Course Description

Introduction to descriptive statistics and probability, probability distributions, estimation, tests of hypotheses, chi-square tests, regression analysis, and correlation with applications in business and science. (Crosslisted with MATH 201)

Rationale

As members of a society increasingly devoted to the use and misuse of numbers, students must learn to correctly interpret and construct statistical presentations in all areas of public discourse, especially in their major fields. This course emphasizes the major applications of statistical knowledge rather than its theory. The course seeks to educate men and women who will make important contributions to their workplaces and communities, follow their chosen vocations as callings to glorify God, and fulfill the Great Commission.

Measurable Learning Outcomes

Upon successful completion of this course, the student will be able to:

1. Construct and interpret appropriate graphical representations of data.
2. Compute statistical measures which describe the location, dispersion, and placement of data values.
3. Compute probabilities associated with multiple events and common distributions.
4. Create confidence intervals for unknown parameters.
5. Perform hypothesis tests.
6. Determine the correlation between two variables and develop linear regression models which predict the value of one variable as a function of the other.

General Education Foundational Skill Learning Outcomes: Technological Solutions and Quantitative Reasoning (TSQR)

1. TSQR 1: Analyze data and inform action through a structured method.
2. TSQR 2: Predict the output based on input in practical scenarios using technological solutions and/or quantitative reasoning.
3. TSQR 4: Relate technology and quantitative reasoning to participation in God’s redemptive work.

Course Assignment

After reading the Course Syllabus and Student Expectations, the student will complete the related checklist found in the Course Overview.

For each module, the student will complete several sets of exercises that will correlate with the assigned Learn material. These exercises will be completed using ALEKS. (MLO: A, B, C, D, E, F; TSQR 1, 2).

This real-world project has 5 parts and utilizes course-specific information to apply statistics to real-life data collection and analysis. The students will gather class data and utilize this data in the first 4 parts of the project. The students will receive feedback on the first 4 parts of the project before submitting the final report as the final 5th part of the project (MLO: A, B, E; TSQR 1, 2, 4).

The purpose of the review assignments is to ensure the students are adequately prepared for each exam assignment. The review assignment for each exam assignment must be completed with a grade of at least 70% before the exam can be taken. Each question on all the Review Assignments can be worked on as many times as needed to get full credit. All the Review Assignments can be found in ALEKS. (MLO: A, B, C, D, E, F; TSQR 1, 2).

The student will complete 4 exams throughout the course. Each exam will be timed, open-book/open-notes, and will cover 2 modules of material. The exams will be taken in ALEKS (MLO: A, B, C, D, E, F; TSQR 1, 2).

Note: Students must submit all written work for each exam question on the Written Work Submission Assignment that correlates with that specific Exam Assignment. Written Work Submission Assignments must be submitted in Canvas. Exams submitted without the accompanying Written Work Submission Assignment will not be accepted.

Written work for each exam question must be submitted in Canvas. Exams submitted without the accompanying work will not be accepted.