1. Introduction to Statistics
ECO B. Potter
Introduction to Statistics

2. Definitions Statistics Data Population Census Sample
ECO B. Potter
Definitions
Statistics
Data
Population
Census
Sample
Inferential Statistics

3. Definitions Statistics
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Definitions
Statistics
The science of the collection, organization, and interpretation of numerical data.
The analysis of population characteristics by inference from sampling.

4. Definitions Statistics Data
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Definitions
Statistics
Data
collections of observations (measurements, survey responses, etc.)

5. Definitions Statistics Data Population
ECO B. Potter
Definitions
Statistics
Data
Population
The complete collection of all individuals to be studied.

6. Definitions Statistics Data Population Census
ECO B. Potter
Definitions
Statistics
Data
Population
Census
The collection of data from every member of the population.

7. Definitions Statistics Data Population Census Sample
ECO B. Potter
Definitions
Statistics
Data
Population
Census
Sample
A subset of the population

8. Definitions Statistics Data Population Census Sample
ECO B. Potter
Definitions
Statistics
Data
Population
Census
Sample
Inferential Statistics
Mathematical methods used to infer the properties of a population from the analysis of the properties of a sample drawn from it.

9. Types of Data Parameter Statistic Quantitative data
ECO B. Potter
Types of Data
Parameter
Statistic
Quantitative data
Categorical (qualitative) data
Discrete data
Continuous data

10. Types of Data Parameter
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Types of Data
Parameter
Numerical measurement describing a characteristic of a population.

11. Types of Data Parameter Statistic
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Types of Data
Parameter
Statistic
Numerical measurement describing a characteristic of a sample.

12. Types of Data Parameter Statistic Quantitative data
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Types of Data
Parameter
Statistic
Quantitative data
numbers representing counts or measurements.
The number of students who are in-state residents.
The weights of students at UCF.

13. Types of Data Parameter Statistic Quantitative data
ECO B. Potter
Types of Data
Parameter
Statistic
Quantitative data
Categorical (qualitative) data
Names or labels (representing categories). Can be numerical
Gender
Jersey numbers

14. Types of Data Parameter Statistic Quantitative data
ECO B. Potter
Types of Data
Parameter
Statistic
Quantitative data
Categorical (qualitative) data
Discrete data
The number of possible values is either a finite number, or a “countable” number (0, 1, 2,…)
The number of students enrolled in ECO (0 – 350)
The number of students enrolled at UCF.

15. Types of Data Parameter Statistic Quantitative data
ECO B. Potter
Types of Data
Parameter
Statistic
Quantitative data
Categorical (qualitative) data
Discrete data
Continuous data
Within an interval there are an infinite number of possible values.
The distance a student travels between home and school

16. 4 Levels of Measurement Nominal Ordinal Interval Ratio
ECO B. Potter
4 Levels of Measurement
Nominal
Ordinal
Interval
Ratio

17. 4 Levels of Measurement Nominal
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4 Levels of Measurement
Nominal
Data that consist of names, labels, or categories only
Cannot be arranged in an ordering scheme (such as low to high)
Ex.: Survey responses (yes, no, undecided)

18. 4 Levels of Measurement Nominal Ordinal
ECO B. Potter
4 Levels of Measurement
Nominal
Ordinal
Data that can be arranged in some order.
Differences between data values either cannot be determined or are meaningless
Ex.: BCS college football rankings.

19. 4 Levels of Measurement Nominal Ordinal Interval
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4 Levels of Measurement
Nominal
Ordinal
Interval
Data that can be arranged in some order.
Difference between any two values is meaningful.
No natural zero starting point, where zero means the absence of any quantity.
Ex.: Temperature.

20. 4 Levels of Measurement Nominal Ordinal Interval Ratio
ECO B. Potter
4 Levels of Measurement
Nominal
Ordinal
Interval
Ratio
Data that can be arranged in some order.
Difference between any two values is meaningful.
There is a natural zero starting point, where zero means the absence of any quantity.
Ex.: The number of points earned on a test.

