Essentials of Data Science With R Software _ 2: Sampling Theory and Linear Regression Analysis YY6FER85D7
| URI | m2://res/YY6FER85D7 |
|---|---|
| Kind | course |
| Creators | Shalabh (instructor) |
| Date | 2021-02-12 |
| Scope | in |
| Ids | course 05pjsgx75:111104147 |
Expressions
e1: edition
| Language | en |
|---|---|
| Date | 2021-02-12 |
- m1: page, IIT Kanpur (record). nptel 111104147
- m2: page, 2021-02-12 (record). nptel 111104147
Contents
- part 1: Week 1
- session 1: Lecture 01: What is Data Science?
- session 2: Lecture 02: Installation and Working with R
- session 3: Lecture 03: Calculations with R as a Calculator
- session 4: Lecture 04: Calculations with Data Vectors
- session 5: Lecture 05: Built-in Commands and Missing Data Handling
- session 6: Lecture 06: Operations with Matrices
- session 7: Lecture 07: Data Handling
- session 8: Lecture 08: Graphics and Plots
- part 2: Week 2
- session 9: Lecture 09: Sampling, Sampling Unit, Population and Sample
- session 10: Lecture 10: Terminologies and Concepts
- session 11: Lecture 11: Ensuring Representativeness and Type of Surveys
- session 12: Lecture 12: Conducting Surveys and Ensuring Representativeness
- part 3: Week 3
- session 13: Lecture 13: SRSWOR, SRSWR, and Selection of Unit- 1
- session 14: Lecture 14: SRSWOR, SRSWR, and Selection of Unit- 2
- session 15: Lecture 15: Probabilities of Selection of Samples
- session 16: Lecture 16: SRSWOR and SRSWR with R with "sample" Package
- session 17: Lecture 17: Examples of SRS with R using "sample" Package
- part 4: Week 4
- session 18: Lecture 18: Simple Random Sampling :: SRS with R using sampling- and sample- Packages
- session 19: Lecture 19: Simple Random Sampling :: Estimation of Population Mean
- session 20: Lecture 20: Simple Random Sampling :: Estimation of Population Variance
- session 21: Lecture 21: Simple Random Sampling :: Estimation of Population Variance
- part 5: Week 5
- session 22: Lecture 22: SRS :: Confidence Interval Estimation of Population Mean
- session 23: Lecture 23: SRS :: Estimation of Mean, Variance and Confidence Interval in SRSWOR using R
- session 24: Lecture 24: SRS :: Estimation of Mean, Variance and Confidence Interval in SRSWR using R
- session 25: Lecture 25: Sampling for Proportions and Percentages :: Basic Concepts
- part 6: Week 6
- session 26: Lecture 26: Sampling for Proportions and Percentages :: Mean and Variance of Sample Proportion
- session 27: Lecture 27: Sampling for Proportions and Percentages :: Sampling for Proportions with R
- session 28: Lecture 28: Stratified Random Sampling :: Drawing the Sample and Sampling Procedure
- session 29: Lecture 29: Stratified Random Sampling :: Estimation of Population Mean, Population Variance and Confidence Interval
- part 7: Week 7
- session 30: Lecture 30: Stratified Random Sampling :: Sample Allocation and Variances Under Allocation
- session 31: Lecture 31: Stratified Random Sampling :: Drawing of Sample Using sampling and strata Packages in R
- session 32: Lecture 32: Stratified Random Sampling :: Drawing of Sample Using survey Package in R
- session 33: Lecture 33: Bootstrap Methodology :: What is Bootstrap and Methodology
- session 34: Lecture 34: Bootstrap Methodology :: EDF, Bootstrap Bias and Bootstrap Standard Errors
- part 8: Week 8
- session 35: Lecture 35: Bootstrap Methodology :: Bootstrap Analysis Using boot Package in R
- session 36: Lecture 36: Bootstrap Methodology :: Bootstrap Confidence Interval
- session 37: Lecture 37: Bootstrap Methodology :: Bootstrap Confidence Interval Using boot and bootstrap Packages in R
- session 38: Lecture 38: Bootstrap Methodology :: Example of Bootstrap Analysis Using boot Package
- part 9: Week 9
- session 39: Lecture 39: Introduction to Linear Models and Regression :: Introduction and Basic Concepts
- session 40: Lecture 40: Simple Linear Regression Analysis :: Basic Concepts and Least Squares Estimation
- session 41: Lecture 41: Simple Linear Regression Analysis :: Fitting Linear Model With R Software
- part 10: Week 10
- session 42: Lecture 42: Simple Linear Regression Analysis :: Properties of Least Squares Estimators
- session 43: Lecture 43: Simple Linear Regression Analysis :: Maximum Likelihood and Confidence Interval Estimation
- session 44: Lecture 44: Simple Linear Regression Analysis :: Test of Hypothesis and Confidence Interval Estimation With R
- session 45: Lecture 45: Multiple Linear Regression Analysis :: Basic Concepts
- part 11: Week 11
- session 46: Lecture 46: Multiple Linear Regression Analysis :: OLSE, Fitted Model and Residuals
- session 47: Lecture 47: Multiple Linear Regression Analysis :: Model Fitting With R Software
- session 48: Lecture 48: Multiple Linear Regression Analysis :: Properties of OLSE and Maximum Likelihood Estimation
- session 49: Lecture 49: Multiple Linear Regression Analysis :: Test of Hypothesis and Confidence Interval Estimation on Individual Regression Coefficients
- part 12: Week 12
- session 50: Lecture 50: Analysis of Variance and Implementation in R Software
- session 51: Lecture 51: Goodness of Fit and Implementation in R Software
- session 52: Lecture 52: Variable Selection using LASSO Regression :: Introduction and Basic Concepts
- session 53: Lecture 53: Variable Selection using LASSO Regression :: LASSO with R
- part 13: Live Session
- session 54: Live Session 01-04-2021
- session 55: Live Session 03-12-2022
- session 56: Live Session 08-03-2024
Catalogue records
- nptel: 111104147 (manifestation)
- nptel_video: 111104147 (manifestation)