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  
Due dates

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