STA 2023 Elementary Statistics – Syllabus and Course Structure Study Guide

STA 2023 Elementary Statistics – Syllabus and Course Structure Study Guide

Course Overview

This course, STA 2023 Elementary Statistics, provides an introduction to the fundamental concepts and methods of statistics. Students will learn to collect, analyze, interpret, and present data using both descriptive and inferential statistical techniques. The course is designed for students from a variety of disciplines and fulfills the Mathematics General Education Core requirement.

Course Objectives

• Visualize and summarize data using descriptive statistics, including graphical displays and measures of central tendency, dispersion, and relative standing.

• Apply probability concepts to draw reasonable conclusions, including basic terminology, rules, and calculation of probabilities for simple and compound events.

• Employ concepts of random variables, sampling distributions, and the Central Limit Theorem to analyze and interpret data representations.

• Choose and apply inferential statistical methods such as confidence intervals and hypothesis testing to make decisions based on sample data.

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

Course Curriculum and Topics

The course is structured into modules that align with the following key chapters and topics:

• Ch. 1 – Introduction to Statistics

  • Statistical and critical thinking

  • Types of data

  • Collecting sample data

• Ch. 2 – Exploring Data with Tables and Graphs

  • Frequency distributions

  • Histograms

  • Graphical representations (scatterplots, misleading graphs)

  • Correlation and regression basics

• Ch. 3 – Describing, Exploring, and Comparing Data

  • Measures of center (mean, median, mode)

  • Measures of variation (range, variance, standard deviation)

  • Measures of relative standing (z-scores, percentiles, boxplots)

• Ch. 4 – Probability

  • Basic probability concepts

  • Addition and multiplication rules

  • Complements, conditional probability, Bayes' Theorem

  • Counting principles

• Ch. 5 – Discrete Probability Distributions

  • Probability distributions

  • Binomial probability distributions

• Ch. 6 – Normal Probability Distributions

  • The standard normal distribution

  • Applications of normal distributions

  • Sampling distributions and estimators

  • The Central Limit Theorem

• Ch. 7 – Estimating Parameters and Determining Sample Sizes

  • Estimating population proportions and means

• Ch. 8 – Hypothesis Testing

  • Basics of hypothesis testing

  • Testing claims about proportions and means

• Ch. 10 – Correlation and Regression

  • Correlation analysis

  • Simple linear regression

• Ch. 11 – Chi-Square and Analysis of Variance

  • Goodness-of-fit tests

  • Contingency tables

Course Materials and Technology

• Required Textbook: Essentials of Statistics (7th Edition) by Mario F. Triola, Pearson (2023). Access is provided via MyLab Statistics with Pearson eText.

• Calculator: A basic scientific calculator is required (e.g., TI-30+), but a TI-83 or TI-84 with statistical functions is preferred. CAS calculators (e.g., TI-89, TI-Nspire) and smartphone apps are not allowed.

• Technology: Consistent access to a computer with webcam and microphone, high-speed internet, and the ability to use MyLab Statistics and Canvas is required.

Assessment and Grading

• Grading Components:

  • Lecture Preparation, Video Lecture Quizzes, Attendance, In-Class Problem Sets, Homework, Quizzes, Tests, Reviews, Practice Exams, and Proctored Exams.

  • Proctored Exams (Midterm and Final) constitute the majority of the grade (70%).

  • Grading Scale: A (90+), B (80–89.99), C (70–79.99), D (60–69.99), F (≤59.99).

• Policies:

  • No extra credit assignments.

  • Partial credit may be awarded for corrected errors with explanation.

  • Late work and extensions are governed by documented policies (see syllabus for details).

Course Structure and Assignments

• Textbook Readings and PowerPoints: Prepare students for homework and quizzes.

• Lecture Preparation: Due before class to encourage engagement.

• Problem Sets: In-class assignments to reinforce lecture material.

• Homework: Weekly assignments with multiple attempts allowed.

• Quizzes: Timed, two attempts per quiz, closed book/notes.

• Tests and Exams: One attempt per test/exam, proctored, closed book/notes, formula sheets provided.

• Study Plan: Supplemental resource for additional practice and mastery.

Academic Integrity and Conduct

• Academic Honesty: Cheating, plagiarism, collusion, and improper use of electronic devices are strictly prohibited. Violations may result in severe sanctions, including course failure.

• Generative AI Policy: Use of AI tools (e.g., ChatGPT) is not permitted for any coursework.

• Code of Conduct: Students are expected to maintain professionalism, respect, and courtesy in all communications and classroom interactions.

Support and Resources

• Instructor and Department Contacts: Provided for academic and administrative support.

• Learner Support: Free tutoring, accessibility services, academic support, and student services are available.

• Technology Support: Technical assistance is available through the SPC Technical Support Center.

Course Schedule and Participation

• Assignment Release: Assignments are released weekly; due dates are specified in the Course Assignment Schedule.

• Attendance: Active participation is required. Excessive unexcused absences or incomplete assignments may result in withdrawal.

• Withdrawal Policy: Students are responsible for understanding the impact of withdrawal on academic standing and financial aid.

Summary Table: Major Course Topics and Assessment Alignment

Chapter/Module	Main Topics	Assessment Types
Ch. 1	Introduction to Statistics, Types of Data, Collecting Data	Lecture Prep, Homework, Quiz
Ch. 2	Tables, Graphs, Scatterplots, Correlation	Lecture Prep, Homework, Quiz
Ch. 3	Describing and Comparing Data	Lecture Prep, Homework, Quiz
Ch. 4	Probability Concepts and Rules	Lecture Prep, Homework, Quiz
Ch. 5	Discrete Probability Distributions	Lecture Prep, Homework, Quiz
Ch. 6	Normal Distributions, Central Limit Theorem	Lecture Prep, Homework, Quiz
Ch. 7	Estimating Parameters, Sample Sizes	Lecture Prep, Homework, Quiz
Ch. 8	Hypothesis Testing	Lecture Prep, Homework, Quiz
Ch. 10	Correlation and Regression	Lecture Prep, Homework, Quiz
Ch. 11	Chi-Square, ANOVA	Lecture Prep, Homework, Quiz

Key Formulas and Concepts (Examples)

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

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

• Binomial Probability: $P(X = k) = {n \choose k} p^k (1-p)^{n-k}$

• Normal Distribution (Z-score): $z = \frac{x - \mu}{\sigma}$

• Confidence Interval for Mean (\(\sigma\) known): $\bar{x} \pm z^* \frac{\sigma}{\sqrt{n}}$

• Hypothesis Test Statistic (for mean): $z = \frac{\bar{x} - \mu_0}{\sigma/\sqrt{n}}$

Tips for Success

• Attend all classes and participate actively.

• Complete assignments ahead of deadlines to allow time for questions and review.

• Utilize all available resources, including tutoring and instructor office hours.

• Practice academic honesty and integrity in all coursework.

• Familiarize yourself with the technology and tools required for the course.