Elementary Statistics (STAT C1000) Syllabus and Course Overview

Course Overview

Introduction to Elementary Statistics (STAT C1000)

This course provides a comprehensive introduction to the fundamental concepts and procedures of statistics, including descriptive and inferential statistics, probability theory, and statistical analysis using calculators and software. The course is designed to develop statistical thinking and application skills across various disciplines.

Course Content and Structure

Main Topics Covered

• Descriptive Statistics: Summarizing and presenting data using tables, graphs, and numerical measures.

• Probability: Basic probability theory and its applications.

• Probability Distributions: Including binomial, normal, t, chi-square, and F distributions.

• Estimation: Point and interval estimation for population parameters.

• Hypothesis Testing: Procedures for one and two populations, including tests for means and proportions.

• Correlation and Regression: Analyzing relationships between variables.

• ANOVA and Test of Independence: Analysis of variance and chi-square tests.

• Non-Parametric Tests: Introduction to alternative statistical methods when assumptions are not met.

Student Learning Outcomes (SLOs)

• Analyze data sets and present information using tables, graphs, and statistical calculations.

• Use estimation strategies to make inferences about population characteristics (mean, proportion, variation).

• Apply appropriate hypothesis tests to draw conclusions about population parameters.

Course Objectives

• Summarize and interpret data using standard statistical methods.

• Identify and evaluate methods of data collection.

• Analyze and interpret graphical data presentations.

• Calculate and interpret measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation).

• Solve basic probability problems and apply probability distributions.

• Distinguish between sample and population distributions; understand the Central Limit Theorem.

• Formulate and test hypotheses for one and two populations, including p-values and error types.

• Construct and interpret confidence intervals for means and proportions.

• Test for independence and equality of means using ANOVA and chi-square tests.

• Analyze correlation and regression, including finding and interpreting regression lines.

• Use calculators and statistical software for data analysis.

• Apply statistical techniques to real-world data from various disciplines.

Grading Policy

Component	Weight
Participation in Discussion Board	5%
Online Homework	5%
Online Quizzes	6%
Projects	4%
Exams (2 exams, 25% each)	50%
Final Exam	30%

Grading Scale: A: 90-100%, B: 80-89.9%, C: 70-79.9%, D: 60-69.9%, F: below 60%

Course Requirements and Policies

Textbook and Tools

• Textbook: Statistics: Informed Decision Using Data, 7th edition, by Michael Sullivan III.

• Calculator: TI-83 Plus or TI-84 Plus required (no cell phones or other devices allowed).

• Software: MyMathLab/MyStatLab access required for homework and quizzes.

Assignments and Exams

• Homework is completed online via MyLab&Mastering with interactive support features.

• Quizzes: 12 total (2 proctored, 10 unproctored), with varying time limits and attempts.

• Exams: 2 midterms and a comprehensive final, all proctored and closed book/notes.

• Group Project: Collaborative presentation and peer review required.

Participation and Attendance

• Active participation in online discussions is required (minimum six posts per week).

• Regular login and assignment submission are necessary to avoid being dropped from the course.

Entry and Exit Level Skills

Entry Level Skills

• Solve linear and non-linear equations

• Simplify numerical expressions and use order of operations

• Plot and interpret points and lines on the Cartesian plane

• Translate verbal problems into mathematical forms

• Evaluate exponential functions and sigma notation

• Solve inequalities and use interval notation

Exit Level Skills

• Describe data sets statistically

• Apply probability laws and distributions

• Formulate and test hypotheses

• Estimate parameters and analyze correlation/regression

• Use statistical calculators/software

• Critically evaluate statistical claims

Academic Honesty and Student Support

• Strict adherence to academic honesty policies is required; violations may result in disciplinary action.

• Support for students with disabilities is available through the Center for Students with Disabilities.

• Tutoring is available both on-ground and online through the Math Lab and Brainfuse.

Additional Information

• Withdrawal deadlines vary; check your student account for specific dates.

• Extra credit is not available for this course.