Transcript BA 275, Fall 1998 Quantitative Business Methods

BA 275
Quantitative Business Methods
Agenda
 Housekeeping
 Introduction to Statistics


Elements of Statistical Analysis
Concept of Statistical Analysis
 Statgraphics
1
Contact Information
Temporary office at 407 Bx until mid-January
Instructor:
Office:
Office Phone:
E-mail:
Office Hours:
Ping-Hung Hsieh
Bexell 402
737-6060
[email protected]
Monday 9:45 – 10:45 a.m. (except for 1/22 and 2/26, TBA)
Tuesday 1:30 – 2:30 p.m.
Wednesday 9:15 – 10:15 a.m.
And by appointment
2
Course Description and Objectives
 Management decision processes utilizing statistical methods, use and
application of probability concepts, sampling procedures, statistical
estimation, and regression to the analysis and solution of such
business problems as income and cost estimation, sales forecasting,
performance evaluation, inventory analysis, and quality control.
 This course teaches quantitative methods used in data analysis and
business decision making. Topics covered include: descriptive
statistics, correlation and regression, hypothesis testing, statistical
process control, and forecasting. Business applications of these
techniques are emphasized. Students in this course will acquire
expertise in computer-based methods for data analysis and decision
making, through computer analysis of business datasets.
 Upon completion of this course, students will understand and be able to
use STATGRAPHICS PLUS to analyze business, economic and
financial data with various statistical tools. (The software can be
obtained from Milne Computer Center. Free of charge.)
3
Class and Textbook Websites
 Class Website:




http://www.bus.orst.edu/
Student Services / Course Materials
“>>” BA 275 Quantitative Business Methods
HSIEH
 Textbook Website:

http://www.whfreeman.com/pbs
4
Grading
 Course scores will be computed as follows:
Quizzes
SG Projects
Midterm
Final Exam
Progress
60
50
80
45
15
(7 quizzes at 10 points each. One lowest quiz will be dropped.)
(2 computer projects at 25 points each.)
(2 midterm exams at 40 points each.)
(3 progress reports at 5 points each.)
 Grades will be assigned based on the
following scale:
227 – 250.00 = A
203 – 224.99 = B
179 – 200.99 = C
155 – 175.99 = D
226 – 226.99 = A minus
202 – 202.99 = B minus
178 – 178.99 = C minus
No D minus
225 – 225.99 = B plus
201 – 201.99 = C plus
176 – 177.99 = D plus
Below 155 = F
5
Class Schedule
Week
1
Reading Assignment
(Textbook page no.)
Exploring Data: Data Analysis
Date
1/ 8
Important Dates
 Housekeeping
 Introduction to Statistics
4 – 46
CD Assignment
(Upgrade lesson no.)
1, 2, 3, 4, 5, 6, 7, 8
1/10
Experiencing Random Behavior: Probability and Sampling Distributions
2
1/15
Martin Luther King, Jr. Day
1/17
Quiz #1
56 – 66
206 – 217
282 – 295
10, 11, 12
31, 32, 33, 34, 35
Drawing Conclusions from Data: Confidence Interval Estimation on Quantitative Data
3
1/22
1/24
Quiz #2
362 – 374
40, 41, 42, 43, 44, 45, 46,
47
Drawing Conclusions from Data: Hypothesis Testing on Quantitative Data
4
5
1/29
Midterm #1 (covers Weeks 1 – 3)
 Progress report #1 is due.
1/31
2/ 5
2/ 7
Quiz #3
380 – 394
48, 49, 50, 51, 52
394 – 398
404 – 420
53, 54, 55, 56
Drawing Conclusions from Data: Statistical Inference – Small and Two-Sample Problems
6
2/12
Quiz #4
 Computer Project #1 is due.
2/14
432 – 439
443 – 445
461 – 468
Modeling Relationships: Linear Regression Analysis
2/19
Quiz #5
86 – 96
57, 58, 60, 61
6
Drawing Conclusions from Data: Hypothesis Testing on Quantitative Data
4
1/29
Midterm #1 (covers Weeks 1 – 3)
 Progress report #1 is due.
1/31
2/ 5
2/ 7
Quiz #3
Class Schedule
5
380 – 394
48, 49, 50, 51, 52
394 – 398
404 – 420
53, 54, 55, 56
Drawing Conclusions from Data: Statistical Inference – Small and Two-Sample Problems
6
2/12
Quiz #4
 Computer Project #1 is due.
2/14
432 – 439
443 – 445
461 – 468
57, 58, 60, 61
Modeling Relationships: Linear Regression Analysis
2/19
7
Quiz #5
 Progress report #2 is due.
2/21
2/26
8
Midterm #2 (covers Weeks 4 – 7)
2/28
86 – 96
102 – 108
111 – 124
133 – 140
584 – 589
594 – 597
608 – 610
13, 14, 15
16, 17, 18, 19, 20
78, 79,80, 81, 82
Drawing Conclusions from Data: Statistical Inference on Qualitative Data
9
10
11
Quiz #6
 Simple Regression Review
319 – 327
504 – 509
3/ 7
3/12
Quiz #7
3/14
 Project #2 is due.
509 – 516
520 – 528
3/ 5
3/20
38
62, 63, 64, 65
66, 67, 68
69, 70
Finals Week
 Comprehensive Final Exam (16:00 – 17:50, Tuesday, 3/20/2007. Room: TBA.)
 Progress report #3 is due.
7
Weekly Quizzes
 20 – 30 minutes at the beginning of the class.
 Open book/notes.
 Need a calculator.
 They cover the materials assigned in the
previous week.
8
Progress Reports #1, #2, #3
 There is a self assessment quiz at the end of each CD lesson.
 Once the quiz is loaded, click the “Begin Assessment” button to




