Schedule and Lectorials

Here is your road map for the course!

Week 1-4: lectures (online)

Week 1

  • Introduction to regression analysis

  • Simple linear regression

    • Terminologies

    • Correlation

πŸ“šSlides πŸ“’ [Reading-Ch1 Introduction to Linear Regression Analysis] πŸ“Š Data Sets - anscombe πŸ“Ž Problems - Install R and R studio πŸ”–Answers

Week 2 - 3

  • Simple linear regression (cont.)

    • Simple linear regression model

    • Least-squares estimation of the parameters

πŸ“šSlides πŸ“’ [Reading-Ch2/2.2.2 Introduction to Linear Regression Analysis] πŸ“Š Data Sets - alr3::heights πŸ“Ž Problems πŸ”–Answers - Discussions

Week 4

  • Simple linear regression (cont.)

    • Model adequacy checking

    • Introduction

    • Residual Analysis

πŸ“šSlides πŸ“’ Reading - Ch1 (2.6) and Ch4 (4.2.3) πŸ“Š Data Sets πŸ“Ž Problems πŸ”–Answers

Week 5-6: Discussion/practical (in class)

Week 5 - 6

  • Coefficient of determination

  • Hypothesis testing on the slope and intercept

  • Interval estimation in simple linear regression

  • Prediction of new observations: point estimates and prediction intervals

  • Regression through the origin

  • Regression analysis with R

  • Problem discussion and Regression Analysis with R

  • Regression analysis with other software (Python, Minitab, SPSS, Excel) πŸ“š

πŸ“š 1. Slides 2. Basics of R Programming πŸ“’ Reading - Ch1 and Ch2 (2.3, 2.4) πŸ“Š Data Set - house.csv πŸ“Ž Problems Tutorial 2 πŸ”– Additional problems - Ch2 Problems: 2.1 - 2.17

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Week 7 - 8: lectures (online)

Week 7

R for Reproducible Scientific Analysis - Visit Google Classroom

Assignment due: 19 Oct 2020

Mid Exam: Cancelled due to Covid-19 Outbreak

Week 8

  • Multiple linear regression

    • Introduction

    • Estimation of the model parameters

    • Residual analysis

πŸ“š Slides πŸ“’ Reading - Ch3 πŸ“Š Data Sets - heart.data.csv πŸ“Ž Problems πŸ”–Answers

Week 9

  • ANOVA

    • Test of significance of regression

    • Tests on individual regression coefficients

πŸ“š Slides πŸ“’ Reading - here πŸ“Š Data Sets - heart.data.csv and alr3::heights πŸ“Ž Problems - Quiz Google Classroom πŸ”–Answers

Week 10

  • Confidence intervals in multiple regression

    • Confidence intervals on the regression coefficients

    • Confidence interval estimation of the mean response

  • Prediction of new observations

Mid Evaluation Assignment - Visit LMS/ Google Classroom (1 Nov 2020) πŸ“

Assignment deadline: 15 Nov 2020

Special considerations email me on or before 8 Nov 2020

πŸ“š Slides πŸ“’ Reading - Chapter 3, Montgomery, Peck, Vining πŸ“Š Data Sets πŸ“Ž Problems πŸ”–Answers

Week 11

  • Transformations

    • Variance-stabilization transformations

    • Transformations to linearize the model

    • Analytical methods for selecting a transformation

πŸ“šSlides πŸ“’ Reading - Section 3.3, Montgomery, Peck, Vining πŸ“Š Data Sets - coconut.csv salarydata.csv πŸ“Ž Problems πŸ”–Answers

14 November 2020 - Public Holiday (Deepavali Festival Day)

Week 12

  • Detection and treatment of outliers

  • Diagnostics for leverage and influence

    • Importance of detecting influential observations

    • Leverage

    • Measures of influence

    • Treatment of influential observations

  • Regression with qualitative variables

    • Indicator/ Dummy variables

    • Indicator variable with more than two levels

    • More than one indicator variable

    • Interaction term involving dummy variables

πŸ“š Slides-i Slides-ii πŸ“’ Reading - Ch6, 11.1, Ch8 (Montgomery, Peck, Vining) πŸ“Š Data Sets - see slides πŸ“Ž Problems πŸ”–Answers - see Week 13 discussion notes

Week 13-14: Discussion/practical (live - zoom)

Week 13

  • Variable selection procedures

    • Introduction

    • All possible regressions

    • Forward selection procedure

    • Backward elimination procedure

    • Stepwise method

πŸ“š Slides πŸ“’ Reading - Ch 9 πŸ“Š Data Sets - real-estate.csv πŸ“Ž Problems πŸ”–Answers - Inclass discussion

Week 14

  • Multicollinearity

    • Introduction

    • Multocollinearity diagnostics

    • Treatments for dealing with multicollinearity

  • Bootstrapping in regression

    • Introduction to bootstrap sampling

    • Bootstrap sampling in regression

    • Bootsrap confidence intervals

πŸ“šSlides - 1 Slides - 2 πŸ“’ Reading - Ch 10 πŸ“Š Data Sets-bloodpressure πŸ“Ž Problems πŸ”–Answers - Inclass discussion
  • Mid semester examination - Answer discussions.

  • Model questions: Download from the Google Classroom. We will discuss answers next week (12 Dec 2020).

Week 15: Revision (live - zoom)

Week 15: Revision

  • Current state-of-art techniques in regression analysis and statistical modelling

  • Model questions: Discuss answers

  • Open problems

  • Recap

πŸ“šSlides πŸ“’ Reading πŸ“Š Data Sets πŸ“Ž Problems πŸ”–Answers

Week 16-18: Study leave and Final Examination

Week 16: Study leave

Week 17-18: Final exam

END