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Connect the Dots: Linear and Logistic Regression in Excel, Python and R
Introduction
You, This Course and Us (1:54)
Course Materials
Connect the Dots with Linear Regression
Using Linear Regression to Connect the Dots (9:04)
Two Common Applications of Regression (5:24)
Extending Linear Regression to Fit Non-linear Relationships (2:36)
Basic Statistics Used for Regression
Understanding Mean and Variance (6:03)
Understanding Random Variables (11:27)
The Normal Distribution (9:31)
Simple Regression
Setting up a Regression Problem (11:36)
Using Simple regression to Explain Cause-Effect Relationships (4:57)
Using Simple regression for Explaining Variance (8:07)
Using Simple regression for Prediction (4:04)
Interpreting the results of a Regression (7:25)
Mitigating Risks in Simple Regression (7:56)
Applying Simple Regression Using Excel
Applying Simple Regression in Excel (11:57)
Applying Simple Regression in R (11:14)
Applying Simple Regression in Python (6:05)
Multiple Regression
Introducing Multiple Regression (7:03)
Some Risks inherent to Multiple Regression (10:06)
Benefits of Multiple Regression (3:48)
Introducing Categorical Variables (6:58)
Interpreting Regression results - Adjusted R-squared (7:02)
Interpreting Regression results - Standard Errors of Co-efficients (8:12)
Interpreting Regression results - t-statistics and p-values (5:32)
Interpreting Regression results - F-Statistic (2:52)
Applying Multiple Regression using Excel
Implementing Multiple Regression in Excel (8:54)
Implementing Multiple Regression in R (6:26)
Implementing Multiple Regression in Python (4:21)
Logistic Regression for Categorical Dependent Variables
Understanding the need for Logistic Regression (9:24)
Setting up a Logistic Regression problem (6:02)
Applications of Logistic Regression (9:55)
The link between Linear and Logistic Regression (8:13)
The link between Logistic Regression and Machine Learning (4:16)
Solving Logistic Regression
Understanding the intuition behind Logistic Regression and the S-curve (6:21)
Solving Logistic Regression using Maximum Likelihood Estimation (10:02)
Solving Logistic Regression using Linear Regression (5:32)
Binomial vs Multinomial Logistic Regression (4:04)
Applying Logistic Regression
Predict Stock Price movements using Logistic Regression in Excel (9:52)
Predict Stock Price movements using Logistic Regression in R (8:00)
Predict Stock Price movements using Rule-based and Linear Regression (6:44)
Predict Stock Price movements using Logistic Regression in Python (4:49)
Predict Stock Price movements using Rule-based and Linear Regression
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