# Course schedule

Math 214 meets Monday and Wednesday 2.30-4.30pm. The Wednesday session is split between a classroom session and a computer lab session.

Mondays will be dedicated to theory, Wednesdays to practice.

A full time semester is 12 credits; this means that a 4 credit course is about 33% of full time.

Out of 40 hours, these 33% makes out 13h20m in a week. We meet 4h in class. You should be spending about 9h every week out of class working with our material.

It has been shown in research that “Effective Time on Task” is the most important predictor of how much you learn. I will assign homework to train the most recent techniques, but I strongly encourage you to work through as many of the book’s exercises as you can.

## Topic sequence

Week 2016 dates Topic Chapter Homework
Descriptive Statistics
1 Aug 29, 31 Graphs 1.2 W: 1.34, 1.40-41
1 Scatterplots 2.2 2.19, 2.24-25
2 Sep 7 Center and spread 1.3 1.52, 1.72, 1.77-81
3 Sep 12, 14 Normal distributions 1.4 M: 1.118, 1.122, 1.128-130
3 Correlation 2.3 W: 1.154-155, 2.43-44
4 Sep 19, 21 Least squares 2.4 M: 2.66-70
4 Cautions 2.5 W: 2.94-95, 2.102-104
5 Sep 26, 28 Two-way tables 2.6 M: 2.127-130
5 Causation 2.7 W: 2.136, 2.138, 2.141-143
6 Oct 5, 6 Experiment design 3.2 M: 3.33-34, 3.52, 3.57-58, 3.78
6 Sampling design 3.3 W: 3.97, 3.115-116
6 Ethics 3.5 W: Read about the Willowbrook State School; did they conform to APA’s Ethical Principles? What issues can you find? Explain.
7 Oct 17 First Examination (Project 1) 1-4
Inferential Statistics
7 Oct 19 Toward inference 3.4 M: 3.80, 3.83-87,
7 Randomness 4.1 W: 4.14-15, 20, 38, 62, 69-70, 80-81
7 Models 4.2
7 Random Variables 4.3
7 Mean and Variance of Random Variables 4.4
8 Oct 24, 26 Sampling distribution: sample mean 5.1 M: Partner feedback due
8 Sampling distribution: counts/proportions 5.2 W: 4.136-137, 5.14-15, 19, 52-54, 60, 62
9 Oct 31, Nov 2 Confidence intervals 6.1 M: Hypotheses due
9 Significance 6.2 M: 6.12-14, 24-25, 42-45
9 Abuse of tests 6.3 W: 6.54-59, 100-101, 119
9 Power, error types 6.4
10 Nov 7, 9 One mean inference 7.1 M: 7.17, 22-24, 27, 30, 39, 46
10 Two means inference 7.2 W: 7.64-65, 72, 91
11 Nov 14, 16 One proportion inference 8.1 M: 8.12-17, 27, 39
11 Two proportions inference 8.2 W: 8.52-57, 71
12 Nov 21, 23 Two-way tables, chi-squared 9.1 M: 9.1-2, 7-10
12 Goodness of fit 9.2 W: 9.11-13, 17-19
13 Nov 28, 30 Linear regression 10 M: 10.1-4, W: 10.16-21
14 Dec 5, 7 ANOVA 12 M: 12.3, 5-6 W: 12.54-57
15 Dec 12 Second Examination (Project 2) 2, 5-10
15 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.

## 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 bea 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-.