NPTEL : NOC:Essentials of Data Science With R Software 2: Sampling Theory and Linear Regression Analysis (Mathematics)

Co-ordinators : Prof. Shalabh


Lecture 1 - What is Data Science ?

Lecture 2 - Installation and Working with R

Lecture 3 - Calculations with R as a Calculator

Lecture 4 - Calculations with Data Vectors

Lecture 5 - Built-in Commands and Missing Data Handling

Lecture 6 - Operations with Matrices

Lecture 7 - Data Handling

Lecture 8 - Graphics and Plots

Lecture 9 - 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