1. Algebra 2 Warm up 5.4.13
Write a brief description of how to determine each
statistical measure:
a. Mean
b. Variance
c. Range
d. Median
e. Standard Deviation
f. Mode

2. Correlation
• Correlation is relationship between 2
variables.
– Example: There is a positive relationship between
the type of house you live in and the amount of
money you make. The more money you make the
nicer you house you probably have.
• The idea is to plot out the data and see if they
all align up together on one curve.

3. Y
X
Y
X
Y
Y
X
X
Linear relationships Curvilinear relationships
Various Correlations
Slide from: Statistics for Managers Using Microsoft® Excel 4th Edition, 2004 Prentice-Hall

4. Correlation Coefficient , (r)
 A number between –1 and 1, used to describe
the “correlation” between 2 data points.
 0 = No relationship between the data.
 –1 = A strong negative linear relationship
 1 = A strong the positive linear relationship
 The more closely aligned data is, the
higher the correlation .

5. Scatter Plots of Data with
Various Correlation Coefficients
Y
X
Y
X
Y
X
Y
X
Y
X
r = -1 r = -.6 r = 0
r = +.3r = +1
Y
X
r = 0
Slide from: Statistics for Managers Using Microsoft® Excel 4th Edition, 2004 Prentice-Hall

6. Y
X
Y
X
Y
Y
X
X
Strong relationships Weak relationships
Linear Correlation
Slide from: Statistics for Managers Using Microsoft® Excel 4th Edition, 2004 Prentice-Hall

7. Linear Correlation
Y
X
Y
X
No relationship
Slide from: Statistics for Managers Using Microsoft® Excel 4th Edition, 2004 Prentice-Hall

8. Example
• A director of sales for Blockbuster Video wants to predict
future sales of his videos
• He believes that there is a correlation between the number
of sales he will make and the number of houses that have a
VCR.
• He compiles some data and makes a chart:

9. Example
• Treating the data as ordered pairs he makes a “scatter plot” of
the data:

10. Example
• There appears to be a “linear” relationship between the data.
• They all line up pretty nicely to a straight line.
• The data has a HIGH positive correlation

11. But what is the correlation coefficient?
• There is a nasty formula we could use to find it
that looks like this:
• We won’t be using that (Thankfully)
• We will be using Technology!

12. Regression line
• An equation that best describes the data.
• Remember an equation of a line gives you
each point, so we can use this to predict!
• From the technology we got:
y = 2.81 x - 15.12
X = households with VCRS ( in millions)
Y = Sales

13. Homework
1. Think about 2 things that might be correlated.
2. Create a hypothesis (or a prediction)
3. Poll at a minimum 10 people.
4. Record your data in a Google spreadsheet
Remember there needs to be 2 columns
5. We will test your hypothesis tomorrow.
Example:
• Will the number of students who are absent vary according
to the temperature?
• Does the color of one’s car correlate to their income?
• Will music help students study and if so what kind?