This introductory course in data analysis and statistical inference requires no background in statistics. Its objective is to provide individuals who aspire to enter IT management positions with the basic statistical tools for analyzing and interpreting data. The course is divided into three distinct modules: descriptive statistics, statistical inference, and regression analysis. The emphasis of the classes on descriptive statistics is the calculation and interpretation of summary statistical measures for describing raw data. The sessions on statistical inference are designed to provide you with the background for executing and interpreting hypothesis tests and confidence intervals. The final component of the course focuses on regression analysis, a widely used statistical methodology. Throughout the course you will regularly analyze data relevant to IT management using the statistical software package Minitab.

Upon completion of this course, the student will be able to:

- Apply techniques for analyzing and interpreting data to real-world datasets relevant to IT management.
- Perform and interpret elementary statistical inferences (such as confidence intervals and hypothesis tests) both by hand and using the statistical software package Minitab.
- Analyze real data relating to online pricing and software cost estimation and describe the analysis results and conclusions.

Section A: Wednesdays, 9:00-11:50am, Hamburg Hall 1001

Section B: Mondays and Wednesdays, 3:30-4:50pm, Hamburg Hall 1502

Section C: Tuesdays, 5:30-8:20pm, Baker Hall 136A

Review sessions (all sections): Fridays, 3:30-4:50pm, Doherty Hall 2210

Statistics for Business and Economics, 11th Edition

(McClave, Benson, and Sincich)

*** It's fine to use the 9th or 10th Edition instead, but the section numbers may be slightly different! ***

Module II: Hypothesis Testing and Inference (pdf)

Module III: Regression (pdf)

Three homework assignments: 30% (10% each)

Two mini-projects: 30% (15% each)

Final exam: 40%

The mini-projects will be done in 2-person teams and will involve the analysis of real data relating to online pricing and software cost estimation. In your 2-3 page mini-project reports you will describe the results of your analyses and your conclusions regarding the issues outlined in the assignment. Teams will be self-selected, and both team members will receive the same grade. The final exam will be held on Wednesday, October 17th from noon-1:20pm. Rooms will be: Section A: HBH 1001, Section B: HBH 1502, Section C: HBH 1000.

**Cheating Policy:** We encourage discussion among teams about the mini-projects and among individual students on homework assignments. However, the project and that is submitted for grading must be the work of the 2-person team alone. Similarly, completed homework assignments must be your work alone. Specifically, discussion of results that are identical or nearly identical across projects will be regarded as cheating. Also, your answers on the final exam must reflect your work alone. Sanctions for cheating include lowering your grade including failing the course. In egregious instances, the instructors may recommend the termination of your enrollment at CMU.**Late Work Policy:** You are expected to turn in all work on time (at the **start** of class on the due date). Because we understand that exceptional circumstances may arise, each student will be permitted to turn in one assignment (homework or mini-project) up to 48 hours late. Any other late assignments will not be accepted.**Re-grade Policy:** Any requests for a re-grade must be submitted *in writing* to the course instructor within seven (7) days from when the graded assignment is returned. You must include a clear written explanation of why the regrade is necessary, stapled to your graded assignment. If a re-grade request is submitted, we may re-grade your entire assignment, which may either raise or lower your score. In general, we will only raise scores in cases where we have made an error in grading, and all decisions on re-grade requests are final.**E-mail Questions Policy:** To balance the workload fairly among the teaching assistants, and to ensure a reasonable response time for questions received via e-mail, each student has been assigned one TA as a "first contact". This is the person that you should e-mail first with any questions that you might have regarding the course. We will do our best to answer questions within 24 hours, or 48 hours on weekends. If you do not receive a response within this time, or if your first contact is unable to resolve your question, then you should feel free to e-mail another TA or the instructor.