start the quiz. At the end of the quiz, click the “Grade Quiz”
button to see your score.
You may repeat the quiz as many times as you want.
For each quiz, make a hard copy of the page that contains your
best score.
Download the progress report forms from the class website and
record the best scores.
The reports pertaining to the CD lessons assigned between
Weeks 1 – 3, Weeks 4 – 6, and Weeks 7 – 10 must be turned in
at the beginning of the class on 1/29, 2/19, and 3/20,
respectively.
9
Computer Projects #1, #2
 Two computer projects will be assigned at




the end of Weeks 2 and 6.
They are due on 2/12 and 3/14 at the
beginning of the class.
You can work in a group of 2 to 4 members.
Each team turns in one copy.
Download related files/documents from the
class website.
Download Statgraphics instruction from the
class website.
10
Mandatory Classes
 The classes on 3/12 and 3/14 (Week 10) are
mandatory.
 Two topics that are not on the CDs will be
covered.
11
Definition
“Statistics” is the science of data.
It involves collecting, classifying,
summarizing, organizing, analyzing,
and interpreting numerical information.
We will learn how to make
based on data
12
Fundamental Elements of Statistics
 A population is a set of units (usually people, objects,
transactions, or events) that we are interested in studying. It is
the totality of items or things under consideration.
 A sample is a subset of the units of a population. It is the
portion of the population that is selected for analysis.
 A parameter is a numerical descriptive measure of a population.
It is a summary measure that is computed to describe a
characteristic of an entire population.
 A statistic is a numerical descriptive measure of a sample. It is
a summary measure calculated from the observations in the
sample.
13
Example
 A manufacturer of computer chips claims that
less than 10% of his products are defective.
When 1000 chips were drawn from a large
production, 7.5% were found to be defective.



What is the population of interest?
What are the sample, parameter and statistic?
Does the value 10% refer to the parameter or
to the statistic? How about the value 7.5%?
14
statistics: x , s2, s, p̂ , etc.
x1, x2, …, xn
Sample of size n
Qualitative
Quantitative
Organizing data:
Estimation
Hypothesis Testing
Regression Analysis
Contingency Tables
Drawing conclusions from data:
Random variables,
Probability,
Distributions
Discrete: binomial distribution
Continuous: normal distribution,
Sampling distribution of the sample mean
Describing uncertainty:
X1, X2, …, Xn
Selecting a random sample:
parameters: , 2, , p, etc.
POPULATION
Statistical Analysis
15
Types of Data
 Numerical (Quantitative) Data
 Regular numerical observations. Arithmetic
calculations are meaningful.
 Age
 Household income
 Starting salary
 Categorical (Qualitative) Data
 Values are the (arbitrary) names of possible
categories.
 Gender: Female = 1 vs Male = 0.
 College major
16
Example
 For each of the following examples of data,
determine the type.




The number of miles joggers run per week
The starting salaries of our business students
The months in which a firm’s employees
choose to take their vacations
The final letter grades received by students in
a statistics course
17
Example
 A sample of shoppers at a mall was asked the
following questions. Identify the type of data
each question would produce.





What is your age?
How much did you spend?
What is your marital status?
Rate the availability of parking: excellent,
good, fair, or poor
How many stores did you enter?
18
Employee Data (SG Demo)
Variable
Employee ID
Salary
Gender
Age
Experience
Training Level+
Definition
Employee identification number. These numbers were assigned for the sole
purpose of giving each employee a unique number.
Annual salary.
M = male; F = female.
Age of the employee (in years).
Years of experience.
The training is offered from time to time and is voluntary (it is not a job
requirement).
19