Course Description | Objectives | Format | Grading | Books & Course Materials | Exams | Honor Code |Course Outline

Professor: Sarv Devaraj
Department of Management
Office: 377 COBA
Office Hours: Tu Th, 4:00-5:00 p.m.
Phone: 631-5074
E-Mail: sdevaraj@nd.edu
FAX: 631-5255

Lecture Room: 162 COBA  

I. COURSE DESCRIPTION

Introduction to probabilistic and statistical techniques for decision making, including inferential statistics, hypothesis tests, analysis of variance, regression analysis, and statistical quality control. Using computer software and data in statistical analysis. Emphasis on formal modeling and the use of data for managerial decision making and problem solving.  

II. COURSE OBJECTIVES

Upon completion of this course you should be able:

1. To use basic techniques of inferential data analysis, quality control, and regression modeling; 2. To use software(Microsoft Excel) to analyze data for decision making;

3. To analyze a set of data, to reach a conclusion based on these analyses, and to make and defend a recommended course of action;

4. To be well-equipped to take courses in Marketing, Investments, Accounting, Finance, and Operations Management that require proficiency in statistical methods.  

III. COURSE FORMAT

Class time will be devoted to lecture and discussion. A part of your final grade is dependent on useful and meaningful class participation. You are encouraged to ask questions. A portion of the class time will also be devoted to familiarize you with Microsoft Excel. All class projects require the use of this software.

IV. GRADING AND OTHER REQUIREMENTS

Cases:            25%
Participation:  10%
Problem Sets: 15%
Midterm:        25%
Final:             25%

The midterms and the final exam will be closed-book and closed-notes. All necessary tables will be included in the exams.

The projects will be used to test your ability to apply statistical tools learned in class to real life business situations.  

V. TEXTBOOK AND COURSE MATERIALS  

Anderson, Sweeney, and Williams, Statistics for Business and Economics, Seventh Edition, West Publishing Co., available at Hammes Bookstore.
 
  Devaraj, "MGT 500: Statistics, " Lecture Notes available at LaFortune Copy Center      

VI. EXAMS

Midterm Exam :

Final Exam:   

 VII. ACADEMIC HONOR CODE

All students are expected to abide by the Honor Code Specifically,

1. Students are expected to work on homework problems and projects individually. You may, however, discuss these with your colleagues. Exams are closed-book closed notes, and to be taken on the dates mentioned.      

VIII. COURSE OUTLINE  

DESCRIPTIVE STATISTICS    

TOPIC: Introduction, Descriptive Statistics

- Descriptive and Inferential Statistics
- Types of measurements
- Descriptive Statistics(Using Graphs)
- Descriptive Statistics(Using Numbers)
- Measures of location, variability, and relative standing
 

READINGS: Chapters 1-3
   

PROBABILITY AND SAMPLING THEORY    

TOPIC: Probabilities, Distributions, and Decision Making

- Applications, and Rules

- Conditional probability

- Discrete Distributions(Binomial, Poisson, Hypergeometric, Geometric)

- Continuous distributions

- Normal and Standard Normal distributions

READINGS: Chapters 4-6
 

TOPIC: Sampling Distributions

- Sampling distribution parameters

- Central Limit Theorem

- Applying sampling distribution theory

READINGS: Chapter 7
 
 

CONFIDENCE INTERVALS & HYPOTHESIS TESTING

TOPIC: Estimation and Hypothesis Testing- ?

- point estimators

- interval estimation of ? using z

- t-distribution

- interval estimation of ? using t

- Hypothesis testing, p-values

READINGS: Chapters 8-9
 

TOPIC: Estimation of Population Proportion: p

- Point, and interval estimation

- sampling distribution of sample proportion

- Computing samle sizeREADINGS: Chapter 10

 

STATISTICAL PROCESS/QUALITY CONTROL
   

TOPIC: On-line Quality Control

- Common causes and Special causes of variation

- X-Bar Chart

- R Chart

- P Chart

READINGS: Chapter 20  

 

TWO-SAMPLE TESTS AND ANOVA    

TOPIC: Comparing Two Population Means(Confidence Intervals)

Comparing Three or More Population Means(ANOVA)

- Dependent and Independent populations

- Analysis of Variance

- Bonferroni Multiple Comparisons

READINGS: Chapters 10 and 13  

 

REGRESSION ANALYSIS  

TOPIC: Simple and Multiple Linear Regression

- Relationship between two(simple), three or more(multiple) variables
 
 

- Model estimation

- Model Inference

* model assumptions

* model validation: t-Tests

* R2

* analysis of variance

* model validation: global F test

- Model Checking

* Error Distribution: Zero Mean, Normality, Independence

* heteroscedascity(non-constant variance)

* Multicollinearity

* Effect of outliers
 
 

- Model Use

* Description, Estimation and Prediction

- Model Building

* variable selection: R2, MSE

* general procedures(STEPWISE)

* model comparisons

- Using Qualitative Independent Variables

- Caveat (Causality)
 
 

READINGS: Chapter 14-16

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