STAT 152: Introduction to Statistics – Syllabus and Course Structure Study Guide

Course Overview

Introduction to Statistics

This course provides a comprehensive introduction to the theory and practice of probability and statistics. It covers foundational concepts, descriptive and inferential statistics, graphical data representation, probability theory, estimation, hypothesis testing, correlation, and regression. The course utilizes real data sets and statistical software to illustrate concepts and applications.

• Course Title: STAT 152 – Introduction to Statistics

• Institution: University of Nevada, Reno

• Semester: Spring 2026

• Instructor: Tien Tran

• Class Schedule: MW 1:00 – 2:15 PM

• Location: DMSC 104

Course Description and Objectives

Key Concepts and Skills

The course emphasizes the language, essential ideas, and concepts of statistics. Students will learn to:

• Compute descriptive statistics and probabilities from data using correct statistical notation and language.

• Choose and apply appropriate statistical analysis or modeling methods to solve problems in various research fields.

• Use statistical tools to solve problems from different disciplines.

• Critically read and interpret quantitative information from diverse sources.

• Produce coherent, well-supported arguments demonstrating critical thinking, analysis, and decision making.

Course Materials

Required Texts and Tools

• Textbook: Essentials of Statistics (7th Edition) by Mario F. Triola (e-book included with MyStatLab).

• Software: StatCrunch (included with MyStatLab).

• Calculator: TI-30XA, TI-30X IIS, or any non-programmable calculator (cell phones/iPads not allowed).

• Online Access: Pearson MyLab & Mastering (MyStatLab) via Canvas.

Course Structure and Schedule

Weekly Topics and Assessments

The course is organized by weekly sections, each covering specific chapters and subtopics. Assessments include quizzes, tests, and a cumulative final exam. Recitation sessions are held weekly for review and practice.

Week	Sections / Tests	Quiz
1	Sections 1.1, 1.2, 1.3
2	Sections 2.1, 2.2, 2.3	Quiz-1
3	Sections 3.1, 3.2, 3.3	Quiz-2
4	Sections 4.1, 4.2, 4.3, 4.4	Quiz-3
5	Test-1 (2/18)
6	Sections 5.1, 5.2, 5.3	Quiz-4
7	Sections 6.1, 6.2	Quiz-5
8	Sections 6.3, 6.4, 6.5	Quiz-6
9	Sections 7.1, 7.2; Test-2 (3/18)
10	Spring Break
11	Sections 8.1, 8.2, 8.3	Quiz-7
12	Sections 9.1, 9.2	Quiz-8
13	Sections 10.1, 10.2	Quiz-9
14	Test-3 (4/22)
15	Sections 11.1, 11.2	Quiz-10
16	Final Exam Review; Final Exam (5/11, 12:45–2:45 PM)

Additional info: The schedule aligns with the standard statistics curriculum, covering all major topics listed in the chapter titles.

Assessment and Grading

Grading Components

Grades are determined by a combination of homework, quizzes, tests, and the final exam. Each component is designed to reinforce learning and provide multiple opportunities for feedback and improvement.

Component	Weight (%)
Homework	20
Quizzes	10
Test 1	15
Test 2	15
Test 3	15
Final Exam	25

Letter Grade Scale:

Grade	Percentage Range
A	93–100%
A-	90–92.9%
B+	87–89.9%
B	84–86.9%
B-	80–83.9%
C+	76–79.9%
C	69–75.9%
D+	65–68.9%
D	60–64.9%
F	< 60%

Course Topics (Based on Schedule and Syllabus)

Major Topics Covered

• Ch. 1: Introduction to Statistics

• Ch. 2: Exploring Data with Tables and Graphs

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

• Ch. 4: Probability

• Ch. 5: Discrete Probability Distributions

• Ch. 6: Normal Probability Distributions

• Ch. 7: Estimating Parameters and Determining Sample Sizes

• Ch. 8: Hypothesis Testing

• Ch. 9: Inferences from Two Samples

• Ch. 10: Correlation and Regression

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

Example: In Week 4, students will study probability theory, including basic probability rules, events, and applications. In Week 13, correlation and regression analysis will be covered, focusing on relationships between variables and predictive modeling.

Student Support and Resources

Academic Success Services

• Math Center, Tutoring Center, and University Writing Center are available for additional support.

• Office hours and recitation sessions provide opportunities for personalized assistance.

• Technical support for Pearson MyLab is available online and by phone.

University Policies

Academic Integrity and Accessibility

• Strict policies against academic dishonesty (cheating, plagiarism).

• Accommodations available for students with disabilities through the Disability Resource Center.

• Compliance with university conduct and classroom behavior standards.

• Policies regarding audio/video recording, campus safety, and mental health support.

Summary Table: Course Structure and Key Policies

Aspect	Details
Course Topics	Chapters 1–11 (see above)
Assessment	Homework, quizzes, tests, final exam
Support	Office hours, recitation, tutoring centers
Policies	Academic integrity, disability accommodations, classroom conduct

Additional Info

Additional info: The syllabus provides a structured overview of the course, aligning with the standard college statistics curriculum. While specific formulas and definitions are not included in the syllabus, students are expected to learn and apply concepts such as mean, median, mode, probability rules, normal distribution, hypothesis testing, and regression analysis throughout the course. For example, students will use formulas like:

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

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

• Normal Distribution: $f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{-\frac{(x-\mu)^2}{2\sigma^2}}$

• Correlation Coefficient: $r = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}}$

• Hypothesis Testing (Z-test): $Z = \frac{\bar{x} - \mu}{\sigma / \sqrt{n}}$

These formulas and concepts will be covered in detail in the respective chapters and sections as outlined in the course schedule.