CAAP Statistics
This page is designed to maintain course materials for 2022 Statistics Summer Session.
Schedule - Summer 2022
This course follows regular CAAP schedule.
Related chapter or sections in the textbook will be posted at least one day before the lecture. Other reading materials can be changed anytime before and after the lecture. Please make sure to check this page regularly to see the updates.
Lec 01 - July 5th(Tue)
Course Material
Lec 02 - July 7th(Thu)
Course Material
- Introduction to data based on OpenIntroStat
Readings
- Hihgly recommended Chapter 1 with emphasis on
- Guided Practice 1.4
- Sec 1.2.2.Types of variables
- Sec 1.3.3.Sampling from a population,
- Sec 1.4.1.Principles of experimental design
- Recommended Variable Types in Analytics Using R
- Recommended Video: Experimental Design by PSYCH EXPLAINED
- Advanced Overview of Sampling in Wikipedia
- Advanced Practical Machine Learning Perspective on Sampling
- Advanced Observational Studies Explained by Statistics By Jim
Lec 03 - July 8th(Fri)
Course Material
Readings
- R For Data Science by Hadley Wickham
- Chapter 1. Introduction
Resources
- Video: How to install R and Rstudio by DDS
- An Introduction to R
- Introduction to R by IDRE Statistical Consulting Group
- R Packages by Hadley Wickham and Jenny Bryan
- Stackoverflow - R
Further Practice
- Lab 1 from OpenIntroStat
Lec 04 - July 11th(Mon)
Course Material
- Summarizing Data based on OpenIntroStat
Readings
- Highly Recommended Chapter 2 with emphasis on
- Sec 2.1.1 Scatterplots for paired data
- Sec 2.1.3 Histograms and shape
- Sec 2.1.5 Box plots, quartiles, and the median
- Sec 2.2.1 Contingency tables and bar plots
- Sec 2.3.1 Variability within data
- Recommended Graphical Summaries in Introduction to Data Science
- Recommended Describing Data in Statistics Notebook
Lec 05 - July 12th(Tue)
Course Material
Readings
- R For Data Science by Hadley Wickham
- Chapter 3. Visualization
- Chapter 27. R Markdown ~ Chapter 30. R Markdown workflow
Resources
- Highly recommended R Markdown: The definitive guide by Yihui Xie, J. J. Allaire, Garrett Grolemund
- R Markdown Tutorial by RStudio
- Why R Markdown is Useful from Introduciton to R
- ggplot2: elegant graphics for data analysis
- The tidyverse style guide
Further Practice
- Lab 2 from OpenIntroStat
Lec 06 - July 14th(Thu)
Course Material
- Probability based on OpenIntroStat
Readings
- Highly Recommended Chapter 3 with emphasis on
- Sec 3.1.1 Introductory examples – highly recommend to try solving by hand
- Sec 3.1.5 Probability distributions
- Sec 3.1.7 Independence
- Advanced Sec 3.2.3 Defining conditional probability
- Sec 3.4.1 Expectation
- Sec 3.4.2 Variability in random variables
- Law of Large Numbers in Wikipedia
- Supplementary Disjoint vs Independent from PSU Statistics Online
Lec 07- July 15th(Fri)
Course Material
Readings
- Recommended 20. Simulation from R Programming for Data Science
Further Practice
- Lab 3 from OpenIntroStat
Lec 08 - July 18th(Mon)
Course Material
- Distributions based on OpenIntroStat
Readings
- Highly Recommended Chapter 4 with emphasis on
- Sec 4.1 Normal distribution
- Sec 4.2.1 Bernoulli distribution
- Sec 4.3 Binomial distribution
- Sec 4.5 Poisson distribution(depending on the progress)
- Advanced Univariate Distribution Relationship
- Supplementary Normal Approximation to Binomial
- Continuity Correction in Wikipedia
Lec 09 - July 19th(Tue)
Course Material
Readings
- R For Data Science by Hadley Wickham
- Chapter 7. Exploratory Data Analysis
Further Practice
- Lab 4 from OpenIntroStat
Lec 10 - July 21st(Thu)
Course Material
Quiz 1
Lec 11 - July 22nd(Fri)
Course Material
Readings
- Highly Recommended Chapter 5 with an emphasis on
- Sec 5.1.1 Point estimates and error
- Sec 5.1.2 Understanding the variability of a point estimate
- Central Limit Theorem in Wikipedia
Lec 12 - July 25th(Mon)
Course Material
Readings
- Highly Recommended Chapter 5 with an emphasis on
- Sec 5.2.5 Interpreteing confidence intervals
- Sec 5.3.1 Hypothesis testing framework
- Sec 5.3.3 Decision errors
- Sec 5.3.7 One-sided hypothesis tests
Lec 13 - July 26th(Tue)
Course Material
Readings
- R For Data Science by Hadley Wickham
- Chapter 8. Workflow: projects
Further Practice
Lec 14 - July 28th(Thu)
Course Material
- Inference on Numerical Data based on OpenIntroStat
Readings
- Highly Recommended Chapter 7 with an emphasis on
- Sec 7.1 One-sample means with the t-distribution
- Sec 7.3 Difference of two means
- Advanced Sec 7.5 Comparing many means with ANOVA
Lec 15 - July 29th(Fri)
Course Material
Readings
- R For Data Science by Hadley Wickham
- Chapter 22. Introduction(Modeling)
Further Practice
- Lab7 from OpenIntroStat
Lec 16 - Aug 1st(Mon)
Course Material
Inference on Categorical Data based on OpenIntroStat
- Note: R Session7 is combined to the lecture.
Readings
- Highly Recommended Chapter 6 with an emphasis on
- Sec 6.1 Inference for a single proportion
- Sec 6.2 Difference of two proportions
- Advanced Sec 6.3 Testing for goodness of fit using chi-square
- Advanced Sec 6.4 Testing for independence in two-way tables
Further Practice
- Lab6 from OpenIntroStat
Lec 17 - Aug 2nd(Tue)
Course Material
Introduction to Linear Regression based on OpenIntroStat
Readings
- Highly Recommended Chapter 7 with an emphasis on
- Sec 8.1.1 Fitting a line to data
- Sec 8.1.3 Residuals
- Advanced Sec 8.2 Least squares regression
- Sec 8.4 Inference for linear regression
- Recommended One-variable Linear Regression by MIT OpenCourseWare
- Advanced Ordinary Least Squares in Wikipedia
- Supplementary
Lec 18 - Aug 4th(Thu)
Course Material
Readings
- Highly Recommended Interpret
lmoutput from R by Filipe Rego
Readings
- R For Data Science by Hadley Wickham
Further Practice
- Lab8 from OpenIntroStat
Lec 19 - Aug 5th(Fri)
- Project Guidance 4: Line Fitting and Visualization
- Deadline for submission: Aug 7th 23:59 PM
Lec 20 - Aug 8th(Mon)
Presentation Day
Lec 21 - Aug 9th(Tue)
Course Material
Finals Period
2022 CAAP Final Period is 8/10 - 8/11 The final quiz will be on Aug 11th(Thu).
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