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Introduction to Regression Analysis
Introduction
Introduction to Regression Analysis (2:39)
Exercise: Meet Your Classmates and Instructor
Course Resources
Multilinear Regression
Game Plan for Multilinear Regression (1:22)
CASE STUDY Briefing - Pricing Diamonds (1:53)
Linear Regression (5:12)
Python - Libraries and Data (2:27)
Python - Exploratory Data Analysis (3:18)
Python - Linear Regression (2:16)
Regression Statistics (4:23)
Python - Plotting Regression Curve (7:07)
Dummy Variable (Trap) (4:00)
Python - Linear Regression with Dummy Variables (7:15)
EXERCISE: Create Function that Reads the Regression Coefficients (9:35)
CASE STUDY - Linearity Bias - We Will All Be Obese! Wait What? (4:01)
Multilinear Regression (1:47)
Python - Categorical Variables (5:39)
Under and Overfitting (3:27)
Training and Test Set (2:34)
Python - Multilinear Regression (3:44)
Assessing Regression Models (6:07)
Python - Assessing Regression Model (3:47)
PRACTICE TEST - Multilinear Regression
PRACTICE TEST - Multilinear Regression - Explanations
CASE STUDY - Dangers of Regression Analysis (2:51)
Multilinear Regression Wrap Up (2:02)
Capstone Project - Multilinear Regression
Captone Project - Understanding Sales Drivers (1:19)
Python - Solutions - Step 1 (7:20)
Python - Solutions - Step 2-4 (4:29)
Python - Solutions - Step 5-6 (3:47)
Logistic Regression
Game Plan for Logistic Regression (1:39)
CASE STUDY Briefing - Spam Emails (1:25)
Logistic Regression (3:28)
Python - Preparing Script and Loading Data (3:31)
Python - Summary Statistics (3:44)
Python - Histograms and Outlier Detection (5:36)
Python - Correlation Matrix (3:26)
Python - Logistic Regression Preparation (3:59)
How to Read Logistic Regression Coefficients (2:11)
Python - Logistic Regression (2:17)
Python - Build a Coefficient Function with ChatGPT (9:06)
Python - Predictions (3:19)
Confusion Matrix and Model Assessment (6:24)
Python - Confusion Matrix and Classification Report (5:34)
Python - Assessing Classification Models with ChatGPT (5:30)
PRACTICE TEST - Logistic Regression
PRACTICE TEST - Logistic Regression - Explanations
Section Wrap Up - Logistic Regression (3:15)
Capstone Project - Surviving Titanic - Logistic Regression with ChatGPT
Capstone Project - Surviving Titanic (1:02)
Python - Libraries and Data (8:20)
Python - Removing Outliers and EDA (6:33)
Python - Logistic Regression Model and Assessment (6:06)
Cox Proportional Hazard Regression
Game Plan for Cox Proportional Hazard Regression (2:14)
Introduction to Survival Analysis (7:47)
CASE STUDY - Briefing (1:47)
Python - Libraries and Data (5:09)
Kaplan-Meier Estimator (4:35)
Python - Kaplan Meier Estimator (4:22)
Python - Calculating for a Specific Event (2:46)
Python - Plotting Kaplan-Meier and Cumulated Curves (3:51)
Censoring (3:45)
Log Rank Test (2:56)
Python - Kaplan-Meier Estimator per Gender and Visualization (5:50)
Python - Log Rank Test (6:33)
Cox Proportional Hazard Regression (4:51)
Python - Prepare Data for CPH Model (3:11)
Python - Cox Proportional Hazard Regression (9:36)
Python - Visualize Results (2:12)
Assessing Cox Proportional Hazard Models (5:18)
Python - Assessing the CPH Model (8:37)
Python - Predicting Specific Instances (3:38)
PRACTICE TEST - Cox Regression
PRACTICE TEST - Cox Regression - Explanations
Cox Proportional Hazard Regression Wrap Up (3:14)
Capstone Project - Surviving the App-pocalypse - Cox Regression with ChatGPT
Capstone Project - Will Your App Make it? (1:23)
Python - Libraries and Data (7:10)
Python - Data Cleaning (19:10)
Python - Dependent Variable (8:26)
Python - Kaplan-Meier Estimator (4:30)
Python - Cox Model (10:00)
Logarithmic Regression
Game Plan for Logarithmic Regression (4:19)
Python - Logarithmic Regression Setup (4:05)
Python - Data Prep and Visualization (6:11)
Python - Normal Linear Regression (4:42)
Python - Plotting Normal Linear Regression (3:59)
Python - Linear - Log Regression (5:55)
Python - Log - Linear Regression (5:42)
Python - Log - Binary (6:36)
Python - Log-Log Regression (3:23)
Where To Go From Here?
Let's Keep Learning Together! (0:51)
Review This Byte!
Dummy Variable (Trap)