MAT-156 Introductory Statistics Syllabus and Course Overview

Course Overview

Course Description

This course, MAT-156 Statistics, provides an introduction to the fundamental concepts and methods of statistics. The curriculum covers descriptive statistics, probability, inferential statistics, and their applications to real-world problems, particularly in business and other fields. Students will learn to analyze data, interpret results, and utilize technology for statistical analysis.

• Descriptive Statistics: Focus on measures such as mean, median, mode, standard deviation, and variance.

• Correlation and Regression: Introduction to analyzing relationships between variables.

• Probability and Distributions: Emphasis on probability rules, binomial and normal distributions, and the Central Limit Theorem.

• Inferential Statistics: Confidence intervals, hypothesis testing for means and proportions, and applications to various fields.

• Technology: Use of StatCrunch and MyLab Statistics for data analysis and assignments.

Course Structure and Grading

Assignments and Evaluation

• Chapter Homework Assignments (200 points): Eight assignments covering course chapters, with deadlines and late penalties.

• Tests (240 points): Five tests (multiple choice and short answer), with the lowest test score dropped.

• Cumulative Final Exam (80 points): Comprehensive assessment at the end of the course.

• Check-In Discussions (60 points): Two discussion assignments to encourage engagement and reflection.

Grading Scale:

Letter Grade	Percentage Range
A	90-100
B+	87-89.9
B	80-86.9
C+	77-79.9
C	70-76.9
D+	67-69.9
D	60-66.9
F	0-59.9

Course Topics and Learning Outcomes

Main Topics Covered

• Introduction to Statistics and Data Collection

• Describing Data with Tables and Graphs

• Describing Data Numerically

• Probability

• Binomial and Normal Distributions

• Sampling Distributions and the Central Limit Theorem

• Confidence Intervals (Means and Proportions)

• Hypothesis Testing (Means and Proportions, One and Two Samples)

• Correlation and Regression

Student Learning Outcomes:

• Produce and analyze numerical summaries and graphical displays of data.

• Analyze bivariate data using correlation and regression.

• Apply basic rules of probability.

• Use the Central Limit Theorem for sampling distributions.

• Draw inferences about populations using sample data.

• Utilize technology in data analysis.

Course Policies and Resources

Attendance and Participation

• Attendance is tracked via completion of the syllabus quiz within the first ten days.

• Regular engagement and timely completion of assignments are essential for success.

Late Work and Make-Up Policy

• Homework submitted after the due date is penalized 30% (if before the final deadline).

• No assignments accepted after the course closes.

• Tests must be completed by their respective deadlines; the lowest test score is dropped.

Academic Integrity and AI Usage

• Students are expected to use AI tools ethically and acknowledge their use when applicable.

• All work must reflect the student's own understanding and effort.

Support Services

• Free tutoring (on campus and online)

• Accommodation Services for students with disabilities

• Counseling, Career Services, Food Pantry, and more

• Learning Commons and Library Services

• Test Center for proctored exams

Technology Requirements

• MyLab Statistics and StatCrunch: Required for homework, quizzes, and tests. Access is included in tuition.

• VitalSource eTextbook: Electronic textbook provided through MyLab.

Course Schedule and Deadlines

• Homework and tests have specific deadlines; late submissions are penalized or not accepted after the final deadline.

• The course closes at 11:00 PM CST on December 11; no work is accepted after this time.

Summary Table: Course Components and Points

Component	Number	Total Points
Chapter Homework Assignments	8	200
Tests	5	240
Final Exam	1	80
Check-In Discussions	2	60
Total		500

Key Academic Skills Developed

• Utilizing symbolic, graphical, numerical, and written representations of mathematics

• Communicating mathematical reasoning

• Modeling real-world problems with mathematics

• Interpreting results in context

• Collaboration, information literacy, analytic inquiry, and quantitative reasoning

Additional info:

• This syllabus outlines the structure and expectations for an introductory statistics course, covering all major topics typically found in a college-level statistics curriculum.

• Students are encouraged to use available resources and communicate with the instructor for support.