MAT111F Elementary Statistics – Syllabus and Course Structure Overview

Course Overview

Course Description

MAT111F Elementary Statistics is a foundational course designed for students from various disciplines. The course introduces the essential concepts and methods of statistics, focusing on both theoretical understanding and practical application. It covers the following major topics:

• Descriptive statistics

• Probability and probability distributions

• Estimation and confidence intervals

• Hypothesis testing

• Correlation and regression

This course fulfills general education requirements in mathematics and is suitable for students seeking to develop quantitative reasoning skills.

Course Materials

• Textbook: Elementary Statistics: Picturing the World (8th Edition) by Larson (with MyStatLab access)

• Calculator: Scientific, financial, or graphing calculator required

• Other Supplies: Stapler, binder, blank/lined paper

Grading Structure

Grades are determined by the following weighted components:

Component	Weight
MyStatsLab Homework	20%
Written/Excel Assignments	10%
Project	10%
Midterms	35–45%
Final Exam	25–15%

Letter grades are assigned according to the following scale:

Grade	Percentage Range
A	94–100
A-	90–93.99
B+	87–89.99
B	83–86.99
B-	80–82.99
C+	77–79.99
C	73–76.99
C-	70–72.99
D+	67–69.99
D	63–66.99
D-	60–62.99
F	0–59.99

Student Learning Outcomes

• Develop organizational, computational, and technological skills for statistical analysis

• Formulate problem statements and hypotheses

• Analyze data and interpret results

• Communicate findings effectively in oral and written forms

• Model and manipulate mathematical relationships to draw logical conclusions

Assessment Measures

• Minimum 60% proficiency on exams, homework, and assignments

• Competency-based assessment on a significant assignment

Course Assignments and Exams

• Homework: Regular assignments via MyStatsLab; additional suggested problems on Canvas

• Written/Excel Assignments & Quizzes: Periodic written or Excel-based assignments, sometimes replaced by in-class quizzes

• Project: Capstone project involving real-life data analysis using Excel

• Exams: Three midterms and a cumulative final exam; two optional '2nd chance' retake days for midterms

Course Schedule (Tentative)

The course follows a structured weekly schedule, covering the following chapters and topics (corresponding to the standard statistics curriculum):

Week	Topics
1–2	Introduction to Statistics (Ch. 1)
3–4	Descriptive Statistics (Ch. 2)
5–6	Probability (Ch. 3)
7	Discrete Probability Distributions (Ch. 4)
8–9	Normal Probability Distributions (Ch. 5)
10–11	Confidence Intervals (Ch. 6)
12–13	Hypothesis Testing with One Sample (Ch. 7)
14	Correlation and Regression (Ch. 9)
15	Hypothesis Testing with Two Samples (Ch. 8)
16	Review and Final Exam

Additional info: The schedule includes review sessions, Excel practice, and project discussions. Specific sections (e.g., 1.1, 2.1, etc.) correspond to textbook subsections.

Academic Policies and Support

• Academic Integrity: Strict adherence to Berry College's code; unauthorized use of AI is prohibited

• Accommodations: Provided through the Academic Success Center for students with documented needs

• Academic Success Center: Offers free peer tutoring and academic consultations

Summary of Main Statistics Topics Covered

• Ch. 1 – Introduction to Statistics: Definitions, types of data, sampling methods

• Ch. 2 – Descriptive Statistics: Measures of central tendency and variability, graphical summaries

• Ch. 3 – Probability: Basic probability rules, counting techniques

• Ch. 4 – Discrete Probability Distributions: Binomial and other discrete distributions

• Ch. 5 – Normal Probability Distributions: Properties of the normal curve, standard normal calculations

• Ch. 6 – Confidence Intervals: Estimating population parameters

• Ch. 7 – Hypothesis Testing with One Sample: Steps and logic of hypothesis testing

• Ch. 8 – Hypothesis Testing with Two Samples: Comparing means and proportions

• Ch. 9 – Correlation and Regression: Linear relationships, least squares regression

• Ch. 10 – Chi-Square Tests and the F-Distribution: (Not explicitly listed in schedule, but may be included as time permits)

Example: Key Formula (Normal Distribution)

The probability density function (PDF) for the normal distribution is:

$f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{-\frac{1}{2} \left( \frac{x-\mu}{\sigma} \right)^2}$

where $\mu$ is the mean and $\sigma$ is the standard deviation.

Conclusion

This syllabus provides a comprehensive overview of the structure, expectations, and content of MAT111F Elementary Statistics. Students are encouraged to use this guide to track their progress and prepare for each topic throughout the semester.