Course Syllabus: Analyzing Business Problems With Hypothesis Testing

Course Overview

This syllabus outlines the structure, requirements, and policies for the course GB513M2: Analyzing Business Problems With Hypothesis Testing at Purdue Global. The course focuses on applying hypothesis testing and probability analysis to solve business problems, which aligns with core topics in college-level statistics.

Course Information

• Course Title: Analyzing Business Problems With Hypothesis Testing

• Credit Hours: 1

• Prerequisites: None

Learning Outcomes

• Apply hypothesis testing and probability analysis to solve business problems.

Textbook Information

• Title: Business Statistics with MyLab

• Authors: David M. Levine, Kathryn A. Szabat, David F. Stephan

• Edition: 5th

• Publisher: Pearson

• ISBN: 9780136787332

Course Activities

• Readiness Check: Multiple-choice assessment to confirm understanding of foundational concepts.

• Learn: Instructional content and resources to build knowledge of hypothesis testing and probability.

• Connect: Faculty Connect opportunities for discussion and clarification of course material.

• Practice: Activities to reinforce concepts and prepare for assessments.

• Competency Assessment (CA): Summative assessment to demonstrate mastery of module objectives.

Grading Criteria

Component Type	Total Points
Writing Assignment	1000
Total	1000

Purdue University Global Grading Scale

Grade	Points	Percent	Grade Point
A	900-1000	90-100	4.0
B	800-899	80-89	3.0
C	700-799	70-79	2.0
F	0-699	0-69	0.0

Key Policies

• Extension Policy: Extensions for assessments are at the discretion of the professor and must be requested before the due date.

• Academic Accommodations: Available for students with documented needs; contact the appropriate office for support.

• Discussion Boards: Used for sharing responses to discussion topics and engaging with peers and instructors.

• Instructor Feedback: Provided within 24 hours for Competency Assessments submitted during the course.

• Rubrics: Provided for Competency Assessments to clarify grading criteria.

• Tutoring: Available through the Academic Success Center.

• Netiquette: Guidelines for respectful and effective online communication are outlined.

Relevant Statistics Topics Covered

• Hypothesis Testing: The process of making inferences about population parameters based on sample statistics. This includes formulating null and alternative hypotheses, selecting appropriate test statistics, and interpreting results.

• Probability Analysis: The study of likelihood and uncertainty, foundational for understanding statistical inference and hypothesis testing.

Key Definitions

• Hypothesis Testing: A statistical method used to decide whether there is enough evidence to reject a null hypothesis about a population parameter.

• Null Hypothesis ($H_0$): The default assumption that there is no effect or no difference.

• Alternative Hypothesis ($H_a$): The statement that contradicts the null hypothesis, indicating the presence of an effect or difference.

• Test Statistic: A standardized value calculated from sample data, used to determine whether to reject the null hypothesis.

• p-value: The probability of obtaining a test statistic as extreme as, or more extreme than, the observed value, assuming the null hypothesis is true.

Key Formulas

• Test Statistic for a One-Sample z-Test:

$z = \frac{\bar{x} - \mu_0}{\sigma / \sqrt{n}}$

• Test Statistic for a One-Sample t-Test:

$t = \frac{\bar{x} - \mu_0}{s / \sqrt{n}}$

• p-value Interpretation: If the p-value is less than the significance level ($\alpha$), reject the null hypothesis.

Example Application

• A business wants to determine if a new process has changed the average time to complete a task. They collect a sample, calculate the sample mean and standard deviation, and use hypothesis testing to decide if the observed difference is statistically significant.

Additional info: This syllabus provides the framework for a statistics course focused on hypothesis testing and probability, which are central to college-level statistics curricula. The specific textbook and learning outcomes confirm the relevance to the listed chapters, especially Chapters 4, 8, and 9.