Course schedule
Math 214 meets Mondays and Wednesdays, 14.30-16.25. Monday is theory in 1S-115, Wednesday is computer lab with practice in 1S-108.
We have tutoring available Mondays 4.30-6.30 and Thursdays during club hours. Both of these tutoring sessions will take place in the Mathematics tutoring center, on the second floor of the 1S building.
Textbooks
We will be using Introduction to the Practice of Statistics 9th ed (Amazon: rent $35, buy used $35, buy new $132), The R Software (when accessed through the campus library: $0 ebook, $25 paperback) and ggplot2 (when accessed through the campus library: $0 ebook, $25 paperback).
There is no need to buy either of them with access codes.
In the schedule below, we will abbreviate these as IPS for the Introduction to the Practice of Statistics, RS for The R Software and GG2 for ggplot2.
Stated homework is due at the following theory lecture.
Topic sequence
| Week | Lecture | Date | Topic | Chapter | Homework |
|---|---|---|---|---|---|
| Descriptive Statistics | |||||
| 1 | T 1 (RMd) | Aug 27 | Introduction. Course structure. Graphs and Scatterplots | IPS:1.2, 2.2 | 1.27, 1.29; read the R help file on pie; 2.24, 2.25, 2.26 |
| 1 | L 2 (RMd) | Aug 29 | Intro to RStudio. Our first RMarkdown lab. Loading and graphing data. | RS:1.1-1.3, 3.1, 4.1.1 GG2: 1, 2.1-3 | |
| 2 | T 3 (RMd) | Sep 5 | Summary Statistics: Center and Spread | IPS: 1.3 | 1.65-67 |
| 3 | L 4 | Sep 12 | Summary Statistics in R | RS:11.1, 11.2, 11.4 | |
| 4 | T 5 (RMd) | Sep 17 | Normal distributions | IPS 1.4 | 1.118, 1.124, 1.130, 1.138, 1.140 |
| 5 | T 6 (RMd) | Sep 24 | Correlation and linear fits | IPS 2.3, 2.4, 2.5 | 2.43, 2.44, 2.68-70, 2.94, 2.95 |
| 5 | L 7 | Sep 26 | Correlation and linear fits | RS:11.5.3, 11.6.5.2 GG2:2.5.1 | |
| 6 | T 8 (RMd) | Oct 1 | Two-way tables and causation | IPS 2.6, 2.7 | 2.122, 2.127, 2.128, 2.132 |
| 6 | L 9 | Oct 3 | Data tables. Survey of plot types. | RS:11.3, 11.6.1, 11.6.4, 11.6.5 GG2:2.5 | |
| 7 | L 10 | Oct 10 | Worksession for first report | First Report draft due | |
| 8 | T 11 (RMd) | Oct 15 | Experiment and sampling design | IPS 3.2, 3.3 | 3.27-3.30, 3.54 |
| 8 | L 12 | Oct 17 | Worksession and due date for first report | First Report due | |
| 9 | T 13 (RMd) | Oct 22 | Randomness, Models, Random Variables | IPS:4.1, 4.2, 4.3, 4.4 | 4.4, 4.17, 4.32, 4.59, 4.68, 4.71, 4.82 |
| 9 | L 14 | Oct 24 | Randomness, a survey of distributions, bootstrap | RS: 12.2, 12.6, 12.7 | |
| 10 | T 15 (RMd) | Oct 29 | Sampling distributions: means, counts and proportions | IPS: 5.1, 5.2, 5.3 | 5.7, 5.9, 5.12, 5.28, 5.29, 5.59 |
| 10 | L 16 | Oct 31 | Data processing: subsetting, sampling, summarizing, new variables | RS: chapter 5 | |
| 11 | T 17 (RMd) | Nov 5 | Confidence intervals, Significance, Power and error types | IPS: 6.1-6.4 | 6.12-6.14, 6.54, 6.55, 6.108, 6.109, 6.112 |
| 11 | L 18 | Nov 7 | Confidence intervals in R | RS:13.2 | |
| 12 | T 19 (RMd) | Nov 12 | Means: the t-test | IPS 7.1, 7.2 | 7.15, 7.16, 7.27, 7.79 |
| 12 | L 20 | Nov 14 | T-test in R | RS:13.3.1.1 | |
| 13 | T 21 (RMd) | Nov 19 | Proportions: normal approximations and exact tests, median test | IPS 8.1, 8.2 | 8.27, 8.36, 8.39, 8.56-8.60 |
| 13 | L 22 | Nov 21 | Proportions in R | RS:13.3.1.3 | |
| 14 | T 23 (RMd) | Nov 26 | Two-way tables, goodness of fit | IPS:9.1, 9.2 | 9.25, 9.27, 9.33, 9.34, 9.36 |
| 14 | L 24 | Nov 28 | Chi-squared in R | RS:13.3.2 | |
| 15 | T 25 (RMd) | Dec 3 | Linear regression | IPS:10 | 10.10, 10.11, 10.27 |
| 15 | L 26 | Dec 5 | Linear regression in R | RS:14.1, 14.2 | Second Report draft due |
| 16 | T 27 (RMd) | Dec 10 | ANOVA | IPS:12 | Second Report due |
| 16 | L 28 | Dec 12 | ANOVA in R | RS:15.1 |
Project 1
Pick a dataset from our collection, write a report describing the dataset comprehensively. Your report should contain appropriately chosen graphs, descriptions of observed descriptive statistics of the available variables in the data, and an evaluation of the experiment and sampling design in the data collection.
Due dates
A draft of the first report is due October 11.
First report is due October 18.
As a support for formulating hypotheses, you will read an assigned co-students' report and suggest ideas to them according to the report you read. These ideas are due October 25.
You then need to formulate your hypotheses and register them with me through Blackboard. Your hypotheses are due October 30.
A draft of your report for feedback, as with the first report, should be submitted December 5.
Your report is due through Blackboard timestamped no later than 14.30 on December 10.
Project 2
Based on the report from Project 1, suggest a number of hypotheses for what the dataset could indicate. With the larger sample from the same source made available for Project 2, write a report evaluating your hypotheses and testing them. Report your inferential results both with confidence intervals and with significance levels.
Final Examination
The final will be a computer lab session focusing on using and interpreting results from RStudio. It counts for 5% or your course grade.