Instructor: Prof. Megan Owen
Phone: 718-960-7423
Office hours: Tuesdays 12:00-12:50pm (Gillet 137E) and 3-4pm (Gillet 231) and Thursdays 3-4pm (Gillet 231)
Course time:Tuesday and Thursday, 1:00-2:40pm, Gillet 231




Academic Integrity Policy

You are encouraged to work together on the homework, but you should write up the solutions by yourself.

Your grade for this course will be based on:

Assignments* 15%
Project 15%
Midterm 1 17.5%
Midterm 2 17.5%
Final Exam 35%
You must take and pass the final to pass the course.
* For students in MAT 782, the assignment grade will be made up of the weekly homework assignments (10%) and a presentation on non-parametric tests (5%).


All code and plots must be submitted on Blackboard. Written homework can be submitted on paper during class or a (legible) photo of it can be uploaded to Blackboard. No late homework is accepted. Solutions to the homework problems should be written clearly, so that they could be understood by a fellow student. The lowest 3 homework grades will be dropped.


Presentation for MAT 782 only


Date: Topics: Reading, course materials: Homework assigned: Due:
Tues 28 August
Review of syllabus and academic integrity code.
Introduction to statistics, random variables, mean.
Syllabus Homework 1
Thurs 30 August
Random variables: variance, intro to R, mean and variance in R Homework 2
Mon 3 September CUNY: No classes (Labor Day)
Tues 4 September
Data description and exploratory data analysis: types of data, stripcharts and histograms Kerns 3rd edition: 3.1.1, 3.1.2
Data types problems
Homework 3
Thurs 6 September
Data description and exploratory data analysis cont'd: qualitative data, measures of center, order statistics and sample quantile Kerns 3rd edition: 3.1.3, 3.1.4, 3.2 overview, 3.2.2, 3.3.3 Homework 4: See blackboard Homework 1
Mon 10 September - Tues 11 September CUNY: No classes
Thurs 13 September
Data description and exploratory data analysis cont'd: measures of spread, measures of shape, 5 number summary, boxplots, and outliers Kerns 3rd edition: 3.3.4, 3.3.5, 3.4.2, 3.4.3, 3.4.4 Homework 5: See blackboard Homework 2
Project Milestone 1: GitHub user name
Tues 18 September - Wed 19 September CUNY: No classes
Thurs 20 September
Data description and exploratory data analysis cont'd: standardizing variables, multivariate data and data frames, comparing populations
Importing data into R from a CSV file
Kerns 3rd edition: 3.4.5, 3.5, 3.6
Supplementary notes
Basics of importing CSV file: Software Carperntry: Reading and Writing CSV Files
Extra troubleshooting for importing CSV: Datacamp's import tutorial
NYPD crime complaint data (Sept. 2017)
NYC historical population data
Green taxi trip data (Feb. 2, 2016)
Homework 6: See blackboard Homework 3
Tues 25 September
Probability in a nutshell Visualizing conditional probability Homework 7: See blackboard Homework 4
Thurs 27 September
Random variables revisited, Probability distributions, binomial distribution, normal distribution Random variables visualized
Normal distribution interactive graph
Binomial distribution visualization
Homework 8: See blackboard Homework 5
Project Milestone 3: Reading in CSV files, means, and standard deviation
Tues 2 October
Likelihood and maximum likelihood estimation Kerns 3rd edition: 9.1
Maximum Likelihood Estimation
Homework 9: See blackboard Homework 6
Thurs 4 October
Maximum likelihood estimation cont'd Homework 10: See blackboard Homework 7
Project Milestone 3: Reading in CSV files, means, and standard deviation
Project Milestone 4: Plotting your numerical data
8 October CUNY: No classes (Columbus Day)
Tues 9 October
Midterm 1 review Homework 8
Thurs 11 October
Midterm 1
Tues 16 October
Central Limit Theorem Kerns 3rd edition: 8.3
Central Limit Theorem with R
Homework 11: See blackboard Homework 9
Thurs 18 October
Confidence intervals Kerns 3rd edition: 9.2
Penn State: Confidence Level for One Mean
Homework 12: See blackboard Homework 10
Project Milestone 5: Plotting your nominal or ordinal data and comparing data
Tues 23 October
Introduction to hypothesis testing; type 1 and 2 errors; p-values; tests about one proportion Kerns 3rd edition: 10.1, 10.2
Penn State: Hypothesis Testing for Proportions
Homework 13: See blackboard Homework 11 (Day 13)
Thurs 25 October
Hypothesis testing cont'd; tests about two proportions, tests about one mean Kerns 3rd edition: 10.3.1
Penn State: Comparing two proportions
2 proportion tests in R: Two Proportion Z-Test in R
Penn State: Tests about one mean
Homework 14: See blackboard Homework 12 (Day 14)
Project Milestone 6:
Tues 30 October
Regression Hypothesis testing cont'd; tests about two means; subsets of data in R imdb_1000.csv
Kerns 3rd edition: 10.4.1
Penn State: Comparing two means
Calculating proportions from datasets in R: Proportions with mean()
Taking subsets of data in R: Subsetting data
Homework 15: See blackboard Homework 13 (Day 15)
Thurs 1 November
Simple Linear Regression Kerns 3rd edition: 11.1, 11.2 Homework 16: See blackboard Homework 14 (Day 16)
Project Milestone 6: Confidence Intervals
Tues 6 November
Simple Linear Regression Cont'd, Multiple Linear Regression, QQ-plots Code from class
Kerns 3rd edition: 11.2.3, 11.2.4, 11.2.6, 12.1
Homework 17: See blackboard Homework 15 (Day 17)
6 November Last day to withdraw from class with a grade of W
Thurs 8 November
Midterm 2 review Homework 16 (Day 18)
Tues 13 November
Midterm 2
Thurs 15 November
Chi square test Work on project Code from class: Day22.R Homework 18: See blackboard
Project Milestone 7: Hypothesis Testing
Tues 20 November
Analysis of variance (ANOVA) Code from class: Day23.R
Penn State: One-Factor Analysis of Variance
Visualizing ANOVA
Homework 17: See blackboard
23-25 November Thanksgiving Recess: College Closed
Tues 27 November
Analysis of variance cont'd Penn State: One-Factor Analysis of Variance
Visualizing ANOVA
Code from class: Day24.R
Homework 18: See blackboard Homework 17 (Day 23)
Thurs 29 November
Chi-squared test Penn State: Chi-Squared Goodness-of-Fit Test
Code from class: Day25.R
Homework 19: See blackboard Project Milestone 7: Hypothesis Testing
Milestone 8: Regression or another analysis
Tues 4 December
Bayesian statistics Homework 20: See blackboard Homework 18 (Day 24)
Thurs 6 December
Project presentations, Bayesian satistics cont'd Homework 19 (Day 25)
Project slides due at 10am
Tues 11 December
Review for final exam Homework 20 (Day 26)
Thurs 13 December Final project due
Tues 18 December Final exam 3:45pm - 5:45pm