1. Econ 3790: Business and Economics Statistics
Instructor: Yogesh Uppal

2. Lecture 1 — Schedule Goals of the course Data and statistics
Tabular methods for summarizing data
Graphical methods for summarizing data

3. Why use Statistics? To make sense of large amounts of data:
What are the demographics of Youngstown in 2000?
Have U.S. wages increased since 1975?
To test hypotheses:
Is demand curve downward sloping?
Are GDP and Saving Rate positively correlated?
To make predictions:
What might happen to savings behavior after a large tax cut?
These are specific to economics, but there are of course example from other disciplines:
Medicine – drug testing
These are increasing in their complexity.
At the end of Econ 10A, you may be able to answer the first two on your own. These are univariate studies – one variable at a time.
The others require looking at many variables at one time, and often require advanced methods.

4. Data: Basic Definitions
Data: a set of measurements
Dataset: all data collected for one study
Element, or unit: an entity on which data are collected
Variable: a property or attribute of each unit
Observation: the values of all variables for one unit

5. Data: Basic Definitions
Variables
Observation
Element
Names
Stock Annual Earn/
Exchange Sales($M) Share($)
Company
Dataram
EnergySouth
Keystone
LandCare
Psychemedics
AMEX
OTC
NYSE
NYSE
AMEX
Data Set

6. Data: Scales of Measurement
Four scales of measurement:
Nominal, ordinal, interval, and ratio scales
Scale determines which methods of summarization and analysis are appropriate for any given variable
These are different ways that variables can be measured.
Binary: taking exactly one of two possible values

7. Data: Scales of Measurement
Characteristic
Nominal, like a label or name for a characteristic
e.g., color: red, green, blue
race: black, Hispanic, white, Asian
binary: (male, female), (yes, no), (0, 1)
Ordinal, still a characteristic, but having a natural order
e.g., how was service?: poor, average, good
These are different ways that variables can be measured.
Binary: taking exactly one of two possible values

8. Data: Scales of Measurement
Numeric
Interval scale
Numeric data showing the properties of ordinal data
e.g., SAT scores, Fahrenheit temperature
Ratio scale
Ordered, numeric data with real zero
e.g., income, distance, price, quantity

9. Data: Other Classifications
Qualitative, or categorical: measures a quality
Quantitative: numeric values that indicate how much or how many
Cross-sectional: data collected at one point in time
Time series: data collected over several time periods
Panel or longitudinal: combination of cross-sectional and time series

10. Data: Summary of Definitions
Qualitative
Quantitative
Numerical
Nonnumerical
Numerical
Nominal
Ordinal
Nominal
Ordinal
Interval
Ratio

11. Statistical Inference: Definitions
Population: the set of all elements of interest in a study
Sample: a subset of the population
Statistical Inference: the process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population

12. Statistical Inference: Process
1. Population
consists of all
tune-ups. Average
cost of parts is
unknown.
2. A sample of 50
engine tune-ups
is examined.
3. The sample data
provide a sample
average parts cost
of $79 per tune-up.
Draw diagram with sample inside of the population, then with an arrow showing statistical inference.
4. The sample average
is used to estimate the
population average.

13. Descriptive Statistics: Definition
Descriptive statistics are the tabular, graphical, and numerical methods used to summarize data

14. Descriptive Statistics: Common Methods
Some common methods:
Tabular
Frequency table (for one variable)
Crosstabulation, or crosstab (for more than one variable)
Graphical
Bar graph (for categorical variables)
Histogram (for interval- or ratio-scaled variables)
Scatterplot (for two variables)
Numerical
Mean (arithmetic average)
This is certainly not a complete list, but you should feel comfortable starting this class knowing that you’ve actually seen or even used most of these.

15. Summarizing Qualitative Data
Frequency distribution
Relative frequency distribution
Bar graph
Pie chart
Objective is to provide insights about the data that cannot be quickly obtained by looking at the original data

16. Distribution Tables
Frequency distribution is a tabular summary of the data showing the frequency (or number) of items in each of several non-overlapping classes
Relative frequency distribution looks the same, but contains proportion of items in each class
Define classes: can be categories, or intervals of continuous data.
Open stata, and use it with the census dataset to make frequency distns and rf distns .

17. Example 1: What’s your major?
Frequency
Finance 6
Marketing 10
Accounting 18
Advertising

18. Summarizing Quantitative Data
Frequency Distribution
Relative Frequency Distribution
Dot Plot
Histogram
Cumulative Distributions

19. Example 1: Go Penguins Month Opponents Rushing TDs Sep SLIPPERY ROCK 4
NORTHEASTERN
at Liberty 1
at Pittsburgh
Oct
ILLINOIS STATE
at Indiana State
WESTERN ILLINOIS 2
MISSOURI STATE
at Northern Iowa
Nov
at Southern Illinois
WESTERN KENTUCKY 3

20. Example 1: Go Penguins Rushing TDs Frequency Relative Frequency 3 0.273
0.27 1 2
0.18
0.09 4
0.37
Total=11
Total=1.00

21. Example 2: Rental Market in Youngstown
Suppose you were moving to Youngstown, and you wanted to get an idea of what the rental market for an apartment (having more than 1 room) is like
I have the following sample of rental prices

22. Example: Rental Market in Youngstown
Sample of 28 rental listings from craigslist:

23. Frequency Distribution
To deal with large datasets
Divide data in different classes
Select a width for the classes
Need rules for constructing classes because the classes are not obvious with quantitative data

24. Frequency Distribution (Cont’d)
Guidelines for Selecting Number of Classes
Use between 5 and 20 classes
Datasets with a larger number of elements usually require a larger number of classes
Smaller datasets usually require fewer classes

25. Frequency Distribution
Guidelines for Selecting Width of Classes
Use classes of equal width
Approximate Class Width =
If classes are not equal width, you should be sceptical that someone is trying to deceive you.

