SLASSCOM Data Analytics Curriculum

Diploma level curriculum as a guidance to educational institutes.

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Home > Regression Anlysis > Simple Linear Regression

Module: Regression Anlysis

Topic: Simple Linear Regression

  1. Lesson: Introduction
  2. Lesson: Model assumptions
  3. Lesson: Parameter estimation
  4. Lesson: Interences about the model
  5. Lesson: Predictions
  6. Lesson: Interpretations of regression coefficient
  7. Lesson: Evaluating model adequacy

Lesson 1

Introduction

Self Learning Duration
30 mins
Lecture Duration
20 mins

Self learning content

https://www.kaggle.com/timniven/linear-regression-tutorial

Lecture content

what is linear regression?
When and Where to use it, Properties of Least Square Estimates.

Lab and tutorials

None

Lesson 2

Model assumptions

Self Learning Duration
30 mins
Lecture Duration
15 mins

Self learning content

Lecture content

Lab and tutorials

None

Lesson 3

Parameter estimation

Self Learning Duration
30 mins
Lecture Duration
30 mins

Self learning content

Lecture content

Theortical Estimation along with Practical Example

Lab and tutorials

None

Lesson 4

Interences about the model

Self Learning Duration
30 mins
Lecture Duration
60 mins

Self learning content

Lecture content

How to use hypothesis testing and confidence intervals in regression to estimate parameters, small introduction to ANOVA (without going deeper in to the mathematical side)

Lab and tutorials

None

Lesson 5

Predictions

Self Learning Duration
30 mins
Lecture Duration
30 mins

Self learning content

Lecture content

Practical Example on how to do a prediction using linear regression.

Lab and tutorials

None

Lesson 6

Interpretations of regression coefficient

Self Learning Duration
30 mins
Lecture Duration
120 mins

Self learning content

Lecture content

How to interpret the regression estimates. (use an example)

Lab and tutorials

Lesson 7

Evaluating model adequacy

Self Learning Duration
30 mins
Lecture Duration
120 mins

Self learning content

Lecture content

How to check the assumptions are statisfied, how to identify the outliers and how to deal with model departures ( transformations)

Lab and tutorials

Get a suitable dataset from kaggle and build a linear regression model slove a specific problem. (Case study)