ECON 3790Statistics for Business and

Economics

Prerequisite: Math 1549, 1552, 1570, or 1571

Classroom Instructor: Tod Porter

Office hours: Monday & Wednesday 1:00‐2:00, Tuesday & Thursday 10:00‐11:00, or by appointment

My office is located in the Economics Department suite, room 303

Computer Lab Instructor: Ross Munroe

Points in Course• Homeworks and quizzes (75 pts.)• Three midterm exams (225 pts.)• Final exam (100 pts.)• Computer lab (100 pts.)

15%

45%

20%

20%

Chart Title

Homework and quizzes

Midterm exams

Final exam

Computer lab

Grading Scale• A, 85-100%• B, 75-84%• C, 65-74%• D, 55-64%• F, 0-54%

Other

• Course materials will be posted on my web site, www.as.ysu.edu/~tsporter

• See me privately if you need special accommodations due to a disability

• Cell phones MUST be turned off during quizzes and exams

• This is a course where it is essential for you to keep up with the material

Where is Statistics Used?• Accounting• Marketing• Finance• Economics

For Today’s Graduate, Just One Word: Statistics By STEVE LOHRPublished: August 5, 2009 MOUNTAIN VIEW, Calif. — At Harvard, Carrie Grimes majored in anthropology and archaeology and ventured to places like Honduras, where she studied Mayan settlement patterns by mapping where artifacts were found. But she was drawn to what she calls “all the computer and math stuff” that was part of the job.

“I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.”The rising stature of statisticians, who can earn $125,000 at top companies in their first year after getting a doctorate, is a byproduct of the recent explosion of digital data. In field after field, computing and the Web are creating new realms of data to explore — sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, a research firm.

For TodayBy STEVE

Objectives of the Course

• Teach you how to apply basic statistical techniques

• Make you a knowledgeable consumer of more advanced statistical techniques

Chapter 1Data and Statistics

Making Inferences about Populations

Population – The set of all elements of interest in a particular study

Sample – A subset of the population

Making Inferences about Populations

PopulationSample

Draw sample

Infer population characteristics

Describe sample characteristics

Descriptive vs. Inferential Statistics

Descriptive statistics – Summaries of the characteristics of data

Inferential statistics – Techniques used to infer the characteristics of the population using the sample data

Data and Data Sets

Data – The set of all elements of interest in a particular study

Data set – All of the data collected for a particular study

Components of a Data Set

Element – The entities on which data are collected

Variable – A characteristic of interest for the elements

Observation – Set of measurements for a specific element

Example of a Data SetEmployee Age Gender Degree Tenure SalaryBob 45 M HS 27 $35,000

Sue 60 F HS 42 $75,000

Frank 35 M BA 12 $55,000

Mary 25 F MA 2 $50,000

Employee Age Gender Degree Tenure SalaryBob 45 M HS 27 $35,000

Sue 60 F HS 42 $75,000

Frank 35 M BA 12 $55,000

Mary 25 F MA 2 $50,000

Scales of Measurement

Nominal Scale – When the data for a variable consists of labels or names used to identify some attribute

Ordinal Scale – Nominal data where the order or rank of the data is meaningful

Scales of Measurement, cont.

Interval Scale – When the data show the properties of ordinal data the interval between values is express in terms of a fixed unit of measure

Ratio Scale – The data have all the properties of interval data and the ratio of two variables is meaningful (must have a zero value)

Example of Interval Scale

Dress Size Waist, in inches8 24

10 26

12 28

14 30

16 32

A size 0 dress would correspond to a 8 inch waist

Scales of Measurement, cont.

What scale of measurement would be used for the following variables?• Distance from car to class• Football jersey number• Temperature• Ranking of satisfaction (5 = extremely

satisfied, 1 = extremely dissatisfied)

Qualitative vs. Quantitative Data

Qualitative – Labels or names used to identify the attribute of each element (Nominal or ordinal measurement)

Quantitative – Numeric values that indicate how much or how many of something (Interval or ratio measurement)

