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
Feb 1, Mon Introduction to 85-309
Feb 3, Wed Lesson 1.1: Data–where it comes from and why it matters
Feb 5, Fri Lab 1: Intro to R and RStudio
Feb 8, Mon Lesson 1.2: Exploratory Data Analysis
  Exploring class data
Feb 10, Wed Quiz 1 in class
  Lesson 1.3: More Exploratory Data Analysis
Feb 12, Fri Lab 2: Intro to Data
Feb 15, Mon Lesson 1.4 Using the Tidyverse
Due dates
  • CAOS PreTest: Feb 5 11:55PM
  • Lab 1: Feb 12, Fri, at 2:10pm
  • Lab 2: Feb 19, Fri, at 2:10pm
  • Problem Set 1: Feb 17, Wed, at 2:10pm
    • 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 17, Wed Quiz 2 in class
  Lesson 2.1: Randomization and Sampling
  Bias in hiring
Feb 19, Fri Lab 3: Intro to the Tidyverse and Functions
Feb 22, Mon Lesson 2.2: Hypothesis Testing
  Cardiac arrests
Feb 24, Wed Quiz 3 in class
  Lesson 2.3: The Central Limit Theorem
Feb 26, Fri Lab 4: Testing the Hot Hands Hypothesis
  Curry simulation notes
Mar 1, Mon Lesson 2.4: The Normal Distribution and more on the Central Limit Theorem
  QQ Plots
Mar 3, Wed Quiz 4 in class
  Lesson 2.5: Confidence Intervals
Mar 5, Fri Lab 5: Sampling Distributions
Due dates
  • Lab 3: Feb 26, Fri, at 2:10pm
  • Lab 4: Mar 5, Fri, at 2:10pm
  • Lab 5: Mar 12, Fri, at 2:10pm
  • PS 2: Mar 8, Mon, at 2:10 PM
    • 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
Mar 8, Mon Lesson 3.1: Inference for a Single Proportion
  The Central Limit Theorem for Proportions
Mar 10, Wed Quiz 5 in class
  Lesson 3.2: Inference for the difference of two proportions
Mar 12, Fri Lab 6: Confidence Intervals
Mar 15, Mon Lesson 3.3: The t-distribution
  One sample t-tests
Mar 17, Wed Quiz 6 in class
  Lesson 3.4: Difference of two means
Mar 19, Fri Mid Semester Break - No class
Mar 22, Mon Lesson 3.4: Difference of two means
Mar 24, Wed Quiz 7 in class
  Lesson 3.5: Difference of many means
  Dictator game
Mar 26, Fri Lab 7: Inference for Categorical Data
Due dates
  • Lab 6: Mar 26, Fri, at 2:10 PM
  • Lab 7: Apr 2, Fri, at 2:10 PM
  • PS 3: Mar 29, Mon, at 2:10 PM
    • 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 29, Mon Lesson 4.1: Linear Regression
Mar 31, Wed Quiz 8 in class
  Lesson 4.2: Residuals and Least Squares
Apr 2, Fri Lab 8: Inference for Numerical Data
Apr 5, Mon Break Day - No class
Apr 7, Wed Lesson 4.3: Inference for linear regression
Apr 9, Fri Lab 9: Introduction to Linear Regression
Due dates
  • Lab 8: Apr 9, Fri, at 2:10 PM
  • Lab 9: Apr 23, Fri, at 2:10 PM
  • PS 4: Apr 12 Mon, at 2:10 PM
    • 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 12, Mon Lesson 5.1: Multiple Regression
April 14, Wed Quiz 9 in class
  Lesson 5.2: Model Selection
  Stepwise Regression
April 16, Fri Carnival - No Class
April 19, Mon Lesson 5.3 Transformations
  Log Tranforming Data
April 21, Wed Quiz 10 in class
  Lesson 5.4 The Generalized Linear Model
  The Donner Party
April 23, Fri Lab 10: Multiple Linear Regression
April 26, Mon Lesson 5.5 Mixed effects models
April 28, Wed Quiz 11 in class
  Review
April 30, Fri Lab 11: Choosing the right model
Due dates
  • Lab 10: Apr 30, Fri, at 2:10 PM
  • Lab 11: May 7, Fri, at 2:10 PM
  • PS 4: May 3 Mon, at 2:10 PM
    • 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
May 3, Mon What is Probability?
May 5, Wed Bayesian inference
May 7, Fri Language learning as Bayesian inference
Due dates