Schedule


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

Unit 1 - Introduction to data

Resources
Learning objectives: L0 1 Textbook: Chp 1 Videos: Videos 1
Class / lab
Jan 13, Mon Introduction to 85-309
Jan 15, Wed Lesson 1.1: Data–where it comes from and why it matters
Jan 17, Fri Lab 1: Intro to R and RStudio
Jan 20, Mon Martin Luther King Day – no class
Jan 22, Wed Quiz 1 in class
  Lesson 1.2: Exploratory Data Analysis
Jan 24, Fri Lab 2: Intro to Data
Jan 27, Mon Lesson 1.3: More Exploratory Data Analysis
  Exploring class data
Due dates
  • CAOS PreTest: Jan 18 11:55PM
  • Lab 1: Jan 24, Fri, at 1:30pm
  • Lab 2: Jan 31, Fri, at 1:30pm
  • Problem Set 1: Jan 29, Wed, at 1:30pm
    • 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
Learning objectives: LO 2 Textbook: Chp 2
Class / lab
Jan 29, Wed Quiz 2 in class
  Lesson 2.1: Randomization and Sampling
  Discrimination in Hiring
Jan 31, Fri Lab 3: Intro to the Tidyverse and Functions
Feb 3, Mon Lesson 2.2: Hypothesis Testing
  Cardiac Arrest
Feb 5, Wed Quiz 3 in class
  Lesson 2.3: The Central Limit Theorem
Feb 7, Fri Lab 4: Testing the Hot Hands Hypothesis
Feb 10, Mon Lesson 2.4: The Normal Distribution and more on the Central Limit Theorem
  QQ Plots
Feb 12, Wed Quiz 4 in class
  Lesson 2.5: Confidence Intervals
Feb 14, Fri Lab 5: Sampling Distributions
  Eberly Center Early Course Feedback Form
Due dates
  • Lab 3: Feb 7, Fri, at 1:30pm
  • Lab 4: Feb 14, Fri, at 1:30pm
  • Lab 5: Feb 21, Fri, at 1:30pm
  • PS 2: Feb 17, Mon, at 1:30 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
Learning objectives: L0 3 Textbook: Chp 3 and Chp 4
Class / lab
Feb 17, Mon Lesson 3.1: Inference for a Single Proportion
  Scientific Literacy
Feb 19, Wed Quiz 5 in class
  Lesson 3.2: Inference for the difference of two proportions
Feb 21, Fri Lab 6: Confidence Intervals
Feb 24, Mon Lesson 3.3: The t-distribution
  t-tests
Feb 26, Wed Quiz 6 in class
  Lesson 3.4: Difference of two means
Feb 28, Fri Lab 7: Inference for Categorical Data
Mar 2, Mon 3.5: Difference of many means
  Dictator game
Mar 4, Wed Quiz 7 in class
  Mid-semester Review
  ANOVA example
Due dates
  • Lab 6: Feb 28, Fri, at 1:30 PM
  • Lab 7: Mar 6, Fri, at 1:30 PM
  • PS 3: Mar 23, Mon, at 1:30 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

Spring Break

Mar 6, Fri Mid-Semester Break - no class
Mar 9, Mon Spring Break - no class
Mar 11, Wed Spring Break - no class
Mar 13, Fri Spring Break - no class

Unit 4 - Simple Regression

Resources
Learning objectives: LO 4 Textbook: Chp 5
Class / lab
Mar 16, Mon Class Canceled
Mar 18, Wed Lesson 4.1: Linear Regression
  Correlation
Mar 20, Fri Lab 8: Inference for Numerical Data
Mar 23, Mon Lesson 4.2: Residuals and Least Squares
Mar 25, Wed Quiz 8 in class
  Lesson 4.3: Inference for linear regression
Mar 27, Fri Lab 9: Introduction to Linear Regression
Due dates
  • Lab 8: Mar 27, Fri, at 1:30 PM
  • Lab 9: Apr 3, Fri, at 1:30 PM
  • PS 4: Apr 1 Wed, at 1:30 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
Learning objectives: LO 5 Textbook: Chp 6
Class / lab
Mar 30, Mon Lesson 5.1: Multiple Regression
April 1, Wed Quiz 9 in class
April 3, Fri Lab 10: Multiple Linear Regression
April 6, Mon Lesson 5.2: Model Selection
  Stepwise Regression
April 8, Wed Quiz 10 in class
  Lesson 5.3 Transformations
  Transformations
April 10, Fri Lab 11: Choosing the right model
April 13, Mon Lesson 5.4 The Generalized Linear Model
  Logistic Regression
April 15, Wed Quiz 11 in class
  Lesson 5.5 Hierarchical Models and Mixed effects
April 17, Fri Optional Review
Due dates
  • Lab 10: Apr 10, Fri, at 1:30 PM
  • Lab 11: Apr 24, Fri, at 1:30 PM
  • PS 4: Apr 20 Mon, at 1:30 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
Learning objectives: LO 6
Class / lab
April 20, Mon What is Probability?  
April 22, Wed Bayesian inference  
April 24, Fri Introducing the final project  
April 27, Mon Graphical models  
  Bayesian linear regression  
April 29, Wed [Language learning as Bayesian inference (post/slides/bayesian_learning.html)
May 1, Fri Work on final projects–optional  
Due dates