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Connect the Dots: Linear and Logistic Regression

Video Description

Build robust models in Excel, R and Python

About This Video

  • 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
  • Implement Multiple regression in Excel, R and Python
  • Introduce a categorical variable
  • 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

In Detail

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.

Table of Contents

  1. Chapter 1 : Introduction
    1. You, This Course and Us 00:01:55
  2. Chapter 2 : Connect the Dots with Linear Regression
    1. Using Linear Regression to Connect the Dots 00:09:04
    2. Two Common Applications of Regression 00:05:24
    3. Extending Linear Regression to Fit Non-linear Relationships 00:02:36
  3. Chapter 3 : Basic Statistics Used for Regression
    1. Understanding Mean and Variance 00:06:04
    2. Understanding Random Variables 00:11:27
    3. The Normal Distribution 00:09:32
  4. Chapter 4 : Simple Regression
    1. Setting up a Regression Problem 00:11:37
    2. Using Simple regression to Explain Cause-Effect Relationships 00:04:57
    3. Using Simple regression for Explaining Variance 00:08:07
    4. Using Simple regression for Prediction 00:04:04
    5. Interpreting the results of a Regression 00:07:26
    6. Mitigating Risks in Simple Regression 00:07:57
  5. Chapter 5 : Applying Simple Regression
    1. Applying Simple Regression in Excel 00:11:57
    2. Applying Simple Regression in R 00:11:14
    3. Applying Simple Regression in Python 00:06:06
  6. Chapter 6 : Multiple Regression
    1. Introducing Multiple Regression 00:07:04
    2. Some Risks inherent to Multiple Regression 00:10:06
    3. Benefits of Multiple Regressions 00:03:49
    4. Introducing Categorical Variables 00:06:58
    5. Interpreting Regression results - Adjusted R-squared 00:07:02
    6. Interpreting Regression results - Standard Errors of Co-efficients 00:08:12
    7. Interpreting Regression results - t-statistics and p-values 00:05:33
    8. Interpreting Regression results - F-Statistic 00:02:52
  7. Chapter 7 : Applying Multiple Regression using Excel
    1. Implementing Multiple Regression in Excel 00:08:54
    2. Implementing Multiple Regression in R 00:06:26
    3. Implementing Multiple Regression in Python 00:04:21
  8. Chapter 8 : Logistic Regression for Categorical Dependent Variables
    1. Understanding the need for Logistic Regression 00:09:24
    2. Setting up a Logistic Regression problem 00:06:03
    3. Applications of Logistic Regression 00:09:55
    4. The link between Linear and Logistic Regression 00:08:13
    5. The link between Logistic Regression and Machine Learning 00:04:16
  9. Chapter 9 : Solving Logistic Regression
    1. Understanding the intuition behind Logistic Regression and the S-curve 00:06:21
    2. Solving Logistic Regression using Maximum Likelihood Estimation 00:10:03
    3. Solving Logistic Regression using Linear Regression 00:05:32
    4. Binomial vs Multinomial Logistic Regression 00:05:21
  10. Chapter 10 : Applying Logistic Regression
    1. Predict Stock Price movements using Logistic Regression in Excel 00:09:53
    2. Predict Stock Price movements using Logistic Regression in R 00:08:00
    3. Predict Stock Price movements using Rule-based and Linear Regression 00:06:44
    4. Predict Stock Price movements using Logistic Regression in Python 00:04:50