Types of Data

Cross-sectional – Data collected at approximately the same point in time

Time series – Aggregated values collected at different points in time

Panel – Data collected from the same elements at different points in time

Types of Statistical Studies

Experimental – Researcher has direct control over the variable being studied

Observational – The researcher can only observe the variable being studied

Practice Homework

Pages 20-21, #10, 12, 13

Practice homework will not be graded, the answers are in the back of the book

Chapter 2Descriptive Statistics:Tabular and Graphical

Presentations

Frequency Distribution

A tabular summary of data showing the number (frequency) of items in non-overlapping classes

Frequency Distribution

Employee Age Gender Degree Tenure SalaryBob 45 M HS 27 $35,000

Sue 60 F HS 42 $75,000

Frank 35 M BA 12 $55,000

Mary 25 F MA 2 $50,000

Degree FrequencyHS 2

BA 1

MA 1

Relative and PercentFrequency Distributions

Relative Frequency of a Class = (Frequency of the class)/n

Percent Frequency of a Class = 100 x (Frequency of the class)/n

Frequency Distribution

Employee Age Gender Degree Tenure SalaryBob 45 M HS 27 $35,000

Sue 60 F HS 42 $75,000

Frank 35 M BA 12 $55,000

Mary 25 F MA 2 $50,000

Degree FrequencyRelative Frequency

Percent Frequency

HS 2 0.5 50%

BA 1 0.25 25%

MA 1 0.25 25%

Bar Graph of Frequency Distribution

High School Bachelors Masters0

0.5

1

1.5

2

2.5

Education

Pie Chart of Frequency Distribution

Education

High SchoolBachelorsMasters

Frequency Distributions and Quantitative Data

Building a frequency distribution for quantitative data:1. Choose number of classes2. Determine the width of each class3. Define the class limits, the classes must

include all values and be mutually exclusive

Frequency Distributions and Quantitative Data

Approximate class width =

(Largest data value – Smallest data value)(Number of classes)

Frequency Distributions and Quantitative Data

Class limitsLower class limit – the smallest possible data value assigned to the class

Upper class limit – the largest possible data value assigned to the class___________________________________

Class midpoint = (Lower class limit + Upper class limit)/2

Frequency Distributions and Quantitative Data

Employee SalaryBob $35,000

Sue $75,000

Frank $55,000

Mary $50,000

Fred $80,000

Jim $65,000

Ed $25,000

Ellie $95,000

Rosie $40,000

Jane $45,000

Gary $60,000

Martha $70,000

Assuming five classes, the approximate class width would be ($95,000 - $25,000)/5 = $14,000, round to $15,000

Class Frequency$25,000 up to $40,000 2

$40,000 up to $55,000 4

$55,000 up to $70,000 2

$70,000 up to $85,000 3

$85,000 up to $100,000 1

Frequency Distributions and Quantitative Data

General principles for creating classes:1. Minimize empty classes and classes with

very low values, but don’t make classes so large important information is obscured (4 to 20 classes)

2. Choose class limits that are rounded to some easy-to-read value

3. Make sure the classes include all values are mutually exclusive

HistogramClass Frequency$25,000 up to $40,000 2

$40,000 up to $55,000 4

$55,000 up to $70,000 2

$70,000 up to $85,000 3

$85,000 up to $100,000 1

024

Histogram of Salaries

Frequency Distributions and Quantitative Data

What to do in the case of extreme values?

Employee SalaryBob $35,000

Sue $75,000

Frank $55,000

Mary $50,000

Fred $80,000

Jim $65,000

Ed $25,000

Ellie $1,025,000

Rosie $40,000

Jane $45,000

Gary $60,000

Martha $70,000

Class width = ($1,025,000 - $25,000)/5= $200,000

Class Frequency$25,000 up to $225,000 11

$225,000 up to $425,000 0

$425,000 up to $625,000 0

$625,000 up to $825,000 0

$825,000 to $1,025,000 1

For data sets with extreme values:- Use classes of unequal width- Use open-ended classes

Frequency Distributions and Quantitative Data

Class Frequency$25,000 up to $40,000 2

$40,000 up to $55,000 4

$55,000 up to $70,000 2

$70,000 up to $85,000 3

Over $85,000 1

OgiveA graphical representation of a cumulative frequency distribution.

Class FrequencyUp to $40,000 2

Up to $55,000 6

Up to $70,000 8

Up to $85,000 11

Up to $100,000 12

$25,0

00

$40,0

00

$55,0

00

$70,0

00

$85,0

00

$100

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02468

101214

Ogive of Salaries