Course Overview

Course Description

This Intermediate Statistics course (STA 3163) provides students with tools for the analysis of data, focusing on both parametric and nonparametric statistical methods. The course covers normal distributions, hypothesis testing, ANOVA, regression, correlation, and the use of computerized statistical tools for data analysis.

• Credit Hours: 3.0

• Modality: Fully online, synchronous/real-time

• Required Textbook: Elementary Statistics w/ MyMathLab by Triola, Pearson, 14th Edition

Program Mission and Goals

Mission Statement

The mission of the Keiser University general education component is to provide a comprehensive educational experience that prepares students to critically think, communicate, and problem-solve, while promoting intellectual curiosity and lifelong learning for the betterment of society.

Program Goals

• Critical Thinking Skills: Develop the ability to analyze information and solve complex problems.

• Effective Communication: Foster skills in expressing ideas orally and in writing.

• Ethical and Social Responsibility: Promote ethical reasoning and cultural awareness.

• Lifelong Learning: Encourage ongoing intellectual curiosity and information literacy.

• Quantitative Reasoning: Solve quantitative problems using evidence and various formats (words, tables, graphs, equations).

Course Learning Objectives

Quantitative Analysis and Statistical Methods

Upon completion of the course, students will be able to:

• Extract and convert quantitative information from equations, graphs, diagrams, tables, and words.

• Draw conclusions based on quantitative analysis and critically evaluate processes and results.

Key Statistical Topics

• Nonparametric Tests: Identify qualities and perform tests such as the sign test, Wilcoxon signed ranks test, Wilcoxon rank-sum test, Kruskal-Wallis test, and compute rank correlation .

• Hypothesis Testing: Use sample data to test claims about population parameters.

• Estimation and Inference: Extend methods to situations involving two sets of sample data.

• Correlation and Regression: Determine if a correlation exists, identify best-fit equations, predict values, and analyze differences between predicted and actual values.

• Multiple Regression: Identify linear equations for correlations among three or more variables.

• Nonlinear Models: Develop mathematical models for nonlinear correlations.

• Analysis of Variance (ANOVA): Test for equality of three or more population means (one-way ANOVA) and compare populations separated into categories (two-way ANOVA).

• Statistical Software: Implement inferential techniques using tools such as Excel, Minitab, Statdisk, SAS, SPSS, R, or comparable programs.

Course Structure and Evaluation

Grading and Evaluation Methods

• Discussions (Weeks 1-4): 7.5%

• MyLab Homework (Weeks 1-4): 7.5%

• MyLab Tests (Weeks 1-3): 60%

• Final Exam: 20%

• Posttest: 5%

Total Percentage: 100%

Grading Scale

Topical Outline / Course Calendar

• Week 1: Introduction, Certification, Syllabus, eCampus Course Information, MyLab Statistics Acknowledgement, Practice Quiz, PRETEST, Discussion, Homework, Test (Chapters 8 & 9)

• Week 2: Discussion, Homework, Test (Chapter 10)

• Week 3: Discussion, Homework, Test (Chapters 12, 13.1-13.3)

• Week 4: Discussion, Homework (Chapters 13.4-13.7), Final Exam, POSTTEST

Course Policies

Academic Integrity

• Maintain high standards of academic conduct and honesty.

• Plagiarism, cheating, and misconduct are not tolerated and may result in penalties.

Participation

• Active participation in online discussions and engagement is required.

• Respectful listening and professional conduct are expected.

Missed Tests/Quizzes

• Makeup exams allowed only with pre-approval or documented reasons.

• Exam format may differ but content remains unchanged.

Late Assignments

• Late assignments accepted only in emergencies and with instructor approval.

• All work must be submitted by the last day of class.

Civility and Professionalism

• Respect classmates' opinions and maintain professional courtesy.

• Work together in a spirit of cooperation.

Artificial Intelligence Policy

• All student work must be original and developed through critical thought.

• Use of AI must be disclosed and approved by the professor.

• Violations may result in academic honesty penalties.

Disability Accommodations

• Students requiring accommodations must complete the application process and receive approval.

Required Resources

• Textbook: Elementary Statistics w/ MyMathLab (Triola, Pearson, 14th Edition)

• Computer Assisted Instruction: MyLabMath (MLM) provides multimedia resources, skill builders, e-text, and instructor support.

Summary Table: Key Statistical Methods Covered

Important Formulas and Concepts

• Normal Distribution:

• Hypothesis Testing:

• Correlation Coefficient:

• Regression Equation:

• ANOVA F-statistic:

• Wilcoxon Rank-Sum Test: Nonparametric test for comparing two independent samples

Course Information and Faculty

• Instructor: Lisa Whitaker

• Email: lwhitaker@keiseruniversity.edu

• Office Hours: See course calendar for Kaltura Live dates and times

University Policies and Resources

• Refer to the University Undergraduate and Graduate Catalogs and Student Program Handbook for detailed policies.

• Catalogs available at Keiser University Catalog.

Additional info: The course covers advanced topics beyond introductory statistics, including nonparametric methods and multiple regression, but all topics are directly relevant to the core chapters listed for Introductory Statistics.