EC 1151.02: Statistics – Syllabus and Course Structure Overview

Course Overview

Introduction to Business Statistics

This course introduces students to fundamental statistical concepts and methods used in business and economics. The focus is on building statistical intuition, understanding technical notation, and applying statistical formulas to real-world problems. Students will learn to solve statistical problems, explain principles using technical notation, and communicate methods effectively to those with limited statistical knowledge.

• Statistical Intuition: Emphasis on understanding both the intuition and mathematics behind statistical methods.

• Applications: Concepts are applicable to business management, consulting, research, and non-profit work.

• Technical Notation: Students will use mathematical notation and statistical formulas throughout the course.

Course Structure and Grading

Grading Components

The course grade is determined by a combination of lab assignments, participation, quizzes, midterms, and a final exam. The breakdown is as follows:

Component	Weight (%)
Lab (including assignments)	15
Participation (problem sets, attendance)	8
10 Minute Quizzes	9
Midterm 1	17
Midterm 2 (cumulative)	17
Final Exam (cumulative)	34

• Lab: Weekly lab sessions focus on using STATA software and applying lecture concepts.

• Participation: Includes attendance, completion of graded problem sets, and performance on these sets.

• Quizzes: Short quizzes test understanding of recent lecture material.

• Exams: Closed book, with formula sheets and statistical tables provided.

Course Materials

Textbook and Software

• Textbook: Statistics for Business and Economics with MyStatLab (8th edition) by Newbold, Carlson, Thorne.

• MyStatLab: Online platform for graded problem sets and access to the electronic textbook.

• STATA: Statistical software used in lab sessions for data analysis.

Schedule and Topics

Weekly Topics and Chapters

The course covers a wide range of topics aligned with standard business statistics curriculum. Below is a summary of the main topics and their corresponding chapters:

Date	Topic	Chapter(s)
1/13	Summation Notation, Introduction	1.1-1.4
1/15	Regression to Mean, Descriptive Statistics: Central Tendency, Percentiles, Histograms, Weighted Means	1.5, 1.6, 6.1, 2.1, (2.3)
1/20	Variance, Standard Deviation, z-scores	2.2, 2.4
1/22	Covariance, Correlation, Probability Basics, Conditional Probabilities	2.4, 3.1-3.3
1/27	Conditional Probabilities, Independence, Venn Diagrams	3.1-3.4, 3.4-3.5
1/29	Statistical Independence, Bayes Rule	3.4, 3.5
2/3	Expected Values, Variances of Discrete Random Variables	4.1-4.3
2/5	Binomial Random Variables, Joint Distributions	4.4, 4.7, (4.5, 4.6)
2/10	Selection Bias, Regression to Mean, Multiple Tests, Correlation ≠ Causation	Ch 6 NS*
2/12	Continuous Random Variables: Uniform, Standard Normal Distribution	5.1, 5.2, 5.3, 5.6
2/19	Normal Distributions	5.3
2/24	Business Applications with Normal Distributions, Binomial as Normal	5.3, 5.4
2/26	Distribution of Sample Means, Central Limit Theorem	6.1, 6.2
3/10	Political Polls, Proportions	6.3
3/12	Hypothesis Testing, Two-tailed and One-tailed Tests	9.1, 9.2
3/17	Estimation: Single Population, Confidence Intervals	7.1, 7.2
3/19	Hypothesis Testing: t-distributions	7.3, 9.3
3/24	Hypothesis Testing: Large Sample or Proportions	7.4, 9.4
3/26	Sample Variances, χ² Distribution, Confidence Interval and Hypothesis Testing	6.4, 7.5, 9.6, 10.4
4/7	Multiple Populations: Distribution and Hypothesis Testing	Ch.8, Ch.10.1-10.3
4/9	Power of a Test	7.7, 7.8, 9.5, 10.5
4/14	Linear Models, Linear Regression	11.1-11.3
4/16	Least Squares Model, R-squared, Standard Errors	11.4-11.5
4/23	Log Functional Forms	Handout
4/28	Dummy Variables, Regression as Conditional Mean	Handout, 12.1, 12.3, 12.8
4/30	Multiple Regression, Partial Derivatives, Quadratic Forms	12.4, 12.7

DW* refers to Leonard Mlodinow’s The Drunkard’s Walk: How Randomness Rules Our Lives. NS* refers to Charles Wheelan’s Naked Statistics. Chapters from these books will be available on Canvas.

Key Topics Covered

Describing Data: Graphical and Numerical

• Graphical Methods: Histograms, percentiles, and visual summaries.

• Numerical Methods: Measures of central tendency (mean, median, mode), variance, standard deviation, z-scores.

Probability and Random Variables

• Probability Basics: Conditional probability, independence, Venn diagrams, Bayes Rule.

• Discrete Random Variables: Expected values, variances, binomial distribution, joint distributions.

• Continuous Random Variables: Uniform and normal distributions, standard normal distribution.

Sampling and Sampling Distributions

• Central Limit Theorem: Distribution of sample means, sample variances, χ² distribution.

• Proportions: Applications in political polls and business.

Estimation and Hypothesis Testing

• Estimation: Confidence intervals for single populations.

• Hypothesis Testing: One-tailed and two-tailed tests, t-distributions, large sample tests, power of a test.

Regression and Linear Models

• Simple Regression: Linear models, least squares, R-squared, standard errors.

• Multiple Regression: Dummy variables, partial derivatives, quadratic and log functional forms.

Course Policies and Support

Academic Integrity

• Strict policy against cheating; automatic failure and reporting for violations.

Accommodations

• Support for students with learning disabilities; advance notice and documentation required.

Illness Policy

• Accommodations for illness, including extensions and virtual office hours.

Summary Table: Major Course Topics and Chapters

Topic	Chapter(s)
Describing Data: Graphical	Ch. 1
Describing Data: Numerical	Ch. 2
Probability	Ch. 3
Discrete Random Variables and Probability Distributions	Ch. 4
Continuous Random Variables and Probability Distributions	Ch. 5
Sampling and Sampling Distributions	Ch. 6
Estimation: Single Population	Ch. 7
Hypothesis Testing: Single Population	Ch. 9
Hypothesis Testing: Additional Topics	Ch. 10
Simple Regression	Ch. 11
Analysis of Categorical Data	Ch. 14
Analysis of Variance	Ch. 15

Additional Info

• Lecture handouts, slides, and additional materials will be posted on Canvas.

• Optional video clips are provided to clarify terminology and link course topics.

• Post-class questions on MyStatLab offer practice on daily topics.