# PSYC 2021. Statistical methods I

**Department of Psychology**

York University

Summer term S1 2018

Tuesday and Thursday, 2:30 - 5:30

York University

Summer term S1 2018

Tuesday and Thursday, 2:30 - 5:30

**• Syllabus**

**General description:**This course covers the fundamental concepts and application of descriptive statistics. It provides an introduction to probability and inferential statistics, including hypothesis testing with the normal and t distributions.

## Announcements

**• August 27.**Here are the final marks for the course, including the final exam. Use the last five digits of your student number to look up your mark in this list.

**• June 29.**Here's an example of how to calculate the sample Pearson correlation.

**• June 11.**In Urdan's chapter 8 (t tests), you can skip the box called "Time out for technicality", and you can also skip the discussion of effect sizes in the section on the independent samples t test (e.g., Table 8.2).

**• May 23.**Here is the companion website for Urdan's textbook, with chapter summaries and solutions to the end-of-chapter problems. Urdan also has a separate website with additional problems and solutions.

**• May 23.**Here are some suggestions on how to study.

**• May 23.**Use the missed class form if you miss a test. Provide us with a printed copy of the form with all information filled in.

**• May 23.**For the tests and final exam, you will need a simple calculator with only basic functions (e.g., arithmetic and square roots). Bring a simple calculator, like this, not a scientific calculator, like this.

**• May 23.**If you are interested in following along with the optional R code that I will post from time to time, you can download the R programming language for free from the R website. R is also available on Computing Commons terminals. The R code is completely optional, and will not be covered on tests or the final exam.

## Lecture notes

• Lecture 1a Introduction
| problems | code | slides |

• Lecture 1b Mean and variance
| problems | code | |

• Lecture 2a Normal distributions; z scores
| problems | code | |

• Lecture 2b Standard error; statistical significance
| problems | code | |

• Lecture 3a Effect size; confidence intervals
| problems | ||

• Lecture 4a t tests
| problems | ||

• Lecture 4b Correlation
| problems | correction | |

• Lecture 5a Regression
| problems | ||

• Lecture 6a The chi-square test
| problems | worked example |

## Tests

**•**Test 1 answers

**•**Test 2 answers

**•**Final exam answers