PY 281 - Introduction to Statistics

Description
This course is an introduction to quantitative methods and analytical techniques utilized in behavior research, including research design, data analysis, and interpretation of statistics. Basic descriptive and inferential statistics are considered, including measures of central tendency and variability, the normal distribution, the t-test, ANOVA, correlation, regression, and chi-square. Statistic software SPSS is used to provide computational assistance.

Credit Hours: 4
Contact Hours: 4
Prerequisites/Other Requirements: PY 201 (C or Higher) and [C or Higher in one of the following courses: MA 107, MA 108, MA 110, MA 124, MA 127, MA 129, MA 131, MA 133, MA 134, MA 245, MA 255, or MA 257 (C or Higher) OR ALEKS 46 or Higher]
English Prerequisite(s): None
Math Prerequisite(s): None
Course Corequisite(s): None
Academic Program Prerequisite: None
Consent to Enroll in Course: No Department Consent Required
Dual Enrollment Allowed?: Yes
Number of Times Course can be taken for credit: 1
Programs Where This Course is a Requirement:
Pre-Accounting, A.B. (3+1, Davenport University), Pre-Business, A.B. (3+1, Davenport University), Pre-CyberSecurity, A.A. (General Transfer), Pre-Marketing, A.B. (3+1, Davenport University), Pre-Management, A.B. (3+1, Davenport University), Pre-Psychology, A.A. (General Transfer)

Other Courses Where This Course is a Prerequisite: PY283
Other Courses Where this Course is a Corequisite: None
Other Courses Where This course is included in within the Description: None

General Education Requirement:
None
General Education Learner Outcomes (GELO):
NA
Course Learning Outcomes:

Demonstrate understanding of the basic principles of data collection, observational study, and experimental design.
Construct and interpret graphical and tabular displays of univariate data.
Summarize distributions of univariate data using measures of central tendency, measures of dispersion, and measures of location.
Compare multiple data sets with graphical displays and numerical measures.
Perform basic probability computations.
Solve problems by applying appropriate probability distributions.
Use the Central Limit Theorem to model sampling distributions and compute probabilities based on sampling distributions.
Analyze bivariate quantitative data.
Construct and interpret confidence intervals of proportion or mean for one population.
Construct and interpret confidence intervals for the difference of proportions or means for two populations.
Perform hypothesis tests for the means and proportions for one population.
Perform hypothesis tests for the difference of proportions or means for two populations.
Interpreting p-value, type I and type II errors, and statistical and practical significance.
Input, interpret and apply output from SPSS software.
Interpret and apply appropriate statistical techniques and concepts to real-life data and situations in the behavior and psychological sciences in order to make decisions and/or draw conclusions.
Analyze bivariate qualitative data presented in two-way tables and interpret relationships between categorical variables using chi-square tests.
Perform hypothesis tests including the goodness-of-fit test and ANOVA.

Course Outline:
I. Displaying the Order in a Group of Numbers Using Tables and Graphs

II. Central Tendency and Variability

III. Introduction to Research Design

IV. Core Concepts in Inferential Statistics: Z Scores, the Normal Curve, Sample versus Population, and Probability

V.Introduction to Hypothesis Testing with Z Scores

VI.Hypothesis Tests with Means of Samples

VII. Making Sense of Statistical Significance: Decision Errors, Effect Size, and Statistical Power

VIII. Introduction to t Tests: Single Sample and Dependent Means

IX.The t Test for Independent Means

X. Data Collection and Reporting Inferential Statistics in Research

XI. Introduction to the Analysis of Variance (ANOVA)

XII. Correlation

XIII. Prediction

XIV. Chi-Square Tests (Goodness of Fit, Test for Independence)

Approved for Online and Hybrid Delivery?:
Yes
Instructional Strategies:
Lecture: 50-80%

Facilitated discussion: 10-40%

Group work: 0-40%

Assisted individual work: 0-30%
Mandatory Course Components:
None
Equivalent Courses:
None

Accepted GRCC Advanced Placement (AP) Exam Credit: None
AP Min. Score: NA
Name of Industry Recognize Credentials: None

Course prepares students to seek the following external certification:
No
Course-Specific Placement Test: None
Course Aligned with ARW/IRW Pairing: N/A

Mandatory Department Assessment Measures:
None

Course Type:
Program Requirement- Offering designed to meet the learning needs of students in a specific GRCC program.
Course Format:
Lecture - 1:1
Total Lecture Hours Per Week: 4
People Soft Course ID Number: 101208
Course CIP Code: 42.01
Maximum Course Enrollment: 22
General Room Request: None

High School Articulation Agreements exist?: No
If yes, with which high schools?: None
Non-Credit GRCC Articulation Agreement With What Area: No
Identify the Non Credit Programs this Course is Accepted: NA

School: School of STEM
Department: Psychology
Discipline: PY
Faculty Credential Requirements:
Master’s Degree (GRCC general requirement)
Faculty Credential Requirement Details:
The instructor should posses a Master’s Degree in Psychology or a related Social Science, training in on-line course delivery, and additional training in statistics and research methods.

Major Course Revisions: Prerequisite
Last Revision Date Effective: 20250224T20:23:23
Course Review & Revision Year: 2029-2030