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EPSY 530 Statistics I - Division of Educational Psychology, University at Albany
https://github.com/jbryer/epsy530summer2015
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EPSY 530 Statistics I - Division of Educational Psychology, University at Albany
Host: GitHub
URL: https://github.com/jbryer/epsy530summer2015
Owner: jbryer
Created: 2015-04-06T19:44:33.000Z (over 9 years ago)
Default Branch: master
Last Pushed: 2015-06-10T16:13:54.000Z (over 9 years ago)
Last Synced: 2023-03-12T10:55:30.636Z (almost 2 years ago)
Language: HTML
Size: 78.7 MB
Stars: 0
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md
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README

        ## EPSY 530 - Statistics I - Summer 2015

**Instructor:** Jason Bryer, Ph.D. [[email protected]](mailto:[email protected]?Subject=EPSY530)  

**TA:** Justin Chase [[email protected]](mailto:[email protected]?Subject=EPSY530)  

**Website:** [epsy530.bryer.org](http://epsy530.bryer.org)  

**Class Time:** Monday & Wednesday 4:15pm to 7:05pm  

**Class Location:** Humanities 111  

**Office Hours:** By appointment  

### Course Description

Descriptive statistics including measures of central tendency and variability, correlation and regression. Introduction to statistical inference, including sampling distributions, significance tests, confidence intervals, and power of tests of significance.

### Grading

* Homework (25%)

* Labs (50%)

* Final exam (25%)

#### Grade Distribution

A = 93+; A- = 90-92; B+ = 87-89; B = 84-86; B- = 80-83; C+ = 75-79; C = 70-74; D = 65-69; E = <65

### Schedule

*NOTE: Tentative. Subject to change*

Date   | Chapter    | Topic                                     | Resources

-------|:----------:|:------------------------------------------|:--------------------

May 27 | 1          | Intro to Data                             | [Chapter 1 Slides](https://github.com/jbryer/EPSY530Fall2014/blob/master/Slides/Chapter1/Chp%201/chp1.pdf?raw=true) 
Lab 0 In class

June 1 | 2          | Probability                               | [Chapter 2 Slides](https://github.com/jbryer/EPSY530Fall2014/blob/master/Slides/Chapter2/Chp%202/chp2.pdf?raw=true)

June 3 | 3          | Distributions                             | [Chapter 3 Slides](https://github.com/jbryer/EPSY530Fall2014/blob/master/Slides/Chapter3/Chp%203/chp3.pdf?raw=true)
[Distribution Calculator](http://spark.rstudio.com/minebocek/dist_calc/)
[Probability Tables](https://github.com/jbryer/EPSY530Fall2014/blob/master/Textbook/os2_prob_tables.pdf?raw=true)

June 8 | 4          | Foundation for Inference                  | [Chapter 4 Slides](https://github.com/jbryer/EPSY530Fall2014/blob/master/Slides/Chapter4/Chp%204/chp4.pdf?raw=true)

June 10 | 5         | Inference for Numerical Data              | [Chapter 5 Slides](https://github.com/jbryer/EPSY530Fall2014/blob/master/Slides/Chapter5/Chp%205/chp5.pdf?raw=true) 
[Shiny App for Error Rates](http://shiny.albany.edu/stat/betaprob)
[Shiny App for t-distributions](http://shiny.albany.edu/stat/tdist/) 
[t-Test Exercise](http://htmlpreview.github.io/?https://github.com/jbryer/EPSY530Fall2014/blob/master/R/t-test-Exercise.html) ([Rmd](https://github.com/jbryer/EPSY530Fall2014/blob/master/t-test-Exercise.Rmd?raw=true), [R](https://github.com/jbryer/EPSY530Fall2014/blob/master/NullHypothesisSetup.R?raw=true))

June 15  | 6        | Inference for Categorical Data            | [Chapter 6 Slides](https://github.com/jbryer/EPSY530Fall2014/blob/master/Slides/Chapter6/Chp%206/chp6.pdf?raw=true) 
 [Why .05?](https://www.openintro.org/stat/why05.php)

