• # 85-309 - Statistical Methods and Concepts for Social and Behavioral Sciences

## Schedule

 Jump to: Unit 1 Unit 2 Unit 3 Unit 4         Unit 5        Unit 6

### Unit 1 - Introduction to data

##### Resources
 Textbook: Chp 1 Videos: Videos 1
##### Class / lab
 Jan 19, Wed Introduction to 85-309 Jan 21, Fri Lesson 1.1: Data–where it comes from and why it matters Jan 24, Mon Lesson 1.2: Exploratory Data Analysis Exploring class data Jan 26, Wed Lab 1: Intro to R and RStudio Jan 28, Fri Quiz 1 in class Lesson 1.3: More Exploratory Data Analysis Jan 31, Mon Lesson 1.4 Using the Tidyverse Feb 2, Wed Lab 2: Intro to Data
##### Due dates
• CAOS PreTest: Jan 24 1:25pm
• Lab 1: Feb 2, Wed, at 1:25pm
• Lab 2: Feb 9, Wed, at 1:25pm
• Problem Set 1: Feb 4, Fri, at 1:25pm
• End of chapter exercises from Chp 1. Only turn in answers to graded questions, use the back of the book to check your work on the practice questions. Must show all work to get credit. Submit homework through Canvas.
• Graded questions: 1.2, 1.12, 1.20, 1.30, 1.34, 1.42
• Practice questions:
• Part 1 – Designing studies: 1.1, 1.3, 1.9, 1.11, 1.13, 1.19
• Part 2 – Examining Data: 1.25, 1.27, 1.29, 1.37, 1.39, 1.41, 1.47

### Unit 2 - Foundations for Inference

##### Resources
 Textbook: Chp 2 Videos: Videos 2
##### Class / lab
 Feb 4, Fri Quiz 2 in class Lesson 2.1: Randomization and Sampling Bias in hiring Feb 7, Mon Lesson 2.2: Hypothesis Testing Cardiac arrests Feb 9, Wed Lab 3: Intro to the Tidyverse and Functions Feb 11, Fri Quiz 3 in class Lesson 2.3: The Central Limit Theorem Feb 14, Mon Lesson 2.4: The Normal Distribution and more on the Central Limit Theorem QQ Plots Feb 16, Wed Lab 4: Sampling Distributions Feb 18, Fri Quiz 4 in class Lesson 2.5: Confidence Intervals
##### Due dates
• Lab 3: Feb 16, Wed, at 1:25pm
• Lab 4: Feb 23, Wed, at 1:25pm
• PS 2: Feb 21, Mon, at 11:59pm
• End of chapter exercises from Chp 2. Only turn in answers to graded questions, use the back of the book to check your work on the practice questions. Must show all work to get credit. Submit homework through Canvas.
• Graded questions: 2.4, 2.10, 2.16, 2.18, 2.24, 2.38
• Practice questions:
• Part 1 – Randomization and Hypothesis Testing: 2.1, 2.3, 2.5, 2.9,
• Part 2 – The Normal Distribution: 2.11, 3.13, 2.15, 2.17, 2.19, 2.23, 2.29, 2.31
• Part 3 - Applying the Normal model 2.35, 2.37

