STAT 145-04: Introduction to Statistics I – Course Syllabus and Study Guide

Course Overview

Introduction to Statistics I (STAT 145-04)

This course provides an introduction to statistical methods for extracting meaning from data and basic inferential statistics. The curriculum emphasizes statistical thinking, data integrity, exploratory data analysis, data visualization, numeric summary measures, probability distributions, sampling distributions, confidence intervals, and hypothesis testing. Statistical software is used to facilitate analysis.

• Credits: 3

• Schedule: Monday, Wednesday, Friday, 9:00–9:50 am

• Location: GOS 2154

• Instructor: Prof. Kathryn Graf

• Textbook: Statistics: Informed Decisions Using Data by Sullivan, 7th edition

• Technology: Minitab (free through RIT)

Course Objectives

• Introduce basic statistical concepts and methods

• Provide hands-on usage of computers as tools for data analysis

• Distinguish between inferential statistics and data mining

Learning Outcomes

• Demonstrate knowledge of definitions, concepts, rules, vocabulary, and notation of statistics

• Perform basic statistical calculations using software

• Use statistical measures and displays to describe data sets

• Formulate and perform simple hypothesis tests and state conclusions

• Explain challenges posed by extremely large data sets

• Interpret results of statistical inference and data analysis in context

Course Topics (Mapped to Introductory Statistics Chapters)

• 1. Intro to Stats and Collecting Data: Data integrity, definitions, and collection methods

• 2. Describing Data with Tables and Graphs: Exploratory data analysis, data visualization

• 3. Describing Data Numerically: Numeric summary measures

• 4. Probability: Introduction to probability concepts

• 5. Binomial Distribution & Discrete Random Variables: Probability distributions

• 6. Normal Distribution and Continuous Random Variables: The normal distribution

• 7. Sampling Distributions & Confidence Intervals: Mean: Sampling distributions, confidence intervals

• 8. Sampling Distributions & Confidence Intervals: Proportion: Proportion inference

• 9. Hypothesis Testing for One Sample: Hypothesis testing basics

• 10. Hypothesis Testing for Two Samples: Comparative inference

Additional info: The syllabus does not explicitly mention correlation, regression, chi-square tests, or ANOVA, but these may be covered in later chapters or as part of inferential statistics.

Course Structure and Evaluation

Grading Breakdown

Component	Weight
Exams (2)	40%
Final Exam	20%
Quizzes	10%
Team Activities	20%
Homework	10%
Total	100%

Final Grading Scale

Grade	Percentage
A	93-100
A-	90-92.9
B+	87-89.9
B	83-86.9
B-	80-82.9
C+	77-79.9
C	73-76.9
C-	70-72.9
D	60-69.9
F	Below 60

Course Policies

• Attendance & Participation: Required; notify instructor if absent

• Homework: Due weekly; late penalty applies

• Quizzes: Weekly, open-book; lowest two grades dropped

• Team Activities: Collaborative assignments; lowest two grades dropped

• Exams: Two midterms and a cumulative final; make-up policy applies

• Academic Integrity: All work must be your own; violations result in a grade of 0

• ADA Statement: Accommodations available for students with disabilities

Support Resources

• Instructor office hours and email

• Academic Success Center

• Sol Study Center

• Bates Study Center

• NTID STATS Tutor

Tentative Schedule

The course schedule follows a logical progression through introductory statistics topics, aligning with the standard curriculum:

• Weeks 1–4: Data collection, description, visualization, and numeric summaries

• Weeks 5–6: Probability, distributions, and exam review

• Weeks 7–9: Sampling distributions, confidence intervals, hypothesis testing

• Weeks 10–13: Advanced hypothesis testing, review, and assignments

• Weeks 14–16: Final review and exams

Course Logo

The course is affiliated with RIT, as indicated by the logo below.