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: MTH 112, MTH 128, MTH 122, MTH 116, MTH 136, MTH 193, MTH 148, MTH 178, MTH 208, MTH 245, MTH 248, or MTH 252 (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:

Evaluate statistically-based results reported in popular media by identifying the study design, assessing the validity of conclusion, and justifying whether claims are supported by the data.
Formulate research questions suitable for statistical investigation and apply the statistical investigative process to analyze and interpret data to answer those questions.
Create graphical displays and compute numerical summaries using appropriate software, then analyze and explain what information the displays reveal and what limitations they have.
Define, illustrate, and explain how variability influences statistical conclusions and analyze examples demonstrating its impact on data interpretation.
Explain and apply the concept of randomness to design valid studies and evaluate how randomization supports valid statistical inference.
Construct, fit, and interpret statistical models – including multivariable models – to analyze relationships among variables and evaluate model fit.
Conduct hypothesis tests and calculate confidence intervals for various scenarios, then interpret and justify conclusions in context.
Interpret, summarize, and draw conclusions from statistical software output, explaining how each component (e.g., p-values, confidence intervals, effect sizes) supports their interpretation.
Identify, analyze, and propose appropriate responses to ethical issues related to data collection, analysis, and reporting in statistical practice.

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

GRCC Course Type:
Program Requirement: Meets 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
Course Software Utilized: SPSS

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: N/A
Last Revision Date Effective: 20260220T14:31:23
Course Review & Revision Year: 2030-2031