Binary Logistic Regression with Minitab

Binary Logistic Regression with Minitab

What you’ll learn

Mastering Binary Logistic Regression
Perform and interpret the results of Logistic Regression Analysis using Minitab

Requirements

You should have a good understanding of the Linear and Multiple Regression Analysis
Else take my Mastering Multiple Regression with Minitab: A Deep Dive course before starting this course.
You can download 30 days trial version of Minitab for practice from their website

Description

In this course, I will teach you one of the most commonly used classification techniques in data science, machine learning and statistics and that is: Binary Logistic Regression.A binomial logistic regression is used to predict the binary output (yes/no, true/false, sick/healthy) based on one or more continuous independent variables. It is often referred to as logistic regression. However, in Minitab, it is called binary logistic regression.I will use Minitab 19 to perform the analysis. The focus of my teaching will be on explaining the theoretical concepts and on analyzing and interpreting the results of the analysis.Performing Binary Logistic Regression in Minitab is easy. It is just a few selections and clicks and you are done with it. However, the difficult part is understanding and interpreting the results.The following concepts are covered in this course:The purpose of Binary Logistic RegressionThe concept of Odds and ln(Odds)Logistic Regression EquationOdds RatioConfusion MatrixReceiver Operating Characteristic (ROC) CurveR-Squared in context of Logistic RegressionHosmer-Lemeshow Goodness of Fit TestProject work to practice all the above conceptsIn the project work, marketers at a cereal company investigate the effectiveness of an ad campaign for a new cereal. Three factors considered in this project are the income, whether the person has seen the advertisement and whether the person has kids in the house.Here we performed binary logistic regression to determine whether people who saw the ad are more likely to buy the cereal.After completing this course, you will be easily able to perform the logistic regression, select the model.

Overview

Section 1: Introduction to Logistic Regression

Lecture 1 Introduction – Linear Regression

Lecture 2 Introduction – Logistic Regression

Lecture 3 Logistic Regression Results

Section 2: Basic Concepts – Odds, Exponential and Log etc.

Lecture 4 Understanding Odds – Part 1

Lecture 5 Understanding Odds – Part 2

Lecture 6 Understanding Exponential, Log and ln

Lecture 7 Understanding ln(Odds)

Lecture 8 Logistic Regression Equation

Section 3: Logistic Regression Using Minitab 19

Lecture 9 Perform Logistic Regression Using Minitab

Lecture 10 Understanding the Regression Equation

Lecture 11 What is Odds Ratio?

Lecture 12 Hypothesis Testing – ANOVA

Lecture 13 ANOVA – Types of Errors and the p-Value

Lecture 14 Introducing Confusion Matrix

Lecture 15 Confusion Matrix and Drawing the First Point on ROC Curve

Lecture 16 ROC Curve – Adding More Points to it

Lecture 17 ROC Curve – Add One More Point

Lecture 18 Plotting ROC Curve in Minitab 19

Lecture 19 Interpretation of ROC Curve

Lecture 20 R- Squared Value

Lecture 21 Calculating R-Squared Using Count Method

Lecture 22 Deviance R-Squared Value

Lecture 23 Goodness of Fit Test – Introduction

Lecture 24 Deviance and Pearson Goodness of Fit Tests

Lecture 25 Conditions for Deviance and Pearson Goodness of Fit Tests

Lecture 26 Hosmer-Lemeshow Goodness of Fit Test

Lecture 27 Three-in-One Plots

Section 4: Project Work – Cereal Purchase Decision

Lecture 28 Understanding the Data and Making Tables

Lecture 29 Making Some Plots for Better Understanding of the Data

Lecture 30 Performing Logistic Regression

Lecture 31 Using Stepwise Selection

Section 5: Bonus Section

Lecture 32 BONUS LECTURE

Six Sigma or Data Analysis professionals who want to take their understanding of Regression Analysis to the next level.,Anyone who wants to get a more in-depth insight into interpreting the Logistic Regression results

Course Information:

Udemy | English | 3h 4m | 986.61 MB
Created by: Sandeep Kumar, ­ Quality Gurus Inc.

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