Connect the Dots: Linear and Logistic Regression
4.5 (54 ratings)
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Connect the Dots: Linear and Logistic Regression

Build robust models in Excel, R and Python
4.5 (54 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
7,527 students enrolled
Created by Loony Corn
Last updated 5/2017
English [Auto-generated]
Current price: $10 Original price: $50 Discount: 80% off
5 hours left at this price!
30-Day Money-Back Guarantee
  • 5 hours on-demand video
  • 39 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Build robust linear models that stand up to scrutiny in Excel, R and Python
  • Use simple and multiple regression to explain variance
  • Use simple and multiple regression to predict an outcome
  • Intepret the results of a regression
  • Understand the risks involved in regression and avoid common pitfalls
View Curriculum
  • No statistics background required. Everything is built up from basic math
  • The models are implemented in Excel, R and Python. Install these environments to follow along with the demos

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

This course will teach you how to build robust linear models and do logistic regression in Excel, R and Python.

Let’s parse that.

Robust linear models : Linear Regression is a powerful method for quantifying the cause and effect relationships that affect different phenomena in the world around us. This course will teach you how to build robust linear models that will stand up to scrutiny when you apply them to real world situations. 

Logistic regression: Logistic regression has many cool applications : analyzing consequences of past events, allocating resources, solving binary classification problems using machine learning and so on. This course will help you understand the intuition behind logistic regression and how to solve it using cookie-cutter techniques. 

Excel, R and Python :  Put what you've learnt into practice. Leverage these powerful analytical tools to build models for stock returns. 

What's covered?

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

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?
  • Yep! Data analysts who want to move from summarizing data to explaining and prediction
  • Yep! Folks aspiring to be data scientists
  • Yep! Any business professionals who want to apply Linear regression to solve relevant problems
Students Who Viewed This Course Also Viewed
Curriculum For This Course
40 Lectures
1 Lecture 01:54

We start with an introduction, what the course is about and what you'll be able to do at the end of it 

Preview 01:54
Connect the Dots with Linear Regression
3 Lectures 17:04

Build a thoughtful point of view from data using Regression 

Preview 09:04

Use regression to explain variance and predict an outcome

Two Common Applications of Regression

Linear Regression can be extended to model exponential or polynomial relationships too! 

Extending Linear Regression to Fit Non-linear Relationships
Basic Statistics Used for Regression
3 Lectures 27:01

Understanding Random Variables

The Normal Distribution
Simple Regression
6 Lectures 44:05

Using Simple regression to Explain Cause-Effect Relationships

Using Simple regression for Explaining Variance

Using Simple regression for Prediction

Interpreting the results of a Regression

Mitigating Risks in Simple Regression
Applying Simple Regression
3 Lectures 29:15

Applying Simple Regression in R

Applying Simple Regression in Python
Multiple Regression
8 Lectures 51:33
Introducing Multiple Regression

Some Risks inherent to Multiple Regression

Benefits of Multiple Regression

Introducing Categorical Variables

Interpreting Regression results - Adjusted R-squared

Interpreting Regression results - Standard Errors of Co-efficients

Interpreting Regression results - t-statistics and p-values

Interpreting Regression results - F-Statistic
Applying Multiple Regression using Excel
3 Lectures 19:41
Implementing Multiple Regression in Excel

Implementing Multiple Regression in R

Implementing Multiple Regression in Python
Logistic Regression for Categorical Dependent Variables
5 Lectures 37:47
Understanding the need for Logistic Regression

Setting up a Logistic Regression problem

Applications of Logistic Regression

The link between Linear and Logistic Regression

The link between Logistic Regression and Machine Learning
Solving Logistic Regression
4 Lectures 27:16
Understanding the intuition behind Logistic Regression and the S-curve

Solving Logistic Regression using Maximum Likelihood Estimation

Solving Logistic Regression using Linear Regression

Binomial vs Multinomial Logistic Regression
Applying Logistic Regression
4 Lectures 29:23
Predict Stock Price movements using Logistic Regression in Excel

Predict Stock Price movements using Logistic Regression in R

Predict Stock Price movements using Rule-based and Linear Regression

Predict Stock Price movements using Logistic Regression in Python
About the Instructor
Loony Corn
4.3 Average rating
5,428 Reviews
42,405 Students
75 Courses
An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years  working in tech, in the Bay Area, New York, Singapore and Bangalore.

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

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)