Learn · Essentials of Data Science With R Software-2: Sampling Theory and Linear Regression Analysis
Lecture 11 of 56Week 2
Lecture 11: Ensuring Representativeness and Type of Surveys
Prof. Shalabh · IIT Kanpur
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- Essentials of Data Science With R Software-2: Sampling Theory and Linear Regression Analysis, IIT Kanpur (at NPTEL)
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All the lectures
- Lecture 01: What is Data Science?
- Lecture 02: Installation and Working with R
- Lecture 03: Calculations with R as a Calculator
- Lecture 04: Calculations with Data Vectors
- Lecture 05: Built-in Commands and Missing Data Handling
- Lecture 06: Operations with Matrices
- Lecture 07: Data Handling
- Lecture 08: Graphics and Plots
- Lecture 09: Sampling, Sampling Unit, Population and Sample
- Lecture 10: Terminologies and Concepts
- Lecture 11: Ensuring Representativeness and Type of Surveys
- Lecture 12: Conducting Surveys and Ensuring Representativeness
- Lecture 13: SRSWOR, SRSWR, and Selection of Unit- 1
- Lecture 14: SRSWOR, SRSWR, and Selection of Unit- 2
- Lecture 15: Probabilities of Selection of Samples
- Lecture 16: SRSWOR and SRSWR with R with "sample" Package
- Lecture 17: Examples of SRS with R using "sample" Package
- Lecture 18: Simple Random Sampling :: SRS with R using sampling- and sample- Packages
- Lecture 19: Simple Random Sampling :: Estimation of Population Mean
- Lecture 20: Simple Random Sampling :: Estimation of Population Variance
- Lecture 21: Simple Random Sampling :: Estimation of Population Variance
- Lecture 22: SRS :: Confidence Interval Estimation of Population Mean
- Lecture 23: SRS :: Estimation of Mean, Variance and Confidence Interval in SRSWOR using R
- Lecture 24: SRS :: Estimation of Mean, Variance and Confidence Interval in SRSWR using R
- Lecture 25: Sampling for Proportions and Percentages :: Basic Concepts
- Lecture 26: Sampling for Proportions and Percentages :: Mean and Variance of Sample Proportion
- Lecture 27: Sampling for Proportions and Percentages :: Sampling for Proportions with R
- Lecture 28: Stratified Random Sampling :: Drawing the Sample and Sampling Procedure
- Lecture 29: Stratified Random Sampling :: Estimation of Population Mean, Population Variance and Confidence Interval
- Lecture 30: Stratified Random Sampling :: Sample Allocation and Variances Under Allocation
- Lecture 31: Stratified Random Sampling :: Drawing of Sample Using sampling and strata Packages in R
- Lecture 32: Stratified Random Sampling :: Drawing of Sample Using survey Package in R
- Lecture 33: Bootstrap Methodology :: What is Bootstrap and Methodology
- Lecture 34: Bootstrap Methodology :: EDF, Bootstrap Bias and Bootstrap Standard Errors
- Lecture 35: Bootstrap Methodology :: Bootstrap Analysis Using boot Package in R
- Lecture 36: Bootstrap Methodology :: Bootstrap Confidence Interval
- Lecture 37: Bootstrap Methodology :: Bootstrap Confidence Interval Using boot and bootstrap Packages in R
- Lecture 38: Bootstrap Methodology :: Example of Bootstrap Analysis Using boot Package
- Lecture 39: Introduction to Linear Models and Regression :: Introduction and Basic Concepts
- Lecture 40: Simple Linear Regression Analysis :: Basic Concepts and Least Squares Estimation
- Lecture 41: Simple Linear Regression Analysis :: Fitting Linear Model With R Software
- Lecture 42: Simple Linear Regression Analysis :: Properties of Least Squares Estimators
- Lecture 43: Simple Linear Regression Analysis :: Maximum Likelihood and Confidence Interval Estimation
- Lecture 44: Simple Linear Regression Analysis :: Test of Hypothesis and Confidence Interval Estimation With R
- Lecture 45: Multiple Linear Regression Analysis :: Basic Concepts
- Lecture 46: Multiple Linear Regression Analysis :: OLSE, Fitted Model and Residuals
- Lecture 47: Multiple Linear Regression Analysis :: Model Fitting With R Software
- Lecture 48: Multiple Linear Regression Analysis :: Properties of OLSE and Maximum Likelihood Estimation
- Lecture 49: Multiple Linear Regression Analysis :: Test of Hypothesis and Confidence Interval Estimation on Individual Regression Coefficients
- Lecture 50: Analysis of Variance and Implementation in R Software
- Lecture 51: Goodness of Fit and Implementation in R Software
- Lecture 52: Variable Selection using LASSO Regression :: Introduction and Basic Concepts
- Lecture 53: Variable Selection using LASSO Regression :: LASSO with R
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- Live Session 08-03-2024