Schedule


Jump to:     Unit 1         Unit 2         Unit 3         Unit 4         Unit 5         Unit 6         Unit 7    

Wrap up

In class
Apr 20, Wed Lesson 8.1: Bayesian vs. frequentist inference    
  App Ex 8.1: Bayesian vs. frequentist inference
Apr 21, Thu Lab: Poster session
Apr 25, Mon Lesson 8.2 - Final exam review    
Apr 26, Tue No discussion section - Office hours in Old Chem 213
Apr 27, Wed Review / synthesis

Final Exam - May 5, Thu (7-10pm)

  • Cumulative, covers Units 1 - 7
  • Practice problems for Final: handout + solutions


Unit 1 - Introduction to data

Resources
Videos: Videos for Unit 1 Learning objectives: LO 1 Textbook: Chp 1
Class / lab
Jan 13, Wed Introduction to Sta 101    
  Lesson 1.1: Data Collection, observational studies & experiments        
Jan 14, Thu Lab 0: Introduction
Jan 18, Mon Martin Luther King, Jr. day - no class
Jan 20, Wed RA 1 in class (not graded)
  Lesson 1.2: Exploratory data analysis    
  App Ex 1.1: Distributions of numerical variables
Jan 21, Thu Lab 1: Intro to R and RStudio
Jan 25, Mon Lesson 1.3: More exploratory data analysis        
  App Ex 1.2: Histogram to boxplot
  App Ex 1.3: Scientific studies in the press
Jan 27, Wed Lesson 1.4: Introduction to statistical inference        
  App Ex 1.4: Randomization testing
Jan 28, Thu Lab 2: Introduction to data
Due dates
  • Lab 1: Jan 28, Thu, in lab
  • PS 1: Jan 29, Fri, at 11:55pm
    • 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. Click here to submit the problem set.
    • Graded questions: 1.2, 1.6, 1.12, 1.20, 1.32, 1.34, 1.44, 1.52, 1.56, 1.60, 1.64, 1.66, 1.68, 1.70 (see errata)
    • Practice questions:
      • Part 1 – Designing studies: 1.1, 1.3, 1.11, 1.13, 1.17, 1.19, 1.25, 1.27, 1.31
      • Part 2 – Exploratory data analysis: 1.39, 1.41, 1.45, 1.49, 1.51, 1.55, 1.59, 1.63, 1.65
      • Part 3 – Introduction to inference via simulation: 1.67, 1.69
  • PA 1: Jan 31, Sun, at midnight
    • Take between Jan 27, Wed and Jan 31, Sun.
    • You have 30 minutes to complete. Only one attempt allowed. Click here to take the performance assessment.

Unit 2 - Probability and distributions

Resources
Videos: Videos for Unit 2 Learning objectives: LO 2 Textbook: Chp 2 - Sec 2.1, 2.2 + Chp 3 - Sec 3.1, 3.2, 3.4
In class / lab
Feb 1, Mon RA 2 in class
  Lesson 2.1: Probability and conditional probability        
  App Ex 2.1: Voting probabilities of college students
Feb 2, Tue DS 3: Probability    
Feb 3, Wed Lesson 2.2: Bayes’ theorem and Bayesian inference        
  App Ex 2.2: Bayesian drug testing
Feb 4, Thu Lab 3: Probability
Feb 8, Mon Lesson 2.3: Normal and binomial distribution        
  App Ex 2.3: Hourly rates of manufacturing workers    
Feb 2, Tue DS 4: Distributions    
Due dates
  • Lab 2: Feb 4, Thu, in lab
  • PS 2: Feb 12, Fri, at 11:55pm
    • End of chapter exercises from Chp 2 and Chp 3. 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. Click here to submit the problem set.
    • Graded questions:
      • Chp 2: 2.2, 2.8, 2.18, 2.20, 2.22, 2.26
      • Chp 3: 3.4, 3.6, 3.12, 3.18, 3.26, 3.30, 3.36
    • Practice questions:
      • Part 1 – Defining probability: 2.1, 2.3, 2.5, 2.7, 2.13
      • Part 2 – Conditional probability: 2.15, 2.19, 2.21, 2.23
      • Part 3 – Normal distribution: 3.3, 3.5, 3.9, 3.11, 3.17
      • Part 4 – Binomial distribution: 3.25, 3.27, 3.29, 3.33
  • PA 2: Feb 14, Sun, at midnight
    • Take between Feb 10, Wed and Feb 14, Sun.
    • You have 30 minutes to complete. Only one attempt allowed. Click here to take the performance assessment.

