This ’Linear & Logistic Regression’ online training course will teach you how to build robust linear models and do logistic regressions in Excel, R, and Python that will stand up to scrutiny when you apply them to real world situations. Supplemental Materials included!

OBJECTIVE

In this Linear & Logistic Regression course, you’ll learn about topics such as: understanding random variables, cause-effect relationships, maximum likelihood estimation, and so much more. Follow along with the experts as they break down these concepts in easy-to-understand lessons.

Highlights:-

Simple Regression :

Method of least squares, Explaining variance, Forecasting an outcome

Residuals, assumptions about residuals

Implement simple regression in Excel, R and Python

Interpret regression results and avoid common pitfalls

Multiple Regression :

Implement Multiple regression in Excel, R and Python

Introduce a categorical variable

Logistic Regression :

Applications of Logistic Regression, the link to Linear Regression and Machine Learning

Solving logistic regression using Maximum Likelihood Estimation and Linear Regression

Extending Binomial Logistic Regression to Multinomial Logistic Regression

Implement Logistic regression to build a model stock price movements in Excel, R and Python

CONTENT

Chapter I: Introduction

Lesson I: You, This Course, & Us!

Chapter II: Connect the Dots with Linear Regression

Lesson I: Using Linear Regression to Connect the Dots

Lesson II: Two Common Applications of Regression

Lesson III: Extending Linear Regression to Fit Non-linear Relationships

Chapter III: Basic Statistics Used for Regression

Lesson I: Understanding Mean & Variance

Lesson II: Understanding Random Variables

Lesson III: The Normal Distribution

Chapter IV: Simple Regression

Lesson I: Setting up a Regression Problem

Lesson II: Using Simple Regression to Explain Cause-Effect Relationships

Lesson III: Using Simple Regression for Explaining Variance

Lesson IV: Using Simple Regression for Prediction

Lesson V: Interpreting the results of a Regression

Lesson VI: Mitigating Risks in Simple Regression

Chapter V: Applying Simple Regression

Lesson I: Applying Simple Regression in Excel

Lesson III: Applying Simple Regression in R

Lesson III: Applying Simple Regression in Python

Chapter 06: Multiple Regression

Lesson 01: Introducing Multiple Regression

Lesson 02: Some Risks inherent to Multiple Regression

Chapter 07: Applying Multiple Regression using Excel

Lesson 01: Implementing Multiple Regression in Excel

Lesson 02: Implementing Multiple Regression in R

Lesson 03: Implementing Multiple Regression in Python

Chapter 08: Logistic Regression for Categorical Dependent Variables

Lesson 01: Understanding the need for Logistic Regression

Lesson 02: Setting up a Logistic Regression problem

Lesson 03: Applications of Logistic Regression

Lesson 04: The link between Linear & Logistic Regression

Lesson 05: The link between Logistic Regression & Machine Learning

Chapter 09: Solving Logistic Regression

Lesson 01: Understanding the intuition behind Logistic Regression & the S-curve

Lesson 02: Solving Logistic Regression using Maximum Likelihood Estimation

Lesson 03: Solving Logistic Regression using Linear Regression

Lesson 04: Binomial vs Multinomial Logistic Regression

Chapter 10: Solving Logistic Regression

Lesson 01: Predict Stock Price movements using Logistic Regression in Excel

Lesson 02: Predict Stock Price movements using Logistic Regression in R

Lesson 03: Predict Stock Price movements using Rule-based & Linear Regression

Lesson 04: Predict Stock Price movements using Logistic Regression in Python

LENGTH

5 hrs

LENGTH

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum

Frequently Asked Questions

Total Training

Total Training is a pioneer in innovative online and DVD-based training for leading creative design, digital video, and office productivity software programs.

Ranging from casual hobbyists to the most seasoned professionals, our users quickly learn new software applications and broaden their knowledge of programs currently used. Narrated in an entertaining format by industry experts and leading authors, our video-based software training titles are critical tools for anyone wanting to learn tips, techniques, and best practices from the most respected names in the business.

We currently offer affordable subscriptions to our All-Access Library, which contains hundreds of courses, thousands of clips, and project files so users can follow along.