Statistics I (MATH 1200) Syllabus Study Guide

Course Overview

Introduction to Statistics I

This course, Statistics I (MATH 1200), introduces students to the fundamental concepts of descriptive and inferential statistics, probability, and probability distributions. The course emphasizes both theoretical understanding and practical application using statistical software.

• Credits: 3

• Textbook: Introductory Statistics: Exploring the World Through Data by Gould, Wong, and Ryan (3rd edition)

• Software: StatCrunch via Blackboard

• Prerequisites: Completion of MATH 0988/0989, MATH 1010/1011, or higher placement

Catalogue Description

Scope of the Course

The course covers both descriptive and inferential statistics, including:

• Descriptive Statistics: Population vs. sample, frequency distributions, measures of central tendency and variation, measures of position, correlation, and linear regression.

• Inferential Statistics: Confidence intervals and hypothesis testing.

• Probability: Probability rules and probability distributions.

• Technology: Use of statistical software for analysis.

Course Objectives

Learning Goals

Upon completion, students should be able to:

• Describe data using numerical measures, tables, and graphs.

• Analyze relationships between variables using distributions, correlation, and regression.

• Compute probabilities using rules and distributions.

• Estimate population parameters from sample statistics.

• Conduct hypothesis tests.

Learning Objectives

Key Topics and Skills

• Definition/Overview of Statistics: Understanding the role and scope of statistics in analyzing data.

• Descriptive Statistics: Organizing, describing, summarizing, and displaying categorical and quantitative data.

• Comparing Data: Using distributions to compare datasets.

• The Normal Distribution: Properties, z-scores, and areas under the curve.

• Central Limit Theorem: Importance in sampling distributions.

• Correlation and Regression: Describing relationships between quantitative variables and finding best-fit equations.

• The Statistical Process: Randomness, sampling, and types of studies.

• Probability: Definitions, rules, random variables, and probability models.

• Statistical Inference: Confidence intervals and hypothesis testing using significance levels and p-values.

• Use of Technology: Applying statistical software for data analysis and hypothesis testing.

Course Schedule

Chapter Topics and Sequence

The course is structured around the following chapters, each covering essential statistical concepts:

• Ch. 1: Introduction to Data

• Ch. 2: Picturing Variation with Graphs

• Ch. 3: Numerical Summaries of Center and Variation

• Ch. 4: Regression Analysis: Exploring Associations Between Variables

• Ch. 5: Modeling Variation with Probability

• Ch. 6: Modeling Random Events: The Normal and Binomial Models

• Ch. 7: Survey Sampling and Inference

• Ch. 8: Hypothesis Testing for Population Proportions

• Ch. 9: Inferring Population Means

Each chapter includes subtopics such as classifying data, visualizing variation, summarizing distributions, regression, probability models, and hypothesis testing.

Assessment and Grading

Evaluation Methods

• Homework: 10% (completed online via MyStatLab)

• Tests: 60% (in-class, covering specific chapters)

• Cumulative Final Exam: 30% (covers all course material)

Grading Standards and Equivalency Table

Letter Grade	Grade Scale	GPA Equivalency	Description
A	93-100	4.0	Distinguished achievement
A-	90-92	3.7
B+	87-89	3.3
B	83-86	3.0	High level of achievement
B-	80-82	2.7
C+	77-79	2.3
C	73-76	2.0	Basic understanding
C-	70-72	1.7
D+	67-69	1.3
D	63-66	1.0	Minimal performance
D-	60-62	0.7
F	0-59	0.0	Failure

Note: Grades are rounded according to the policy: at or above .50 truncated rounds up; at or below .49 truncated rounds down.

Academic Integrity

Expectations and Policies

• Plagiarism: Submitting work of another author without proper attribution.

• Cheating: Unauthorized assistance, use of unauthorized sources, falsifying data, and other forms of academic misconduct.

• Penalties: Faculty may assign a grade of "F" for academic misconduct; repeated infractions may result in stronger penalties.

Attendance and Engagement

Guidelines

• Regular attendance is expected; excessive absence may affect grades.

• Active engagement is required; non-participation may result in being dropped from the course.

Course Requirements

Homework and Tests

• Homework is submitted online and due by the date of the next test.

• Tests are taken in class; no make-ups, but the final exam grade may replace a missed or lowest test grade.

• Final exam is cumulative.

Support and Resources

Student Services

• Library: Access to academic resources and study spaces.

• Mental Health: Wellness counseling available.

• Tutoring: In-person and online support for assignments and academic progress.

• Special Needs: ADA accommodations available through the Disabilities Office.

• Veterans: Support for military-related attendance or participation issues.

Withdrawal and Incomplete Policies

Procedures

• Withdrawals must be submitted in writing by the deadline; instructors do not withdraw students.

• Incomplete grades are temporary and require documentation; must be resolved by the tenth week of the next semester.

Summary of Chapter Topics

Statistics I Chapter Sequence

• Ch. 1: Introduction to Data

• Ch. 2: Picturing Variation with Graphs

• Ch. 3: Numerical Summaries of Center and Variation

• Ch. 4: Regression Analysis: Exploring Associations Between Variables

• Ch. 5: Modeling Variation with Probability

• Ch. 6: Modeling Random Events: The Normal and Binomial Models

• Ch. 7: Survey Sampling and Inference

• Ch. 8: Hypothesis Testing for Population Proportions

• Ch. 9: Inferring Population Means

Example: In Chapter 3, students will learn to calculate measures of center (mean, median, mode) and variation (range, variance, standard deviation) using formulas such as:

• Sample Mean: $\bar{x} = \frac{1}{n} \sum_{i=1}^{n} x_i$

• Sample Variance: $s^2 = \frac{1}{n-1} \sum_{i=1}^{n} (x_i - \bar{x})^2$

• Sample Standard Deviation: $s = \sqrt{s^2}$

Additional info: The syllabus provides a comprehensive overview of the course structure, policies, and chapter topics, which align closely with the standard college statistics curriculum.