June 17 | 7          | Linear Regression                         | [Chapter 7 Slides](https://github.com/jbryer/EPSY530Fall2014/blob/master/Slides/Chapter7/Chp%207/chp7.pdf?raw=true) 
 [Linear Regression PDF](https://github.com/jbryer/EPSY530Fall2014/blob/master/Slides/LinearRegression.pdf?raw=true)

June 22 |            | Linear Regression Exercise                | [Handout](https://rawgithub.com/jbryer/EPSY530Fall2014/master/Resources/LinearRegressionExercise.pdf) 
[Slides](https://rawgithub.com/jbryer/EPSY530Fall2014/master/Slides/LinearRegressionNYSRC.html)

June 24 |     | Review       |

June 29 |     | Final Exam - Take Home Due       |

### Textbook

##### Required

Diez, D.M., Barr, C.D., & Çetinkaya-Rundel, M. (2012). *OpenIntro Statistics (2nd Ed).* 

This is an open source textbook and can be downloaded in PDF format [here](https://github.com/jbryer/EPSY530Fall2014/blob/master/Textbook/OpenIntroStatistics2Ed.pdf?raw=true), from the [OpenIntro](http://www.openintro.org/stat/textbook.php) website, or a printed copy can be ordered from [Amazon](http://www.amazon.com/dp/1478217200).

##### Recommended

Kabacoff, R.I. (2011). *R in Action*. Manning Publications.  

You can find a lot of the material in *R in Action* on Kabacoff's website, [statmethods.net](http://statmethods.net/). You can receive 38% off using the ria38 promo code when ordering from [here](http://www.manning.com/kabacoff/).

##### Other Documents

* [Probability Tables](https://github.com/jbryer/EPSY530Fall2014/blob/master/Textbook/os2_prob_tables.pdf?raw=true)

* [Inference Guide](https://github.com/jbryer/EPSY530Fall2014/blob/master/Textbook/os2_extra_inference_guide.pdf?raw=true)

### Homework Assignments

* Chapter 1. p. 47: 1.2, 1.4, 1.8, 1.10, 1.12, 1.16 (part a only), 1.23, 1.30, 1.39, 1.47, 1.48

* Chapter 2. p. 107: 2.1, 2.2, 2.6, 2.8, 2.11, 2.12, 2.15, 2.17, 2.21, 2.28, 2.46

* Chapter 3. p. 149: 3.1, 3.3, 3.5, 3.10, 3.16, 3.18, 3.19

* Chapter 4. p. 197: 4.1, 4.3, 4.4, 4.9, 4.11, 4.15, 4.19, 4.22, 4.29, 4.33, 4.47

* Chapter 5. p. 247: 5.2, 5.4, 5.5, 5.7, 5.9, 5.15, 5.17, 5.18, 5.21, 5.24, 5.33

* Chapter 6. p. 298: 6.1, 6.5, 6.13, 6.16, 6.23, 6.26, 6.32, 6.37, 6.39, 6.42

* Chapter 7. p. 330: 7.3, 7.6, 7.7, 7.13, 7.17, 7.19, 7.20, 7.24, 7.28

### Labs

These mini projects will have you explore statistical topics using R. For each project, create a R Markdown file (you can download the template for each lab below by right clicking and choosing "Save Link As..."). Name your file using the following format: `LastName-X.Rmd` where X is 0 to 8 for the project number. You can email your projects with `EPSY530-Lab` in the subject.



