1. Ways to Describe Data Sets
Shape
Outliers
Center
Spread

3. Shape
We can use a graphs to look at the shape of the quantitative variable distribution.
An example of a bell-shaped or normal distribution which appear often in nature:
Symmetric
Mean, median, mode roughly equal

4. Shape: Skewed Distributions
Scores from an easy exam, skewed left.
Scores from a hard exam, skewed right.
Non-symmetric
Mean < Median < Mode
Non-symmetric
Mean > Median > Mode

5. Shape
Shape described by number of peaks (mode)

6. What does this graph tell you?

7. Outliers
An outlier is an extreme value of the data (extremely high or extremely low). It is an observation value that is significantly different from the rest of the data. There may be more than one outlier in a set of data.

8. Outliers Possible Reasons for Outliers:
1. An error was made while taking the measurement or entering it into the computer.
2. The individual belongs to a different group than the bulk of individuals measured.
3. The outlier is a legitimate, though extreme data value.

9. Identifying Outliers (1.5*IQR)
We can identify an outlier if it is
Less Q1 – 1.5×IQR or
Greater than Q ×IQR

10. Example
Make a box and whisker plot of the data and identify any outliers.
10, 12, 11, 15, 11, 14, 13, 17, 12, 22, 14, 11

11. The prices of a gallon of gasoline (in dollars) for selected countries in 2003 are listed below.
Australia: $2.20
Canada: $2.02
Germany: $4.58
Mexico: $2.09
United States: $1.59
Japan: $3.47
Taiwan: $2.16
Make a box and whisker plot for the gasoline prices.
Which countries, if any, had gasoline prices that can be considered outliers?

12. Center Measures of center: mean, median, mode Average of the data set
The middle value of a data set arranged from smallest to largest
The data value that occurs the most often, is a common measure of center for categorical data

13. Mean vs. Median

14. Center
When describing data, you must decide which number is the most appropriate description of the center.
Mean Median applet:
Use the mean on symmetric data and the median on skewed data or data with outliers

15. What does this graph tell you?

16. Mean Absolute Deviation Variance and Standard Deviation
Spread
Range
Interquartile Range
Mean Absolute Deviation
Variance and Standard Deviation
Max value subtract minimum value
(spread of all data)
Interquartile range (IQR) : shows middle 50% of data
IQR = Q3 – Q1
Not affected as much by outliers
Use when measure of center is median
Average distance between each data value and the mean
Use when measure of center is mean
a measure of the “average” deviation of all observations from the mean.

17. 5 number summary
Complete a 5 number summary and box and whisker plot for the following data.
Number of hours spent on internet per week:
12, 4, 16, 18, 1, 6, 10, 8

18. Mean Absolute Value Deviation
To calculate Mean Absolute Value Deviation:
Calculate the mean for the data set.
Find the distance between each data value and the mean. That is, find the absolute value of the difference between each data value and the mean.
Find the average of those distances.

19. Example Find the mean absolute value of the following data set:
52, 48, 60, 55, 59, 54, 58, 62

20. Standard Deviation
A measure of spread is the Standard Deviation: a measure of the “average” deviation of all observations from the mean.
The symbol for Standard Deviation is σ (the Greek letter sigma).

21. Standard Deviation To calculate Standard Deviation:
Calculate the mean for the data set.
Determine each observation’s deviation: subtract the mean from each data point. (𝑥 − 𝑥 ).
Square each deviation.
“Average” the squared-deviations by totaling the squared- deviation and dividing the total squared deviation by (n-1).
This quantity is the Variance.
Square root the result to determine the Standard Deviation.

22. Example Calculate the standard deviation of the following test scores:
15, 20, 21, 20, 36, 15, 25, 15

23. The most appropriate measure of variability depends on …
The shape of the data’s distribution!
If data are symmetric, with no serious outliers, use range and standard deviation.
If data are skewed, and/or have serious outliers, use IQR.

24. Comparing Data Quantitative Data: through graphs
Categorical Data: through two way frequency tables

25. Graphs
Multiple bar graphs Multiple box and whisker plots

26. Two Way Frequency Tables
These tables examine the relationships between the two categorical variables.
A two-way frequency table will deal with two variables

27. Two-Way Frequency Table

28. Two-Way Relative Frequency Table
Relative frequency is the ratio of the value of a subtotal to the value of the total.

29. Example
Create a two-way frequency table for the following problem.

30. Hint

31. Answer

32. Example