Course Overview: STA 2023 Statistical Methods

Introduction

STA 2023 Statistical Methods is a college-level course designed to introduce students to fundamental concepts and applications of statistics. The course emphasizes problem-solving, data interpretation, and the use of technology in statistical analysis. It is suitable for students across various disciplines and fulfills general education requirements.

Professor Information

• Professor: Joe Coll

• Email: jcoll2@valenciacollege.edu

• Phone: 407-582-3641

• Office Location: DPAC 340K

• Student Engagement Hours: Students can expect timely access to faculty outside of class, including via Canvas or other supplemental communication tools.

Course Information

• Credit Hours: 3

• Prerequisite: MAT0022C or higher, or appropriate score on assessment/state mandate

• Class Meeting Times: Tue/Thu 02:30 PM - 03:45 PM, DPAC252

Course Description

This course covers statistical methods with a focus on descriptive and inferential statistics. Students will learn to visualize and summarize data, apply probability concepts, and use statistical techniques to analyze and interpret data. The course is designed to increase problem-solving abilities and data interpretation through practical applications.

Course Outcomes

• Visualize and summarize data using descriptive statistics.

• Apply basic probability concepts to draw reasonable conclusions.

• Employ concepts of random variables, sampling distributions, and central limit theorem to analyze and interpret data.

• Choose appropriate methods of inferential statistics, including confidence intervals and hypothesis testing, to make broader decisions based on sample data.

• Model linear relationships between quantitative variables using correlation and linear regression.

Key Terms and Definitions

• Descriptive Statistics: Methods for summarizing and visualizing data, such as mean, median, mode, and graphical representations.

• Probability: The measure of the likelihood that an event will occur, often expressed as a number between 0 and 1.

• Random Variable: A variable whose value is subject to variations due to chance.

• Sampling Distribution: The probability distribution of a given statistic based on a random sample.

• 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.

• Inferential Statistics: Techniques for making generalizations from a sample to a population, including estimation and hypothesis testing.

• Confidence Interval: A range of values, derived from sample statistics, that is likely to contain the population parameter.

• Hypothesis Testing: A method for testing a claim or hypothesis about a parameter in a population, using sample data.

• Correlation: A statistical measure that describes the strength and direction of a relationship between two variables.

• Linear Regression: A method for modeling the relationship between a dependent variable and one or more independent variables.

Important Formulas

• Sample Mean:

• Sample Standard Deviation:

• Confidence Interval for Mean (Normal Distribution):

• Simple Linear Regression Equation:

• Correlation Coefficient:

Required Materials

• Textbook: Stats: Modeling the World - MyLab Statistics with Pearson eText 18 Week Access

• ISBN: 8220127572279

• Authors: De Veaux and Bock

• Publisher: Pearson Co

• Required/Recommended: Required

Assessments and Grading

• Attendance: 10%

• Homework/Quizzes: 30%

• Projects: 30%

• Midterm Exam: 15%

• Final Exam (Test & Term Project): 15%

Attendance Policy

Regular attendance and participation are required for success in this course. For online courses, attendance is determined by consistent engagement with course content. Absences should be communicated to the professor as soon as possible, and make-up work is at the instructor's discretion.

Academic Honesty

All forms of academic dishonesty are strictly prohibited. This includes plagiarism, cheating, furnishing false information, forgery, alteration or misuse of documents, misconduct during testing, and facilitating academic dishonesty. Violations may result in penalties such as loss of credit, reduction in final grade, or further disciplinary action.

Student Code of Conduct

Students are expected to conduct themselves responsibly and respectfully, contributing positively to the learning community. Disruptive behavior or interference with college processes is not tolerated.

Disclaimer Statement

Course policies, schedules, and requirements may change at the instructor's discretion to best support student learning. Students will be notified of any changes via class announcements or Canvas.