# Advanced Statistical Methods in Python – 365 Data Science

## Advanced Statistical Methods in Python – 365 Data Science Description

1. Linear regression:
Correlation vs Regression – 365 Data Science
Decomposition of Variability – 365 Data Science
First Regression in Python – 365 Data Science
Geometrical Representation of the Linear Regression Model – 365 Data Science
How to Interpret the Regression Table – 365 Data Science
Introduction to Regression Analysis – 365 Data Science
Python Packages Installation – 365 Data Science
R-Squared – 365 Data Science
The Linear Regression Model – 365 Data Science
Using Seaborn for Graphs – 365 Data Science
Welcome to Advanced Statistics! – 365 Data Science
What is the OLS- – 365 Data Science

2. Multiple Linear Regression:
A1- Linearity – 365 Data Science
A2- No Endogeneity – 365 Data Science
A3- Normality and Homoscedasticity – 365 Data Science
A4- No Autocorrelation – 365 Data Science
A5- No Multicollinearity – 365 Data Science
Adjusted R-Squared – 365 Data Science
Dealing with Categorical Data – Dummy Variables – 365 Data Science
Making Predictions with the Linear Regression – 365 Data Science
Multiple Linear Regression – 365 Data Science
OLS Assumptions – 365 Data Science
Test for Significance of the Model (F-Test) – 365 Data Science

CISCO CYBEROPS: MANAGING POLICIES AND PROCEDURES

3. Linear Regression with sklearn:
Adjusted R-Squared – 365 Data Science
Creating a Summary Table with the p-values – 365 Data Science
Feature Scaling – 365 Data Science
Feature Selection through p-values (F-regression) – 365 Data Science
Feature Selection through Standardization – 365 Data Science
Game Plan for sklearn – 365 Data Science
Making Predictions with Standardized Coefficients – 365 Data Science
Multiple Linear Regression with sklearn – 365 Data Science
Simple Linear Regression with sklearn – 365 Data Science
Simple Linear Regression with sklearn – Summary Table – 365 Data Science
Training and Testing – 365 Data Science
Underfitting and Overfitting – 365 Data Science
What is sklearn- – 365 Data Science

4. Linear Regression – Practical Example:
Practical Example (Part 1) – 365 Data Science
Practical Example (Part 2) – 365 Data Science
Practical Example (Part 3) – 365 Data Science
Practical Example (Part 4) – 365 Data Science
Practical Example (Part 5) – 365 Data Science

5. Logistic Regression:
A Simple Example in Python – 365 Data Science
An Invaluable Coding Tip – 365 Data Science
Binary Predictors in a Logistic Regression – 365 Data Science
Building a Logistic Regression – 365 Data Science
Calculating the Accuracy of the Model – 365 Data Science
Introduction to Cluster Analysis – 365 Data Science
Introduction to Logistic Regression – 365 Data Science
Logistic vs Logit Function – 365 Data Science
Underfitting and Overfitting – 365 Data Science
Understanding Logistic Regression Tables – 365 Data Science
What do the Odds Actually Mean – 365 Data Science

6. Cluster Analysis (Basics and Prerequisites):
Difference between Classification and Clustering – 365 Data Science
Introduction to Cluster Analysis – 365 Data Science
Math Prerequisites – 365 Data Science
Some Examples of Clusters – 365 Data Science

7. K-Means Clustering:
A Simple Example of Clustering – 365 Data Science
Clustering Categorical Data – 365 Data Science
How is Clustering Useful- – 365 Data Science
How to Choose the Number of Clusters – 365 Data Science
K-Means Clustering – 365 Data Science
Market Segmentation with Cluster Analysis (Part 1) – 365 Data Science
Market Segmentation with Cluster Analysis (Part 2) – 365 Data Science
Pros and Cons of K-Means Clustering – 365 Data Science
Relationship between Clustering and Regression – 365 Data Science
To Standardize or to not Standardize – 365 Data Science

8. Other Types of Clustering:
Dendrogram – 365 Data Science
Heatmaps – 365 Data Science
Types of Clustering – 365 Data Science