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Connect the Dots: Linear and Logistic Regression

You, This Course and Us
Using Linear Regression to Connect the Dots
Two Common Applications of Regression
Extending Linear Regression to Fit Non-linear Relationships
Understanding Mean and Variance
Understanding Random Variables
The Normal Distribution
Setting up a Regression Problem
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 in Excel
Applying Simple Regression in R
Applying Simple Regression in Python
Introducing Multiple Regression
Some Risks inherent to Multiple Regression
Benefits of Multiple Regressions
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
Implementing Multiple Regression in Excel
Implementing Multiple Regression in R
Implementing Multiple Regression in Python
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
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
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

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