### Unit 3 - Inference for Categorical and Numerical Data

##### Resources
 Textbook: Chp 3 and Chp 4 Videos: Videos 3a and 3b
##### Class / lab
 Feb 21, Mon Lesson 3.1: Inference for a Single Proportion The Central Limit Theorem for Proportions Feb 23, Wed Lab 5: Testing the Hot Hands Hypothesis Curry simulation notes Feb 25, Fri Quiz 5 in class Lesson 3.2: Inference for the difference of two proportions Feb 28, Mon Lesson 3.3: The t-distribution One sample t-tests Mar 2, Wed Lab 6: Confidence Intervals Mar 4, Fri Mid Semester Break - No class Mar 7, Mon Spring Break - No class Mar 9, Wed Spring Break - No class Mar 11, Fri Spring Break - No class Mar 14, Mon Lesson 3.4: Difference of two means Mar 16, Wed Lab 7: Inference for Categorical Data Mar 18, Fri Quiz 6 in class Lesson 3.5: Difference of many means Dictator game
##### Due dates
• Lab 5: Mar 2, Wed, at 1:25pm
• Lab 6: Mar 16, Wed, at 1:25pm
• Lab 7: Mar 23, Wed, at 1:25pm
• PS 3: Mar 21, Mon, at 1:25pm
• End of chapter exercises from Chp 3 and 4 of Intro to Stats with Randomization and Simulation. Only turn in answers to graded questions, use the back of the book to check your work on the practice questions. Must show all work to get credit. Submit homework through Canvas.
• Graded questions: 3.2, 3.12, 3.24, 4.6, 4.10, 4.14, 4.18
• Practice questions:
• Part 1 – Inference for a single proportion: 3.1, 3.5, 3.11, 3.19
• Part 2 – Difference of two proportions: 3.23, 3.25, 2.15, 2.17, 2.19, 2.23, 2.29, 2.31
• Part 3 - One-sample means with t-tests 4.3, 4.5
• Part 4 - Paired data: 4.9, 4.13
• Part 5 - Difference of two means: 4.15, 4.17, 4.23

### Unit 4 - Simple Regression

##### Resources
 Textbook: Chp 5 Videos: Videos 4
##### Class / lab
 Mar 21, Mon Lesson 4.1: Linear Regression Mar 23, Wed Lab 8: Inference for Numerical Data Mar 25, Fri Quiz 7 in class Lesson 4.2: Residuals and Least Squares Mar 28, Mon Lesson 4.3: Inference for linear regression Twin IQ correlation Mar 30, Wed Lab 9: Introduction to Linear Regression
##### Due dates
• Lab 8: Mar 30, Wed, at 1:25pm
• Lab 9: Apr 6, Wed, at 1:25pm
• PS 4: Apr 1 Fri, at 1:25pm
• End of chapter exercises from Chp 5 of Intro to Stats with Randomization and Simulation. Only turn in answers to graded questions, use the back of the book to check your work on the practice questions. Must show all work to get credit. Submit homework through Canvas.
• Graded questions: 5.4, 5.10, 5.20, 5.22, 5.28, 5.34
• Practice questions:
• Part 1 – Line fitting, residuals, and correlation: 5.1, 5.3, 5.9
• Part 2 – Fitting a line by least squares: 5.17, 5.19
• Part 3 - Types of Outliers in linear regression: 5.23, 5.25
• Part 4 - Inference for linear regression: 5.27, 5.29, 5.33

### Unit 5 - The General Linear Model

##### Resources
 Textbook: Chp 6 Videos: Videos 5
##### Class / lab
 Apr 1, Fri Quiz 8 in class Lesson 5.1: Multiple Regression April 4, Mon Lesson 5.2: Model Selection Stepwise Regression April 6, Wed Lab 10: Multiple Linear Regression April 8, Fri Carnival - No Class April 11, Mon Lesson 5.3 Transformations Log Tranforming Data April 13, Wed Lab 11: Choosing the right model April 15, Fri Quiz 9 in class Lesson 5.4 The Generalized Linear Model The Donner Party April 18, Mon Lesson 5.5 Mixed effects models April 20, Wed Work on labs
##### Due dates
• Lab 10: Apr 13, Wed, at 1:25pm
• Lab 11: Apr 20, Wed, at 1:25pm
• PS 4: Apr 22 Fri, at 1:25pm
• End of chapter exercises from Chp 6 of Intro to Stats with Randomization and Simulation. Only turn in answers to graded questions, use the back of the book to check your work on the practice questions. Must show all work to get credit. Submit homework through Canvas.
• Graded questions: 6.2, 6.4, 6.8, 6.12, 6.14
• Practice questions:
• Part 1 - Introduction to multiple regression: 6.1, 6.3
• Part 2 - Model selection: 6.7, 6.9
• Part 3 - Checking Model Assumptions: 6.11
• Part 4 - Logistic Regression: 6.13, 6.15

### Unit 6 - Beyond Hypothesis Testing

##### Resources
 Book: Statistical Rethinking Videos: Videos 6
##### Class / lab
 April 22, Fri What is Probability? April 25, Mon Bayesian inference April 27, Wed Work on Labs April 29, Fri Language learning as Bayesian inference