Mini-project 1 due Wednesday 9/12 (Sections A & B), or Tuesday 9/11 (Section C)

Homework 1 due Wednesday 9/19 (Sections A & B), or Tuesday 9/18 (Section C)

Homework 2 due Wednesday 10/3 (Sections A & B), or Tuesday 10/2 (Section C)

Homework 3 due Wednesday 10/10 (Sections A & B), or Tuesday 10/9 (Section C)

Mini-project 2 due Monday 10/15 at **2:15pm **(all sections)

**Lecture 1: Descriptive statistics(Section A: Wed. 8/29; Section B: Mon. 8/27; Section C: Tues. 8/28)**

Course overview

What is statistics (1.1-1.3)

Types of data (1.5)

Random sampling (1.6)

Histograms (2.2)

Measures of central tendency (2.4)

Measures of variability (2.5-2.6)

Box plots (2.8)

Using Minitab for descriptive statistics

(Sections A/B: Wed. 8/29; Section C: Tues. 8/28)

Basic probability (3.1-3.4)

Conditional probability (3.5-3.6)

Bayes' Theorem (3.8)

Discrete random variables (4.1-4.3)

(All sections: Fri. 8/31)

Minitab and example problems.

(Sections A/B: Wed. 9/5; Section C: Tues. 9/4)

(Tuesday class will meet from 5:30-6:50pm; Wednesday morning class will meet from 9:00-10:30am)

Continuous random variables (4.5)

The Uniform distribution (4.9)

The Normal distribution (4.6)

(All sections: Fri. 9/7)

Minitab, example problems, and questions on mini-project 1.

(Section A: Wed. 9/12; Section B: Mon. 9/10, Section C: Tues. 9/11)

The Normal distribution, continued (4.6)

Sampling distributions (4.10)

Central Limit Theorem (4.11)

Using Minitab for random variables and sampling

**Lecture 5: Confidence intervals(Sections A/B: Wed. 9/12; Section C: Tues. 9/11) **

Large-sample confidence intervals for the mean (5.2)

Small-sample confidence intervals for the mean (5.3)

Large-sample confidence intervals for the population proportion (5.4)

Determining the sample size (5.5)

Using Minitab for confidence intervals

(All sections: Fri. 9/14)

Minitab, example problems, and questions on HW 1.

(Section A: Wed. 9/19; Section B: Mon. 9/17, Section C: Tues. 9/18)

Introduction to hypothesis testing (6.1-6.2)

Large-sample hypothesis tests for the mean (6.3)

Small-sample hypothesis tests for the mean (6.5)

Large-sample hypothesis tests for the population proportion (6.6)

(Sections A/B: Wed. 9/19; Section C: Tues. 9/18)

Type I and Type II errors (6.1)

p-values (6.4)

Using Minitab for 1-sample hypothesis testing

(All sections: Fri. 9/21)

Minitab, example problems, and questions on HW 2.

(Section A: Wed. 9/26; Section B: Mon. 9/24, Section C: Tues. 9/25)

Comparing two population means (7.2)

Comparing two population proportions (7.4)

Using Minitab for 2-sample hypothesis testing

(Sections A/B: Wed. 9/26; Section C: Tues. 9/25)

Comparing two population means using paired differences (7.3)

Using Minitab for paired differences

(All sections: Fri. 9/28)

Minitab, example problems, and questions on HW 2.

**Lecture 10: Simple regression(Section A: Wed. 10/3; Section B: Mon. 10/1, Section C: Tues. 10/2) **

Linear models (10.1)

Least squares linear regression (10.2)

Estimating the error of the model (10.3)

Making inferences using the model (10.4)

(Sections A/B: Wed. 10/3; Section C: Tues. 10/2)

Coefficients of correlation and determination (10.5)

Using the model for estimation and prediction (10.6)

Using Minitab for simple regression

(All sections: Fri. 10/5)

Minitab, example problems, and questions on HW 3.

(Section A: Wed. 10/10; Section B: Mon. 10/8, Section C: Tues. 10/9)

Multivariate linear models (11.1)

Least squares linear regression (11.2)

Estimating the error of the model (11.2)

Multiple coefficient of determination (11.3)

Making inferences using the model (11.3)

(Sections A/B: Wed. 10/10; Section C: Tues. 10/9)

Using the model for estimation and prediction (11.4)

Advanced topics in model building (11.5-11.7)

Using Minitab for multiple regression

(All sections: Fri. 10/12)

Review for final exam, and questions on mini-project 2.

**Final exam: Wednesday October 17, noon-1:20pm.**