1. EOC Review
Question of the Day

2. How do I graphically represent data?
GSE Algebra I
UNIT QUESTION: How can I represent, compare, and interpret sets of data?
Standard: MCC9-12.S.ID.1-3, 5-9, SP.5
Today’s Question:
How do I graphically represent data?
Standard: MCC9-12.S.ID.1

3. MCC6.SP.5c, MCC9-12.S.ID.1, MCC9-12.S.1D.2 and MCC9-12.S.ID.3
Unit 6 Day 1 Vocabulary
Standards
MCC6.SP.5c, MCC9-12.S.ID.1, MCC9-12.S.1D.2 and MCC9-12.S.ID.3

4. Measures of Center
Includes
Mean
Median

5. Mean
The average value of a data set, found by summing all values and dividing by the number of data points
Example:
= 20
The Mean is 4

6. Median
The middle-most value of a data set; 50% of the data is less than this value, and 50% is greater than it
Example:

7. First Quartile
The value that identifies the lower 25% of the data; the median of the lower half of the data set; written as
Example:

8. Third Quartile
Value that identifies the upper 25% of the data; the median of the upper half of the data set; 75% of all data is less than this value; written as
Example:

9. Mode The value in the data set that appears most often.
Example: 5, 4, 2, 6, 3, 6 The mode is 6.

10. Measures of Spread Includes Range Interquartile Range (IQR)
Mean Absolute Deviation - MAD

11. Range
The difference between the greatest and least values in a set of data.
Example: 5, 4, 2, 6, 3, 6 6-2=4 The range is 4.

12. Interquartile Range
The difference between the third and first quartiles; 50% of the data is contained within this range
Example:
Subtract
Third Quartile ( ) – First Quartile ( ) = IQR

13. Interquartile Range: 20 – 6 = 14
The numbers below represent the number of homeruns hit by players of the Hillgrove baseball team.
2, 3, 5, 7, 8, 10, 14, 18, 19, 21, 25, 28
Q1 = 6
Q3 = 20
Interquartile Range: 20 – 6 = 14
Do the same for Harrison: 4, 5, 6, 8, 9, 11, 12, 15, 15, 16, 18, 19, 20

14. Outlier
A data value that is much greater than or much less than the rest of the data in a data set; mathematically, any data less than
or greater than is an outlier
Example:

15. Mean Absolute Deviation (MAD)
The average of the positive deviations of the data from the mean.
How to find:
Calculate the mean
Find the distance from the mean for each piece of data (must be positive!)
Take the average of the positive deviations.

16. Mean Absolute Deviation (MAD)
The average of the positive deviations of the data from the mean.
How to find:
Find the Mean
Calculate the absolute value of the difference between each data value and the mean
Determine the average of the differences in step 2. This average is the mean absolute deviation