21. ECO B. Potter
Deceptive Statistics
“There are three kinds of lies: Lies, damned lies, and statistics.” - Benjamin Disraeli
“Some people use statistics as a drunken man uses lampposts – for support rather than illumination” – Historian Andrew Lange
“There are two kinds of statistics, the kind you look up, and the kind you make up.” – Rex Stout

22. Deceptive Statistics Two common sources Evil intent
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Deceptive Statistics
Two common sources
Evil intent
Unintentional errors

23. ECO B. Potter

24. Deceptive Statistics Common deceptive methods Misuse of graphs
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Deceptive Statistics
Common deceptive methods
Misuse of graphs
Bad samples
Correlation/Causation
Reported results
Small samples
Loaded questions
Order of questions

25. Deceptive Statistics Common deceptive methods Misuse of graphs
ECO B. Potter
Deceptive Statistics
Common deceptive methods
Misuse of graphs
To correctly interpret a graph, you must analyze the numerical information given in the graph, so as not to be misled by the graph’s shape. READ labels and units on the axes!

26. Deceptive Statistics Common deceptive methods Misuse of graphs
ECO B. Potter
Deceptive Statistics
Common deceptive methods
Misuse of graphs
Pictographs
Exaggerate the difference by increasing each dimension in proportion to the actual amounts

27. Deceptive Statistics Common deceptive methods Bad samples
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Deceptive Statistics
Common deceptive methods
Bad samples
Voluntary response samples
Internet surveys
valid conclusions can be made only about the specific group of people who agree to participate and not about the population.

28. Deceptive Statistics Common deceptive methods Correlation/Causation
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Deceptive Statistics
Common deceptive methods
Correlation/Causation
Concluding that one variable causes the other variable when in fact the variables are linked
Two variables may seemed linked, smoking and pulse rate, this relationship is called correlation. Cannot conclude the one causes the other.

29. Deceptive Statistics Common deceptive methods Reported results
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Deceptive Statistics
Common deceptive methods
How tall are you?
Reported results
When collecting data from people, it’s better to take measurements yourself instead of asking subjects to report results.
I’m 6’ 4” tall

30. Deceptive Statistics Common deceptive methods Small samples
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Deceptive Statistics
Common deceptive methods
Small samples
Conclusions should not be based on samples that are too small.
Children Out of School in America (Children’s Defense Fund, 1974): Among secondary school students suspended in Los Angeles County, 67% were suspended at least 3 times.
Sample size: 3

31. Deceptive Statistics Common deceptive methods Loaded questions
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Deceptive Statistics
Common deceptive methods
Loaded questions
Survey questions can be “loaded” or intentionally worded to elicit a desired response.
In a recent study using two different randomly selected groups:
Question
Agree
Too little money is being spent on welfare.
Too little money is being spent on assistance to the poor.

32. Deceptive Statistics Common deceptive methods Loaded questions
ECO B. Potter
Deceptive Statistics
Common deceptive methods
Loaded questions
Survey questions can be “loaded” or intentionally worded to elicit a desired response.
In a recent study using two different randomly selected groups:
Question
Agree
Too little money is being spent on welfare.
19%
Too little money is being spent on assistance to the poor.
63%

33. Deceptive Statistics Common deceptive methods Order of questions
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Deceptive Statistics
Common deceptive methods
Order of questions
Questions are unintentionally loaded by such factors as the order of the items being considered.
Does traffic contribute more or less to air pollution than industry? Results: 45% said traffic, 27% said industry
Does industry contribute more or less to air pollution than traffic ? Results: 57% said industry, 24% said traffic

34. Collecting Sample Data
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Collecting Sample Data
Observational Study
Experiment
Random Sample
Simple Random Sample
Other Sampling methods…

35. Collecting Sample Data
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Collecting Sample Data
Observational Study
Observing and measuring specific characteristics without attempting to modify the subjects being studied.

36. Collecting Sample Data
ECO B. Potter
Collecting Sample Data
Observational Study
Experiment
Apply some treatment and then observe its effects on the subjects

37. Collecting Sample Data
ECO B. Potter
Collecting Sample Data
Observational Study
Experiment
Random Sample
members from the population are selected in such a way that each individual member in the population has an equal chance of being selected

38. Collecting Sample Data
ECO B. Potter
Collecting Sample Data
Observational Study
Experiment
Random Sample
Simple Random Sample
selected in such a way that every possible sample of the same size n has the same chance of being chosen
Other Sampling methods…

39. Systematic Sampling Select some starting point and then
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Systematic Sampling
Select some starting point and then
select every kth element in the population

40. use results that are easy to get
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Convenience Sampling
use results that are easy to get

41. subdivide the population into at
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Stratified Sampling
subdivide the population into at
least two different subgroups that share the same characteristics, then draw a sample from each subgroup (or stratum)

42. divide the population area into sections
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Cluster Sampling
divide the population area into sections
(or clusters); randomly select some of those clusters; choose all members from selected clusters

43. End of Intro to Statistics
ECO B. Potter
End of Intro to Statistics