26. Frequency Distribution
For our rental data, if we choose six classes:
Class Width = ( )/6 = 70

27. Relative Frequency
To calculate relative frequency, just divide the class frequency by the total Frequency
Remind them not to worry about percent frequency because it’s just relative times 100

28. Relative Frequency
Insights gained from Relative Frequency Distribution:
32% of rents are between $539 and $609
Only 7% of rents are above $680

29. Histogram of Youngstown Rental Prices
So explain how frequency tables are exactly the data contained in a histogram. In fact, when stata calculates the data for a histogram, it is in fact creating a frequency or relative frequency table.

30. Describing a Histogram
Symmetric
Left tail is the mirror image of the right tail
Example: heights and weights of people
.05
.10
.15
.20
.25
.30
.35
Relative Frequency

31. Describing a Histogram
Moderately Left or Negatively Skewed
A longer tail to the left
Example: exam scores
.05
.10
.15
.20
.25
.30
.35
Relative Frequency

32. Describing a Histogram
Moderately Right or Positively Skewed
A longer tail to the right
Example: hourly wages
.05
.10
.15
.20
.25
.30
.35
Relative Frequency

33. Describing a Histogram
Highly Right or Positively Skewed
A very long tail to the right
Example: executive salaries
.05
.10
.15
.20
.25
.30
.35
Relative Frequency

34. Cumulative Distributions
Cumulative frequency distribution:
shows the number of items with values less than or equal to a particular value (or the upper limit of each class when we divide the data in classes)
Cumulative relative frequency distribution:
shows the proportion of items with values less than or equal to a particular value (or the upper limit of each class when we divide the data in classes)
Usually only used with quantitative data!

35. Example 1: Go Penguins (Cont’d)
Rushing TDs
Frequency
Relative Frequency
Cumulative Fre.
Cumulative Relative Fre. 3
0.27 1 2
0.18 5
0.45
0.09 6
0.54 7
0.63 4
0.37 11
Total

36. Cumulative Distributions
Youngstown Rental Prices

37. Crosstabulations and Scatter Diagrams
So far, we have focused on methods that are used to summarize data for one variables at a time
Often, we are really interested in the relationship between two variables
Crosstabs and scatter diagrams are two methods for summarizing data for two (or more) variables simultaneously

38. Crosstabs A crosstab is a tabular summary of data for two variables
Crosstabs can be used with any combination of qualitative and quantitative variables
The left and top margins define the classes for the two variables

39. Example: Data on MLB Teams
Data from the 2002 Major League Baseball season
Two variables:
Number of wins
Average stadium attendance

40. Crosstab Frequency distribution for the wins variable
for the attendance variable

41. Crosstabs: Row or Column Percentages
Converting the entries in the table into row percentages or column percentages can provide additional insight about the relationship between the two variables

42. Crosstab: Row Percentages

43. Crosstab: Column Percentages

44. Crosstab: Simpson’s Paradox
Data in two or more crosstabulations are often aggregated to produce a summary crosstab
We must be careful in drawing conclusions about the relationship between the two variables in the aggregated crosstab
Simpsons’ Paradox:
In some cases, the conclusions based upon an aggregated crosstab can be completely reversed if we look at the unaggregated data

45. Crosstab: Simpsons Paradox
Frequency distribution
for the wins variable
Frequency distribution
for the attendance variable

46. Scatter Diagram and Trendline
A scatter diagram, or scatter plot, is a graphical presentation of the relationship between two quantitative variables
One variable is shown on the horizontal axis and the other is shown on the vertical axis
The general pattern of the plotted lines suggest the overall relationship between the variables
A trendline is an approximation of the relationship

47. Scatter Diagram
A Positive Relationship: y x

48. Scatter Diagram
A Negative Relationship y x

49. Scatter Diagram
No Apparent Relationship y x

50. Example: MLB Team Wins and Attendance
Slightly positive relationship, or no relationship indicated

51. Tabular and Graphical Descriptive Statistics
Data
Qualitative Data
Quantitative Data
Tabular Methods
Graphical Methods
Tabular Methods
Graphical Methods
Freq. Distn.
Rel. Freq. Distn.
Crosstab
Bar Graph
Pie Chart
Freq. Distn.
Rel. Freq. Distn.
Cumulative Freq. Distn.
Cumulative Rel. Freq. Distn.
Crosstab
Histogram
Scatter Diagram
Ogive is just a plot of the cumulative distribution