# 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.

## Textbooks

We will be using Introduction to the Practice of Statistics 8th 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 (IPS8e) IPS9e Data
Descriptive Statistics
1 T 1 Aug 27 Introduction. Course structure. Graphs and Scatterplots IPS:1.2, 2.2 1.25, 1.27; read the R help file on pie. 1.25, 1.27 Titanic
1 L 2 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 L 3 Sep 5 Summary Statistics: Center and Spread IPS: 1.3 1.61, 1.62, 1.63
3 L 4 Sep 12 Summary Statistics in R RS:11.1, 11.2, 11.4
4 T 5 Sep 17 Normal distributions IPS 1.4
5 T 6 Sep 24 Correlation and linear fits IPS 2.3, 2.4, 2.5
5 L 7 Sep 26 Correlation and linear fits RS:11.5.3, 11.6.5.2 GG2:2.5.1
6 T 8 Oct 1 Two-way tables and causation IPS 2.6, 2.7
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 Oct 15 Experiment and sampling design IPS 3.2, 3.3
8 L 12 Oct 17 Worksession and due date for first report First Report due
9 T 13 Oct 22 Randomness, Models, Random Variables IPS:4.1, 4.2, 4.3, 4.4
9 L 14 Oct 24 Randomness, a survey of distributions, bootstrap RS: 12.2, 12.6, 12.7
10 T 15 Oct 29 Sampling distributions: means, counts and proportions IPS: 5.1, 5.2
10 L 16 Oct 31
11 T 17 Nov 5 Confidence intervals, Significance, Power and error types IPS: 6.1-6.4
11 L 18 Nov 7 Confidence intervals in R RS:13.2
12 T 19 Nov 12 Means: the t-test IPS 7.1, 7.2
12 L 20 Nov 14 T-test in R RS:13.3.1.1
13 T 21 Nov 19 Proportions: normal approximations and exact tests, median test IPS 8.1, 8.2
13 L 22 Nov 21 Proportions in R RS:13.3.1.3
14 T 23 Nov 26 Two-way tables, goodness of fit IPS:9.1, 9.2
14 L 24 Nov 28 Chi-squared in R RS:13.3.2
15 T 25 Dec 3 Linear regression IPS:10
15 L 26 Dec 5 Linear regression in R RS:14.1, 14.2 Second Report draft due
16 T 27 Dec 10 ANOVA IPS:12
16 L 28 Dec 12 ANOVA in R RS:15.1 Second Report due

| 1 | 1 | Aug 28 | What is data? Where do we get it? | OIS 1.1-5 | Get the book | 1 | 2 | Aug 30 | The RStudio platform | BoR 1.1-4, 2.1-3, 4.1-3, Appendix B | Make sure you can access RStudio | 2W| 3 | Sep 6 | Summarizing and describing data | OIS 1.6-7, BoR 13.1-2 | 1.5, 1.7, 1.10, 1.17, 1.21, 1.25 | 3 | 4 | Sep 11 | Paired data: linear regression | OIS 7.1-3 | 1.39, 1.42, 1.46, 1.47, 1.54 | 3 | 5 | Sep 13 | Data into R | BoR 5.1-2, 8.2 | Deadline for choosing a dataset | 4M| 6 | Sep 18 | Probability | OIS 2.1 | Play Guess the Correlation 10 times, report your top score. Also: 7.2, 7.3, 7.4 | 5 | 7 | Sep 25 | Conditional probability | OIS 2.2 | 2.4, 2.6, 2.9, 2.13, 2.14 | 5 | 8 | Sep 27 | Plotting | BoR 7.1-4, 14.1-4, 25.1 | | 6 | 9 | Oct 2 | Sampling, Random Variables | OIS 2.3-5 | 2.15, 2.23, 2.25, 2.26 | 6 | 10 | Oct 4 | Simulation and randomness | BoR 15.1-2, 16.1-2 | | 7W| 11 | Oct 11 | Types of random distributions | OIS 3.1-5 | 2.30, 2.32, 2.38, 2.40, 2.44 First report draft deadline | 8 | 12 | Oct 16 | Toward inference: variability, CIs| OIS 4.1-2 | 3.3, 3.8, 3.18, 3.19, 3.26, 3.28, 3.44 | | | | Inferential Statistics | | | 8 | 13 | Oct 18 | Last minute report help | Just write your reports... | First report deadline | 9 | 14 | Oct 23 | Hypothesis testing | OIS 4.3 | 4.1, 4.2, 4.12 | 9 | 15 | Oct 25 | Inference and variability in R | BoR 17.1-2, 18.1 | | 10 | 16 | Oct 30 | Other estimators, one-sample mean | OIS 4.5, 5.1 | 4.13, 4.17, 4.18, 4.21, 4.25, 4.28, 4.30 Hypothesis registration deadline | 10 | 17 | Nov 1 | t-test | BoR 18.2 | | 11 | 18 | Nov 6 | Paired & unpaired two-sample means| OIS 5.2-3 | 4.44, 4.48, 5.3, 5.4, 5.12, 5.14 | 11 | 19 | Nov 8 | Linear Regression | BoR 20.1-4| | 12 | 20 | Nov 13 | Many means: ANOVA & linear models | OIS 5.5 | 5.15, 5.17, 5.18, 5.20, 5.22, 5.28, 5.30 | 12 | 21 | Nov 15 | ANOVA | BoR 19.1-2 | | 13 | 22 | Nov 20 | One- and two-sample proportions | OIS 6.1-2 | | 13 | 23 | Nov 22 | Proportions tests | BoR 18.3 | | 14 | 24 | Nov 27 | Paired data tests: Goodness of fit, independence of variables | OIS 6.3-4, 7.4 | | 14 | 25 | Nov 29 | Categorical tests | BoR 18.4 | Second report draft deadline due on Dec 1 | 15 | 26 | Dec 4 | Small sample proportions; randomization | OIS 6.5-6 | | 15 | 27 | Dec 6 | Last minute report help | Just write your reports... | Second report deadline due on Dec 8 | 16 | 28 | Dec 11 | No set topic | | | | | Dec 18 | Final Exam

## 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 November 29.

Your report is due through Blackboard timestamped no later than 14.30 on December 6.

## 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. There will be an opportunity in the final that allow you to raise your grade by up to two minor grade steps: for instance, raise from a B to a B+ or an A-.