CALIFORNIA STATE UNIVERSITY, SACRAMENTO
School of Business Administration
Department of Management Information Science
MIS 131 - Data Analysis for Managers, Sections (3, 13)
Syllabus, Fall 2005
INSTRUCTOR: Dr. Darrell D. Freeman
OFFICE: TAH –2091
E-MAIL freemand@csus.edu
OFFICE PHONE: 278-7147(Voice Mail)
OFFICE HOURS: MW 3:00-4:30, TTh 4:30-6:00
WWW HOMEPAGE: http://www.csus.edu/indiv/t/freemand
REQUIRED TEXT
Hopfe and Taylor Notes. Go to web site to download.
Suggested reference, Business Statistics, Downing and Clark, Barron’s Business Review Books
On Reserve:
Statgraphics Manuals: Examples: YC 6177; Reference: YC 6178
PREREQUISITES
MIS 1ABC, Stat 1, Math 23/4.
COURSE OBJECTIVES
The objectives of this course are to prepare the students to:
1. Understand variation. Be able to differentiate between specific variation and common variation, as applied
to managerial decision-making situations.
2. Develop an intuitive feel for statistical techniques.
3. Use statistical software [STATGRAPHICS] and interpret computer outputs.
4. Communicate, verbally and in written form, statistical results.
5. Understand the important characteristics of forecasting.
6. Create and manipulate data files in order to conduct appropriate statistical analysis.
CATALOG DESCRIPTION
The current catalog description is as follows: A second course in business statistics that focuses on the application of
statistical methods to business problems. Emphasis is placed on case studies, data analysis, model building
techniques, statistical reasoning, and communications of statistical results. A statistical computer package
[StatGraphics for Windows v 5.0] will be used in the course. Prerequisites: MIS 1ABC, MATH 23 and STAT 1. 3
units.
COURSE OVERVIEW
This course is designed to provide students with the background to execute data analysis in a managerial capacity.
The key factors to accomplish this are obtaining an intuitive feel for statistical modeling and the ability to utilize the
computer via statistical software [StatGraphics for Windows v 5.0]. Emphasis is placed on statistical thinking, not
memorization of statistical facts.
To be successful in this course, you should be:
1. PREPARED -- You should study the assigned pages before coming to class.
2. PRESENT -- You are expected to attend class. [If you miss class, you are responsible for obtaining class
assignments, handouts, and a copy of another student’s notes.]
Homework Assignments will be given via Dr. Freeman’s WWW homepage. Assignments will be discussed in class,
so be prepared to discuss your results.
EXAMS
The exams will require using the computer to analyze data. Make-up exams are not given. Check the course outlines
for the specific dates. Unless there is a justifiable excuse, * missing an exam will result in a score of 0.
Practice exams will be posted on Dr. Freeman’s www homepage.
*Dr. Freeman determines what is justifiable.
PROJECT AND CLASS PRESENTATION
Students will be assigned a team project. Written projects will be due Nov. 21(section 3) or Nov.22 (section 13).
Details of the project, posted on the WWW, will be discussed in class Oct 5(section 3) or Oct.6 (section 13).. If one
fails to submit a proposal on, or before Oct 25(section 3) or Oct.6 (section 13), then they will receive a score of 0 for
the project. Project oral presentations will take place Oct 5(section 3) or Oct.6 (section 13).
RECOMMENDED MATERIALS
1. STATGRAPHICS PLUS for Windows ver 5.0 software. Details regarding the distribution of the software
will be provided in class.
2. Diskette (data). 3.5"
GRADING POLICY
Your final grade will be based on your performance on 2 exams and 1 project. The course components are indicated
below
Exam 1
100 Points
Exam II
100 Points
Final (optional)
200 Points
Written Project*
80 Points
Project – Oral Presentation
20 Points
1. *Abstract must be approved in order to receive any credit for the project.
2. Minimal grades are assigned in accordance with the following percentages:
A = 93 - 100%
C = 73 - 76%
A- = 90 - 92%
C- = 70 - 72%
B+ = 87 - 89%
D+ = 67 - 69%
B = 83 - 86%
D = 63 - 66%
B- = 80 - 82%
D- = 60 - 62%
C+ = 77 - 79%
F = < 60%
3. *The scale may be lowered but not raised.
4. Incomplete (I) is not awarded in this class. Extra credit exercises, or projects, are not assigned nor will they be
accepted for credit.
ACADEMIC DISHONESTY
The attempt by a student to cheat on an exam or other academic assignments or engage in plagiarism is a violation
of a fundamental principle of academic honesty and integrity and will not be tolerated in the University. Penalties
will be imposed on students who are found guilty of academic dishonesty. The Dean of Students will be notified
and, at a minimum, a student guilty of academic dishonesty will be awarded the letter grade of "F" in this course.
TENTATIVE SCHEDULE
In the schedule shown below the class locations are as follows:
Section 3, All classes meet in Alpine 224 computer lab.
Section 13, Meet Tuesdays in Eureka 107 and Thursdays in Mendocino 2004 except 9/29 meet in AIRC 1013
Section 3
Section 13
Topic
Text Pages
8/29
8/30
Introduction, Mini Case
8/31
9/1
Review, Mean and Variance, Parameters v. Statistics,
pp 6-9
Random Variables, Sampling, Sampling Distribution
9/7
9/6
Normal Distribution, Standardization, Confidence
pp 6-9
Intervals, Hypothesis Testing, P Value
9/12
9/8
Introduction to Statgraphics Plus for Windows 5.0
See tutorial on WWW
9/14
9/13
Review, Go Over HW #1
HW #1 on WWW
9/19
9/15
Crosstabs
9/21
9/20
Variation--Specific v. Common , Data Types--Time
pp 10-11
Series v. Cross Sectional
9/26
9/22
Groups Created, Statistical Quality Control
pp 116-136
9/28
9/27
Quality Control
pp 14-27
10/3
9/29
Transformations, Random Walk
pp 28-31
10/5
10/4
Model Building--3 Phases, Simple Linear Regression, pp 32-42
Forecasting, Market Model
10/10
10/6
Case #1
10/12
10/17
10/19
10/11
10/13
10/18
Review
Exam #1
Introduction to Multiple Regression, Dummy Variables, WWW
Project Assigned/Discussed
Residuals, Correlation, Autocorrelation
pp 50-58, pp 74-77
Least Squares Estimation, Residuals, Correlation,
pp 50-58, pp 74-77
Autocorrelation, Project Proposals Due
10/24
10/26
10/20
10/25
10/31
11/2
11/7
11/9
11/14
11/16
11/1
11/3
11/8
11/10
11/17
Go over Project Proposals
Case # 2, Cross correlation, FRED CASE
Multicollinearity, Outliers, Forecasting
Case #3, Review
Exam II…
11/21
11/23
11/28
11/30
12/5
12/7
12/12
11/22
11/15
11/29
12/1
12/6
12/8
12/13
Go over Exam II, Sampling
ANOVA--1 Way, 2-Way
ANOCOVA
Projects Due, Presentations
Case #4
Sampling
Final
pp 77-82
pp 63-73, pp 59-63
Note Section 13 dates are out
of sequence due to lab
availability .
pp 101-111