MATH 1160 – Introduction to Statistics: Course Syllabus and Study Guide

MATH 1160 – Introduction to Statistics

Course Overview

This course provides an introduction to the theory and application of descriptive and inferential statistics. Students will learn about data collection, probability, distributions, estimation, hypothesis testing, correlation, regression, and the analysis of categorical data. The course is designed for students in a variety of disciplines who require a foundational understanding of statistics.

• Instructor: Alan Meichsner

• Schedule: Mondays and Wednesdays, 4:30–6:20pm

• Location: Anvil Tower Room 712, New Westminster Campus

• Textbook: Data and Models, Fourth Canadian Edition, De Veaux et al., Pearson, 2022 (with MyLab)

• Calculator: A calculator with statistical functionality is strongly recommended. The Math Lab has a limited number of TI-83+ and TI-84+ calculators for borrowing.

Course Topics and Weekly Schedule

The following table outlines the weekly topics, corresponding textbook chapters, and important dates for assessments.

Week	Date	Topic	Chapters/Section
1	Sept 1	Labour Day – College closed
2	Sept 3	Introduction to statistics, data, variables	1
3	Sept 8	Sampling, observational and experimental studies	9, 10
4	Sept 10	Categorical data	2
5	Sept 15	Numerical data part 1	3.1–3.3, 3.6
6	Sept 17	Numerical data part 2	3.4, 3.5, 3.7, 4.3, 4.8
7	Sept 22	Probability part 1	12
8	Sept 24	Probability part 2	13
9	Sept 29	Random variables	13.1–13.3
10	Oct 1	Normal distributions and normal random variables	15.1–15.4, 8.1/6
11	Oct 6	Sampling distributions, the Central Limit Theorem	14
12	Oct 13	Thanksgiving Day – College closed
13	Oct 15	Term Test 1
14	Oct 20	Binomial random variables	13.4, 13.7, 13.8
15	Oct 22	Confidence intervals for one proportion	17
16	Oct 27	Inference for proportions	18
17	Nov 3	Inference for two proportions	21
18	Nov 5	Inference for means	19
19	Nov 10	Inference for two means	20
20	Nov 12	Inference for matched paired	20
21	Nov 17	Scatter plots and correlation	6
22	Nov 19	Term Test 2
23	Nov 24	Linear regression and residuals	7, 8
24	Nov 26	Goodness of fit	22.3
25	Dec 1	Chi Square test for independence	22.4
26	Dec 3	Catch up / review
Final Exam Period: Dec 5th – 15th

Assessment and Grading

Grades are based on a combination of weekly tutorials, homework, term tests, and a final exam. The following table summarizes the assessment components and their weightings:

Component	Weight
Tutorials (Weekly)	10%
Homework (Weekly)	10%
Term Test 1 (Oct 15)	22.5%
Term Test 2 (Nov 19)	22.5%
Final Exam (Dec 5–15)	35%

Each student’s overall grade will be rounded to the nearest percent and assigned a letter grade according to the following scale:

Percentage	Letter
90–100	A+
85–89	A
80–84	A-
75–79	B+
70–74	B
65–69	B-
60–64	C+
55–59	C
50–54	C-
0–49	F

Key Course Policies

• Attendance: Regular attendance is expected and may impact performance. Students who miss more than 30% of scheduled lectures may be denied the opportunity to write the final exam.

• Make-up Assessments: Make-up tests or assignments are only available for students with documented, acceptable reasons (e.g., illness, emergency).

• Academic Integrity: All students are expected to adhere to the Douglas College Academic Integrity Policy. Academic dishonesty will be dealt with according to college regulations.

• COVID-19 Policy: Students experiencing symptoms or who have tested positive should not attend class and must follow college guidelines for isolation and notification.

Support Resources

• Math Lab: Located in room S3910, the Math Lab offers homework help and support from faculty and tutors. Check the posted schedule for hours of operation.

• Tutorials: Weekly tutorials focus on problem-solving skills and group work, with support from a Mathematics Laboratory Facilitator.

• Online Resources: Additional practice and support are available through the textbook’s MyLab platform and the Math Lab website.

Summary of Main Topics

• Descriptive Statistics: Methods for summarizing and describing data, including measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation).

• Data Collection: Sampling methods, experimental design, and observational studies.

• Probability: Basic probability rules, random variables, and probability distributions (binomial, normal).

• Inferential Statistics: Estimation, confidence intervals, and hypothesis testing for proportions and means.

• Regression and Correlation: Analysis of relationships between variables using scatter plots, correlation coefficients, and linear regression models.

• Chi-Square Tests: Goodness-of-fit and tests for independence in categorical data.

Example: Confidence Interval for a Population Mean

• Definition: A confidence interval provides a range of values within which the true population mean is likely to fall, with a specified level of confidence (e.g., 95%).

• Formula:

$\bar{x} \pm z^* \frac{s}{\sqrt{n}}$

• Where $\bar{x}$ is the sample mean, $z^*$ is the critical value from the standard normal distribution, $s$ is the sample standard deviation, and $n$ is the sample size.

Additional info:

• This syllabus provides a comprehensive overview of the course structure, assessment, and key topics. For detailed explanations, students should refer to the textbook and attend lectures and tutorials.