheses – Errors in Hypothesis Testing – Hypothesis tests on population means, variances and ratio of variances

Design and Analysis of Single Factor Experiments – Randomized Block Design – Example Problems – Factorial Design with Two Factors – Factorial Design with Multiple Factors – Fractional Factorial Design – Example Problems – Matrix Approach to Linear Regression Analysis – Variance-Covariance Matrix – ANOVA in regression Analysis and Confidence Intervals – Extra Sum of Squares – Lack of Fit Analysis – Example Problems – Properties of Orthogonal Designs – Importance of Center Runs – Central Composite Design – Box Behnken Design and Face Centered Designs – Response Surface Methodology : Method of Steepest Ascent – Identification of optimal process conditions

### Course Curriculum

 Introduction Details 38:15 Random Variables Details 43:16 Discrete Probability Distributions Details 49:26 Example Set – I Details 47:41 Continuous probability distributions Details 32:52 Normal probability distribution Details 43:49 Exploratory Data Analysis â€“ Part A Details 44:51 Exploratory Data Analysis â€“ Part B Details 46:19 Example Set II Details 50:56 Example Set III Details 36:51 Random samples: Sampling distribution of the mean (Part A) Details 46:27 Random samples: Sampling distribution of the mean (Part B) Details 46:26 Point Estimation Details 46:47 Sampling distributions and the Central Limit Theorem Details 47:1 Example Set â€“ IV Part A Details 47:17 Estimation of Population Parameters Using Moments Details 42:30 Confidence Intervals (Part A) Details 45:20 Confidence Intervals (Part B) Details 45:42 The T-distribution Details 49:34 Chi-square distribution Details 15:43 F-Distribution Details 40:1 Example Set 5 Details 52:39 Hypothesis Testing â€“ Part A Details 45:4 Hypothesis Testing â€“ Part B Details 37:45 Hypothesis Testing â€“ Part C Details 28:56 Analysis of Experiments involving Single Factor â€“ Part A Details 51:44 Analysis of Experiments involving Single Factor â€“ Part B Details 52:1 Blocking and Randomization Details 39:30 Example Set -6 â€“ Part A Details 47:4 Example Set -6 â€“ Part B Details 51:55 Factorial Design of Experiments â€“ Part A Details 50:28 Factorial Design of Experiments â€“ Part B: 22 Factorial Design Details 58:45 Fractional Factorial Design â€“ Part A Details 46:34 Fractional Factorial Design â€“ Part B Details 44:53 Factorial Design of Experiments: Example Set (Part 7A) Details 47:30 Factorial Design of Experiments: Example Set (Part 7B) Details 54:27 Factorial Design of Experiments: Example Set (Part C) Details 42:22 Regression Analysis: Part A Details 52:32 Regression Analysis: Part B Details 43:25 Hypothesis Testing in Linear Regression Details 53:28 Discussion on Regression Output Details 59:22 Regression Analysis : Example Set 8 Details 52:3 Regression Analysis: Example Set 8 Continued Details 38:34 Regression Analysis: Example Set 8 Continued. Details 39:9 Orthogonal Model Fitting Concepts – Part A Details 40:2 Orthogonal Model Fitting Concepts â€“ Part B Details 28:15 Experimental Design Strategies – A Details 45:36 Experimental Design Strategies – B Details 35:35 Experimental Design Strategies – C Details 53:13 Response Surface Methodology – A Details 32:21

This course is delivered by NPTEL, is part of IIT Madras online courses.

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