Description

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 2-3 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!

 

Course Curriculum

Introduction
You, This Course and Us FREE 00:01:48
Connect the Dots with Linear Regression
Using Linear Regression to Connect the Dots 00:09:04
Two Common Applications of Regression 00:05:24
Extending Linear Regression to Fit Non-linear Relationships 00:02:36
Basic Statistics Used for Regression
Understanding Mean and Variance FREE 00:06:03
Understanding Random Variables 00:11:27
The Normal Distribution 00:09:31
Simple Regression
Setting up a Regression Problem FREE 00:11:36
Using Simple regression to Explain Cause-Effect Relationships 00:04:54
Using Simple regression for Explaining Variance 00:08:07
Using Simple regression for Prediction 00:04:04
Interpreting the results of a Regression 00:07:25
Mitigating Risks in Simple Regression 00:07:56
Applying Simple Regression
Applying Simple Regression in Excel FREE 00:11:54
Applying Simple Regression in R 00:11:13
Applying Simple Regression in Python 00:06:04
Multiple Regression
Introducing Multiple Regression 00:07:03
Some Risks inherent to Multiple Regression 00:10:06
Benefits of Multiple Regression 00:03:48
Introducing Categorical Variables 00:06:58
Interpreting Regression results – Adjusted R-squared 00:07:01
Interpreting Regression results – Standard Errors of Co-efficients 00:08:11
Interpreting Regression results – t-statistics and p-values 00:05:31
Interpreting Regression results – F-Statistic 00:00:00
Applying Multiple Regression using Excel
Implementing Multiple Regression in Excel 00:08:48
Implementing Multiple Regression in R 00:06:25
Implementing Multiple Regression in Python 00:04:21
Logistic Regression for Categorical Dependent Variables
Understanding the need for Logistic Regression 00:09:22
Setting up a Logistic Regression problem 00:06:01
Applications of Logistic Regression 00:09:54
The link between Linear and Logistic Regression 00:08:13
The link between Logistic Regression and Machine Learning 00:04:15
Solving Logistic Regression
Understanding the intuition behind Logistic Regression and the S-curve 00:06:21
Solving Logistic Regression using Maximum Likelihood Estimation 00:10:01
Solving Logistic Regression using Linear Regression 00:05:31
Binomial vs Multinomial Logistic Regression 00:05:21
Applying Logistic Regression
Predict Stock Price movements using Logistic Regression in Excel 00:09:51
Predict Stock Price movements using Logistic Regression in R 00:07:58
Predict Stock Price movements using Rule-based and Linear Regression 00:06:43
Predict Stock Price movements using Logistic Regression in Python 00:04:49

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