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OPIM 5103 Statistics
Jan Stallaert
Professor OPIM
Educational Goals for the Course
• “Poets”– Learn some basic
principles of data analysis.– Learn enough so that you
will know what your consultant is telling you.
• “Engineers”– Sharpen quantitative skills.– Practice explaining
technical material by helping non-technical people in your group.
Both groups should get something out of this class.
Technical Goal
• Understand Multiple Regression– Relationships between variables.
• Is weight or length more important for determining a car’s gas mileage?
– Predict one variable using others.• What profit should you expect when building a house
with X sq. ft. and Y bedrooms.– Evaluate performance in the face of mitigating
circumstances.• Set production goals for subordinates with different staff
sizes, regional economic conditions, etc.
From here to there...
Looking at data
Graphs
Numerical Summaries
Thinking about a process
Probability
What happens in the future?Inference
Making guesses
Testing theories
Data Collection
Can we generalize our results?
Did one thing “cause” another?Relationships:
two variables
Correlation
Regression
Relationships: several
variables
Multiple regression
Class Resources
• Textbook– Does a better job explaining
theory.– Not as many examples.
• PowerPoint presentation– Usually available before our
meetings
• Lectures– My chance to explain what the
reading is supposed to say.
• Homeworks/Examples– Most of your learning occurs
here.
Intro to MS Excel
• I assume you know how to:– Create formulas in cells– Copy, cut and paste cells within a workbook– Make simple charts
• E.g., Pie charts, bar charts– Have the Data Analysis Add-in installed– Do basic formatting
• E.g., numbers, percentages, etc.
Statistical Methods
• Descriptive statistics– Collecting and describing data
• Inferential statistics– Drawing conclusions and/or making decisions
concerning a population based only on sample data
Inferential Statistics
• Estimation– e.g.: Estimate the population
mean weight using the sample mean weight
• Hypothesis testing– e.g.: Test the claim that the
population mean weight is 120 pounds
Drawing conclusions and/or making decisions concerning a population based on sample results.
Looking at Data
• Example:– House Data
Types of Data
Categorical(Q ualitative)
Discrete Continuous
Num erical(Q uantitative)
D ata
Displaying Numerical Data
• Histogram example
Histogram
0
5
10
15
20
Bin
Fre
qu
ency
Displaying Numerical DataHistogram Portfolio A
02468
101214161820
-15 -5 5 15 25 35 45 More
Fre
qu
ency
.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
Histogram Portfolio B
02468
101214161820
-15 -5 5 15 25 35 45 More
Fre
qu
ency
.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
-15 -5 5 15 25 35 45 More
Portfolio A
Portfolio B
EXCEL Tutorial: Histograms
• Here’s a PDF document
Displaying Bivariate Numerical Data
• Scatterplots
Price / Square Footage
-
50.000
100.000
150.000
200.000
250.000
- 500 1,000 1,500 2,000 2,500 3,000
Squar e f eet
price
Displaying Categorical Data
• Example: bar chart, pie chart, Pareto diagram
House Styles
36%
23%
41% Cape Cod
Two Story
Ranch
Displaying Categorical Data
• Example: bar chart, pie chart, Pareto diagram
39
25
44
0
5
10
15
20
25
30
35
40
45
50
Cape Cod Two Story Ranch
Displaying Categorical Data
• Example: bar chart, pie chart, Pareto diagram
44
39
25
0
5
10
15
20
25
30
35
40
45
50
Ranch Cape Cod Two Story
Displaying Bivariate Categorical Data
• Contingency table
Style vs. basement
style 0 1Grand
Total
0 14 25 39
1 25 25
2 3 41 44
Grand Total 17 91 108
Basement
EXCEL Tutorial: PivotTables
• Here’s a PDF document