Unit 3 - Foundations for inference

Resources
Videos: Videos for Unit 3 Learning objectives: LO 3 Textbook: Chp 4
In class / lab
Feb 10, Wed RA 3 in class
  Lesson 3.1: Variability in estimates and CLT        
Feb 11, Thu Lab 4: Sampling distributions
Feb 15, Mon No class - snow day
Feb 16, Tue DS 5: Foundations for inference    
Feb 17, Wed Lesson 3.2: Confidence intervals        
  App Ex 3.1: Relaxing after work
Feb 18, Thu Lab 5: Confidence intervals
Feb 21, Sun 2 - 3:15pm at LSRC B 101 - MT 1 Review        
Feb 22, Mon Lesson 3.3: Hypothesis tests        
  App Ex 3.2: Grade inflation
Feb 23, Tue DS 6: MT1 Review    
Due dates
  • Lab 3: Feb 11, Thu, in lab
  • Lab 4: Feb 18, Thu
  • PS 3: Feb 19, Fri, at 11:55pm
    • End of chapter exercises from Chp 4. 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. Click here to submit the problem set.
    • Graded questions: 4.2, 4.8, 4.10, 4.12, 4.16, 4.20, 4.24, 4.28, 4.30, 4.32, 4.36, 4.38, 4.44, 4.48
    • Practice questions:
      • Part 1 – Variability in estimates and the Central Limit Theorem: 4.1, 4.3, 4.5, 4.33, 4. 35, 4.37, 4.41
      • Part 2 – Confidence intervals: 4.9, 4.11, 4.13, 4.15
      • Part 3 – Hypothesis tests: 4.17, 4.19, 4.23, 4.25, 4.27
      • Part 4 – Inference for other estimators: 4.43, 4.45
      • Part 5 - Decision errors, significance, and confidence: 4.29, 4.31, 4.47
  • PA 3: Feb 21, Sun, at midnight
    • Take between Feb 17, Wed and Feb 21, Sun.
    • You have 30 minutes to complete. Only one attempt allowed. Click here to take the performance assessment.

Midterm 1 - Feb 24, Wed (in class)


Unit 4 - Inference for numerical variables

Resources
Videos: Videos for Unit 4 Learning objectives: LO 4 Textbook: Chp 5
In class / lab
Feb 25, Thu Lab 6: Inference for numerical data
Feb 29, Mon Lesson 4.1: Inference with t        
  App Ex 4.1: Comparing means
  RA 4 in class
Mar 1, Tue DS 7: t inference    
Mar 2, Wed Lesson 4.2: Bootstrap intervals        
  App Ex 4.2: Bootstrap intervals
Mar 3, Thur Lab: Work on project proposal
Mar 7, Mon Lesson 4.3: Power        
  App Ex 4.3: Power
Mar 8, Tue DS 8: DS 8: Paired vs. independent groups + Power    
Mar 9, Wed Lesson 4.4: ANOVA        
  App Ex 4.4: ANOVA    
Mar 10, Thu Lab 7: (More) inference for numerical data
Due dates
  • Lab 5: Feb 25, Thu
  • Lab 6: Mar 3, Thur
  • Project Proposal: Mar 4, Fri at 5pm
  • PS 4: Mar 11, Fri
    • End of chapter exercises from Chp 5. 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. Click here to submit the problem set.
    • Graded questions: Chp 5: 5.4, 5.6, 5.12, 5.16, 5.20, 5.28, 5.30, 5.34, 5.36, 5.38, 5.44, 5.46, 5.48, 5.50
    • Practice questions:
      • Part 1 – t-inference: 5.1, 5.3, 5.5, 5.13, 5.17, 5.19, 5.21, 5.23, 5.27, 5.31, 5.35, 5.37
      • Part 2 – Power: 5.39
      • Part 3 – Comparing three or more means (ANOVA): 5.41, 5.43, 5.45, 5.47, 5.49, 5.51
      • Part 4 – Simulation based inference for means
  • PA 4: Mar 13, Sun
    • Take between Mar 9, Wed and Mar 13, Sun.
    • You have 30 minutes to complete. Only one attempt allowed. Click here to take the performance assessment.

