Course Overview

Introduction to Statistics I

This course provides a comprehensive introduction to statistics, focusing on both theoretical and applied aspects. Students will learn to collect, organize, analyze, and interpret data, as well as understand probability, distributions, and inferential statistics. The course is designed to build quantitative literacy and prepare students for further study or practical application in various fields.

• Credit Hours: 3

• Prerequisites: Completion of MTH 152, MTH 154, MTH 158, MTH 161, or MTH 163 with a grade of C or better, or placement.

• Required Text: Sullivan, Statistics 7th edition (electronic access via Pearson Mylabs)

• Course Site: Canvas

Course Learning Outcomes

Key Competencies

Upon successful completion, students will be able to:

• Construct and interpret graphical displays and numerical analyses appropriate to the type of data presented.

• Recognize and explain key concepts related to experimental design.

• Calculate probabilities using both relative frequency and theoretical interpretations, including the Law of Large Numbers.

• Calculate probabilities for discrete and continuous random variables using appropriate distributions.

• Apply the Central Limit Theorem to describe sampling distributions.

• Perform statistical inference for one and two samples.

• Create, interpret, and test models for correlation and regression analysis.

• Demonstrate proficiency with appropriate statistical software.

Topics Covered in Statistics I

Major Topics and Subtopics

• Types of Data: Qualitative vs Quantitative, Levels of Measurement

• Graphical and Numerical Analysis: Tables, Graphs, Measures of Center and Dispersion

• Sampling Requirements and Methods: Random, Stratified, Cluster, Systematic Sampling

• Observational Studies and Experiments: Experimental Design, Control Groups, Randomization

• Types of Probability: Empirical, Theoretical, Subjective

• Rules of Probability: Addition Rule, Multiplication Rule, Conditional Probability

• Probability Distributions: Binomial, Normal, t-distributions

• Law of Large Numbers, Empirical Rule, Central Limit Theorem

• Confidence Intervals: For one and two samples

• Hypothesis Testing: For proportions and means, one and two samples

• Correlation and Regression: Linear correlation, Least squares regression

Course Schedule Overview

Weekly Topics

Expanded Academic Context

Key Definitions and Concepts

• Descriptive Statistics: Methods for summarizing and organizing data, including graphical and numerical techniques.

• Probability: The study of randomness and uncertainty; quantifies the likelihood of events. Formula:

• Probability Distributions: Functions that describe the likelihood of different outcomes for random variables. Binomial Distribution Formula:

• Normal Distribution: A continuous probability distribution characterized by its bell-shaped curve. Formula:

• Central Limit Theorem: States that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's distribution.

• Confidence Interval: An estimated range of values likely to include a population parameter. Formula for mean:

• Hypothesis Testing: A method for making decisions about population parameters based on sample data. Example: Testing whether the mean of a population equals a specified value.

• Correlation: Measures the strength and direction of a linear relationship between two variables. Formula:

• Regression: Models the relationship between variables, often using the least squares method to fit a line. Formula:

Assessment and Grading

Grade Distribution

Course Policies and Student Support

Attendance, Late Work, and Academic Integrity

• Attendance: Required for all scheduled meetings; excessive absences may result in withdrawal.

• Late Work: 10% deduction for late assignments; no work accepted more than 2 weeks late.

• Academic Integrity: Plagiarism and cheating are strictly prohibited; violations may result in penalties including course failure.

Student Resources

• Library: Access to research materials and study support.

• Canvas Support: 24/7 technical and course support.

• Academic Support Services: Tutoring, writing centers, and math labs.

• Telehealth Support: TimelyCare for mental health and well-being.

Summary Table: Major Statistical Topics

Additional Info

• Students are expected to demonstrate quantitative literacy and apply statistical reasoning in various contexts.

• Proficiency with statistical software (e.g., Pearson Mylabs) is required.

• Course policies, emergency procedures, and student support resources are detailed in the syllabus and should be reviewed regularly.