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 | 948.40 MB
Created by: Sandeep Kumar, Quality Gurus Inc.
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