:palm_tree::sunny: Spring Break :sunny::tropical_drink:

  • Mar 12, Sat - Mar 20, Sun
  • Prepare for Unit 5

Unit 5 - Inference for categorical variables

Resources
Videos: Videos for Unit 5 Learning objectives: LO 5 Textbook: Chp 6
In class / lab
Mar 21, Mon RA 5 in class
  Lesson 5.1: Inference for a single proportion        
  App Ex 5.1: Inference for a single proportion
Mar 22, Tue DS 9: One and two proportions    
Mar 23, Wed Lesson 5.2: Inference for comparing two proportions        
  App Ex 5.2: Inference for comparing two proportions
Mar 24, Thu Lab 8: Inference for categorical data
Mar 28, Mon Lesson 5.3: Chi-square tests        
  App Ex 5.3: Chi-square tests
Mar 29, Tue DS 10: MT2 Review    
Due dates
  • Lab 7: Mar 24, Thu
  • PS 5: Apr 1, Fri
    • End of chapter exercises from Chp 6. 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. Click here to submit the problem set.
    • Graded questions: 6.2, 6.4, 6.8, 6.12, 6.16, 6.20, 6.26, 6.28, 6.30, 6.36, 6.44, 6.48, 6.52, 6.54, 6.56
    • Practice questions:
      • Part 1 – Single proportion: 6.1, 6.3, 6.5, 6.9, 6.11, 6.13, 6.15, 6.19, 6.21
      • Part 2 – Comparing two proportions: 6.23, 6.25, 6.27, 6.29, 6.31, 6.33, 6.35
      • Part 3 – Inference for proportions via simulation: 6.51, 6.53, 6.55
      • Part 4 – Comparing three or more proportions (Chi-square): 6.39, 6.41, 6.43, 6.45, 6.47
  • PA 5: Mar 29, Tue (Note day change to allow for reviewing answers before the midterm)
    • Take between Mar 23, Wed and Mar 29, Tue.
    • You have 30 minutes to complete. Only one attempt allowed. Click here to take the performance assessment.

Midterm 2 - Mar 30, Wed (in class)

Unit 6 - Introduction to linear regression

Resources
Videos: Videos for Unit 6 Learning objectives: LO 6 Textbook: Chp 7
In class / lab
Mar 31, Thu Lab 9: Introduction to linear regression
Apr 4, Mon RA 6 in class
  Lesson 6.1: Introduction to regression        
  App Ex 6.1: Linear models
Apr 5, Tue DS 11: Linear regression    
Apr 6, Wed Lesson 6.2: Outliers and inference for regression        
  App Ex 6.2: Linear regression
Due dates
  • Lab 8: Mar 31, Thu
  • PS 6: Apr 8, Fri
    • End of chapter exercises from Chp 7. 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. Click here to submit the problem set.
    • Graded questions: 7.2, 7.6, 7.8, 7.10, 7.12, 7.14, 7.16, 7.18, 7.20, 7.26, 7.28, 7.30, 7.34, 7.36, 7.42, 7.44
    • Practice questions:
      • Part 1 – Relationship between two numerical variables: 7.1, 7.3, 7.7, 7.9, 7.11, 7.13, 7.15
      • Part 2 – Linear regression with a single predictor: 7.17, 7.19, 7.25, 7.27, 7.29, 7.31, 7.33
      • Part 3 – Inference for linear regression: 7.25, 7.37, 7.39, 7.41, 7.43
  • PA 6: Apr 10, Sun
    • Take between Apr 6, Wed and Apr 10, Sun.
    • You have 30 minutes to complete. Only one attempt allowed. Click here to take the performance assessment.

Unit 7 - Multiple linear regression

Resources
Videos: Videos for Unit 7 Learning objectives: LO 7 Textbook: Chp 8 - Sec 8.1, 8.2, 8.3
In class / lab
Apr 7, Thu Lab 10: Multiple linear regression
Apr 11, Mon RA 7 in class
  Lesson 7.1: Introduction to multiple linear regression        
Apr 12, Tue DS 12: Multiple regression    
Apr 13, Wed Lesson 7.2: Model selection & diagnostics for MLR        
  App Ex 7.1: Multiple linear regression
Apr 14, Thu Work on project
Apr 18, Mon Lesson 7.3: Transformations and case study        
  App Ex 7.2: Interpreting models with a transformed response
Apr 19, Tue DS 13: Project help - In Old Chem 213
Due dates
  • Lab 9: Apr 7, Thu
  • Lab 10: Apr 14, Thu
  • Project poster session: Apr 21, Thu in lab
  • PS 7: Apr 22, Fri
    • End of chapter exercises from Chp 8. 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. Click here to submit the problem set.
    • Graded questions: 8.2, 8.4, 8.6, 8.8, 8.10, 8.12, 8.14
    • Practice questions:
      • Part 1 – Regression with multiple predictors: 8.1, 8.3
      • Part 2 – Inference for MLR: 8.5
      • Part 3 – Model selection: 8.7, 8.9, 8.11
      • Part 4 – Model diagnostics: 8.13
  • PA 7: Apr 24, Sun
    • Take between Apr 20, Mon and Apr 24, Sun.
    • You have 30 minutes to complete. Only one attempt allowed. Click here to take the performance assessment.