 Introduction to R and RStudio (Template)

 Introduction to Data (Template)

 Probability (Template)

 Distributions of Random Variables (Template, Preview)

 Foundations for Statistical Inference 

     Sampling Distributions (Template, Preview)

     Confidence Levels (Template)





 Inference for Numerical Data (Template)

 Inference for Categorical Data (Template)

 Introduction to Linear Regression (Template)




### Software

*An RStudio will be available through the web (Google Chrome is recommended) at [http://learnrstats.com/rstudio](http://learnrstats.com/rstudio). You will be given a username and password in class. However, it is highly recommended that you also install R and RStudio on your computer if possible.*

Although this course will emphasize statistical concepts, we will make use of [R](http://r-project.org), an open source statistics program and language. Be sure to [install R](http://cran.r-project.org/) and [RStudio](http://rstudio.com) on your own computers within the first week of the class.

* R - [Windows](http://cran.r-project.org/bin/windows/base/) or [Mac](http://cran.r-project.org/bin/macosx/)

* RStudio - [Download Windows or Mac version from here](http://www.rstudio.com/products/rstudio/download/)

If using Windows, you also need to download and install these:

* [RTools](http://cran.r-project.org/bin/windows/Rtools/)

* [ActivePerl](http://www.activestate.com/activeperl/downloads/thank-you?dl=http://downloads.activestate.com/ActivePerl/releases/5.16.3.1603/ActivePerl-5.16.3.1603-MSWin32-x86-296746.msi)

Once everything is installed, execute the following command in RStudio to install the packages we will use for this class (you can copy-and-paste):

```

install.packages(c('openintro','OIdata','devtools','ggplot2','psych','reshape2',

				   'knitr','markdown'), repos='http://cran.r-project.org')

devtools::install_github("seankross/lego")

```

Here are some resources to help you learn and use R Markdown:

* [O'Reilly Try R](http://tryr.codeschool.com/). Great tutorial on R where you can try R commands directly from the web browser.

* [R Reference Card](http://cran.r-project.org/doc/contrib/Short-refcard.pdf)

* [Video Overview of RStudio](http://vimeo.com/97166163)

* [Markdown Basics](http://daringfireball.net/projects/markdown/basics). Markdown is a way of formatting plain text documents mostly for the web. However, it has become for other writing tasks too. It has become popular because it focusses on writing and not formatting. The formatting is taken care later. The [Markdown Basics](http://daringfireball.net/projects/markdown/basics) provides a nice introduction to Markdown.

* [The R Markdown Website](http://rmarkdown.rstudio.com/) has a nice introduction on how Markdown is extended to allow for the inclusion of R code and output.

* [Two page cheat sheet on R Markdown](https://github.com/jbryer/EPSY530Fall2014/blob/master/Resources/rmarkdown-cheatsheet.pdf?raw=true). 

* [Video Introduction to R Markdown](https://www.youtube.com/watch?v=cFe1UJrj7lc). This short video (under 4 minutes) was recorded with an older version, so not all of the features and dialog boxes will look the same, but may be helpful.

* [Video on RMarkdown by RStudio](http://vimeo.com/94181521) - This 26 minute video talks about some updates to RMarkdown.

### Academic Integrity

Whatever you produce for this course should be your own work and created specifically for this course. You cannot present work produced by others, nor offer any work that you presented or will present to another course. If you borrow text or media from another source or paraphrase substantial ideas from someone else, you must provide a reference to your source.

The University policy on academic dishonesty is clearly outlined in the Student Bulletin, and includes, but is not limited to plagiarism, cheating on examinations, multiple submissions, forgery, unauthorized collaboration, and falsification. These are serious infractions of University regulations and could result in a failing grade for the work in question, a failing grade in the course, or dismissal from the University. http://www.albany.edu/undergraduate_bulletin/regulations.html

### Reasonable Accommodation

Reasonable accommodations will be provided for students with documented physical, sensory, sys- temic, cognitive, learning and psychiatric disabilities. If you believe you have a disability requiring accommodation in this class, please notify the Director of Disabled Student Services (Campus Center 137, 442-5490). That office will provide the course instructor with verification of your dis- ability, and will recommend appropriate accommodations. For more information, visit the website of the UAlbany Office for Disabled Student Services. http://www.albany.edu/studentlife/DSS/ guidelines/accomodation.html