Warm-Up: Find the slope of the line that contains the

```Warm-Up:
Determine the slope to be positive,
negative, zero, or undefined for the two
sets of relations and the graph.
1. (2,1),(4,2),(6,3)
2. (3,0), (5,0), (7,0)
3.
Warm-Up:
Determine the slope to be positive,
negative, zero, or undefined for the two
sets of relations and the graph.
1. (2,1),(4,2),(6,3)
Positive
2. (3,0), (5,0), (7,0)
No slope
3.
Negative
Gathering Data
“The bigger the better?”
 Today you are going to look at your hand and one(1)
centimeter cube. Now make a bucket of cubes.
 Take a moment to write that number on your data collection
sheet in Part 1.
 Now use the tape measure to measure your hand from the tip
of your 3rd finger to the bottom of your hand(meets the wrist).
Write that number using centimeters.
Use the picture to
make sure you
measure correctly.
“Scatter Plots and
Correlation”
Standard(s):
MAFS.8.SP.1.1
Construct and interpret scatter plots for bivariate
measurement data to investigate patterns of association
between two quantities. Describe patterns such as
clustering, outliers, positive or negative association, linear
association, and nonlinear association.
“Scatter Plots”
 Graphs of data pairs for a real-world situation rarely
fall in straight line.
 The arrangement of data can suggest a relationship
(correlation) than can be modeled to help us draw
Scatter Plots
 A scatter plot is a type of graph that relates 2
data sets by plotting the data as ordered pairs.
 They are used to determine the strength of a
relationship, or correlation, between 2 sets of
data.
 There are 3 general types of correlation that can
be determined using a scatter plot.
Positive Correlation
 In a data set with a positive correlation, you note
that as the domain increases, so does the range.
Basically, we see that as the x-values increase, so
does the y-value.
www.medizinfo.de
This scatter plot shows a positive correlation
between players height and weight, because
generally as the weight increases so does the
height.
This data shows that as the number of hours
worked increases, so does the amount of earnings.
We can surmise that the time worked determines
our pay.
Negative Correlation
 In a data set with a negative correlation,
you note that as the domain increases,
the range decreases.
www
This scatter plots shows a negative correlation
between the unemployment rate and the GDP.
This suggests that as the GDP increases the
unemployment rate decreases.
“Negative Correlation”
This scatter plot shows the relationship between
the amount of money left after a day of shopping
and the number of hours spent shopping.
“Negative
Correlations”
“Correlations”
we learned so far. Then use your whiteboards
and markers to write down 1 real-world scenario
that has a positive correlation and 1 real-world
scenario with a negative correlation.
 Identify the independent variable and the
dependent variable.
“No Correlation”
 A scatter plot with no correlation is one in
which a change in one data set has no effect on
the other.
 This type of graph has no recognizable pattern.
www.education.com
This scatter plot appears to have no correlation at
all. There is no pattern in how the x- and yvalues behave. Therefore, we can conclude that
there is no correlation.
“No Correlation”
 Some items with no correlation include:
1. Age and number of A’s earned
2. Earnings and ethnicity
3. Hair color and hair length
4. Height and nail length
5. Number of siblings and color of hair
Gathering “More” Data
We will conduct an experiment to
determine whether our hand lengths
affect the number of centimeter cubes we
can grab from the bucket of cubes on
Directions
 Take turns(one at a time) attempting to grab as many
centimeter cubes from the bucket of cubes in your group.
 You may not use any other body part(s) to assist you.
 If a cube falls, it cannot be counted in that trial.
 Complete 10 trials, recording the number of cubes on
your data collection sheet grabbed each time.
 After you complete a total of 10 trials, find the mean,
mode, and range of your data. The mean will serve as
 When prompted, you are going to “report out” one by
one.
 The x-value will be your palm size in cm
 The y-value will be your mean cube value
 Everyone’s data will be recorded on the board for you to
 Make sure you have as many data points as there are
people in the class.
Now, graph that data!!!
Analyzing the Data
 What similarities do you notice about the creation of the
graphs?
 What are the common differences evident in the scatter
 Do those similarities or differences change the
correlations in the data?
 Why? Or Why Not?
“Lesson Summary”
 In this lesson, we’ve looked at how useful scatter plots are in
helping us to determine correlations in 2 sets(bivariate) data.
 We understand that a set of data may imply a positive
correlation, negative correlation, or no correlation at all.
 We further understand how the correlations help us draw
conclusions about the data to further deepen our
understanding of related versus unrelated data sets.
 We can use this data to help us make predictions about values
not readily evident in a table of values or a set of ordered
pairs.
Ticket Out!!!
 Write positive, negative, or no correlation for the following
scenarios.
1. Age and dance ability
2. Earnings and ethnicity
3. Education and earnings
4. Hours worked and exhaustion
5. Hours of TV watched and quiz grades
6. Size of meal and caloric intake
```