Stat 312 - Intermediary Statistics

Course Syllabus
Data sets used in the course
List of R commands used in the course

114 videos
Total Video Time 11:14:49
Average video Length 5:55

Welcome to Class
5 videos
Total Time 28:38

Program used in Stat Methods (3:10)
How methods is different from intro (9:46)
Data Science: Construction Analogy (3:28)
Data Science: Love Analogy (4:39)

Chapter 2 : Prerequisites
11 videos
Total Time 44:32

5 Lies we tell intro stat students (4:52)
Basic Prerequisites (4:50)
The basics of testing two groups against each other (5:27)
Basic Regression Vocabulary (4:28)
Testing the Slope (3:30)
Three Regression Pictures (4:08)
Homework videos (0:37)
Homework Explain Statistics (3:46)
Homework Bad Memo (2:24)
Homework Bad Explanations (4:31)
Homework Fish Oil and SDA (5:59)

Chapter 3 : Learning R
25 videos
Total Time 01:52:28

Downloading R and R Studio (4:35)
How R works (4:25)
I'm trying to update links as fast as I can (2:42)
Putting data in a box (5:26)
Problems reading in data (5:25)
t-tests in R (4:49)
Square Bracket Notation (5:13)
Doing plots in R (4:22)
Running Regression in R (6:07)
Looking at residuals (5:40)
Basic Regression example (4:29)
Using hats to add lines (3:54)
Prediction accuracy (2:34)
Confidence interval of the slope (3:40)
How to handle outlier errors (7:02)
When numerical data is incorrectly read in as categorical (5:31)
Reading in data by hand (4:39)
Making your own residual plots (4:10)
Regression on Age vs Height (5:08)
Report for Age vs Height (4:21)
Homework first regression (1:27)
Homework Bears Data (3:41)
Homework Captain Bucktooth (4:49)
Homework Apiologist (3:47)
Advanced: How to make up your own data (4:32)

Chapter 4 : Categorical Variables
24 videos
Total Time 01:55:07

Quick Overview - Categorical Regression (4:09)
Broad Idea for categorical Regression (5:23)
Testing two categories (5:13)
Simple case prediction equation (2:16)
Two categories with a numerical variable (5:00)
Reading the output with categorical regression (5:29)
Why use numerical*categorical (5:25)
Regression with more than 2 categories (4:54)
Making six prediction equations (6:52)
Drawing the multiple lines (4:36)
Making only one prediction hat equation (5:37)
Reporting on multiple categories (4:41)
The data set with errors (5:06)
Making hat lines for the error data (7:19)
The errors data report (4:49)
What if there are two categorical variables (5:41)
Candle Prediction Equations (7:42)
Candle Report (3:13)
Homework Categorical Regression (2:00)
Homework Drug Reactions (4:08)
Homework Deliciousness (2:01)
Homework Apple Spiders (3:54)
Advanced: Letting R keep track of coefficients (4:21)
Advanced: Changing the order of the levels in R (5:18)

Chapter 5 : Curved Regression
13 videos
Total Time 01:40:24

The idea of curved regression (4:47)
Six rules for picking the right curve (4:06)
Debates about the best model (5:41)
Update on redoing videos (1:20)
Running curved regression in R (10:18)
Testing different curves on the processor data (14:10)
Proper ways to explain the curve (5:35)
Curved regression with categories (17:14)
Explaining Malware Data (7:51)
Example Rain Water Level (11:37)
Example Lung Volume (14:57)
Homework Server Times (0:57)
Homework Quadratics (1:51)

Chapter 6 : Numerical Interactions
9 videos
Total Time 01:30:20

The idea of numerical interactions (6:18)
Seven guides to spot an interaction (5:12)
Start the brain data (11:46)
Explaining a numerical interaction (6:54)
Graphing the numerical interaction (13:17)
Example soccer data (20:23)
Example Bees (8:22)
Example Fabric (15:48)
Homework Sleep Levels (2:20)

Chapter 7 : Multicollinearity
8 videos
Total Time 01:10:27

The idea of Multicollinearity (6:54)
An anology for multicollinearity (4:15)
Looking at model selection (7:25)
A highly collinear data set (12:05)
Other methods of model selection (5:18)
Example Rhode Island (27:23)
Homework Foxbooks Model (4:22)
Homework Foxbooks Report (2:45)

Chapter 8 : Final Project
19 videos
Total Time 01:52:53

Steps for analyzing a complex data set (5:39)
Looking at the final project (5:15)
Questions you can/can't ask about the Final Project (5:34)
The Final project Rubric (8:25)
Reading in the final project (5:45)
Looking at plots of the data (5:54)
Starting to look at residuals (5:28)
Finding the best model I can (5:48)
Finalizing the model (5:24)
Making Enemyhat (6:31)
Explain the linear, categorical, cubed, and one interaction (7:08)
Graphing the interaction (6:32)
Starting the report (5:31)
Explaining the simpler terms (5:06)
Explaining the interactions (5:38)
Finishing the report (7:20)
Making a second model (5:22)
Making the graphs (5:20)
Second report (5:13)