From the course: Complete Guide to AI and Data Science for SQL Developers: From Beginner to Advanced
Unlock the full course today
Join today to access over 23,400 courses taught by industry experts.
Creating the linear regression model and model summary: Part 2 - SQL Tutorial
From the course: Complete Guide to AI and Data Science for SQL Developers: From Beginner to Advanced
Creating the linear regression model and model summary: Part 2
- [Instructor] Okay, before we move on, here is a good spot to pause the video and take a breather. There are a lot of numbers to take in, so don't be afraid to rewind and review what has been discussed thus far before we move on. Okay, now let's talk about the coefficients. Think of them as the key players in your model. Among them, we have const, which represents the intercept or starting point of our predictions. Similar to where a journey begins on a map, it's like the initial reference point for making predictions. It tells you where your predictions begin on the scale. Now, each predictor variable, like crime rate or river boundary, gets a coefficient. Think of the predictor variables such as crime rate or river boundary as key players in your model. Each one has its own coefficient, which is like a scorecard telling you both the direction, up or down, and the strength of their influence on home value log. It's…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Contents
-
-
-
-
-
-
-
-
-
-
-
-
-
(Locked)
Creating the linear regression model and model summary: Part 19m 33s
-
(Locked)
Creating the linear regression model and model summary: Part 27m 16s
-
(Locked)
Creating the linear regression model and model summary: Part 35m 33s
-
(Locked)
Dropping insignificant variables and re-creating the model7m 57s
-
(Locked)
Checking assumptions for linear regression3m 18s
-
(Locked)
Assumption 1: Checking for mean residuals2m 47s
-
(Locked)
Assumption 2: Checking homoscedasticity3m 13s
-
(Locked)
Assumption 3: Checking linearity2m 12s
-
(Locked)
Assumption 4: Checking normality of error terms3m 24s
-
(Locked)
Q-Q plot for checking the normality of error terms3m 14s
-
(Locked)
Model performance comparison on train and test data6m 7s
-
(Locked)
Applying cross-validation and evaluation4m 40s
-
(Locked)
Challenge: Model building48s
-
(Locked)
Solution: Model building1m 16s
-
(Locked)
-
-
-