CSI: Catch the Chocolate Thief!!
Stats 3.1 & 3.2
You will be able to provide one or more of these skills for your group today:
Using Excel
Measuring in the Metric system
Using a Tablets
Stick-with-it-ness
Using Nspires
Building and Analyzing Scatter Plots
Hand Span Info
CSI Skills
Deductive Reasoning
Computing the Correlation r-Value
Collecting Evidence
Least Squares Regression Lines
Are you Clever and Sneaky?
Graphing and Calculating Residuals
Roles:
RM: Team Questions, Make sure everyone has supplies, Organize clean up
Captain: Make sure everyone measures really accurately, Read task card steps carefully
RR: Make sure all data gets recorded, Enter group data onto the tablet holding all data
Facilitator: Watch the time, Keep the group moving, Help “measure twice, record once”
CSI: Catch the Chocolate Thief!!
Stats 3.1 & 3.2
The Crime!
 Mrs. Kruzich keeps a secret supply of chocolate bribes in her desk, saving the best chocolate for herself
in serious chocolate emergency situations. Over the past few days, some of her favorite chocolate has
disappeared, when she went to eat lunch in the staff lounge. The people with keys to Mrs. Kruzich’s
classroom include all other Liberty staff members!! She asks her colleagues whether they have been
making withdrawals from her chocolate drawer. No one confesses to the crime. However, her astute
deduction skills have narrowed the list to only those that know about the secret chocolate drawer.
The next day she catches a break in the investigation! More chocolate disappeared, but the thief has left a clear handprint on the drawer. The
careless culprit has left behind crucial evidence. At this point, Mrs. Kruzich calls in the CSI Stats Team (LHS AP Statistics) to help identify the
prime suspect in “The Case of Catch the Chocolate Thief”.
Supplies: Tablet, Ruler, Pencil, Scratch Paper
Task Number 1
 Your group needs to help collect a sample of a person’s hand span length and compare this to their height. By using this data, and the list of
suspects currently being collected by Mrs. Kruzich, you will help determine who the prime suspect is. This person will be interrogated until they
confess!!
1. Measure the height and hand span of each member of your group to the nearest centimeter (cm).
 Hand span is the maximum distance from the tip of the thumb to the tip of the pinkie finger on a person’s fully stretched-out hand.
2. The RR needs to record your group’s data onto the classroom excel spreadsheet. Find the tablet being used for this.
 There should be two columns to complete on excel.
Hand Span (cm)
Height (cm)
3. Next class, you will obtain all the data from all AP Statistics students, and analyze it to
compare the suspects. Stay Tuned…
CSI: Catch the Chocolate Thief!!
Stats 3.1 & 3.2
The Suspects – Who is “The Chocolate Thief”?
Mr. Valach?
Mr. Stookey?
Mrs. Leake?
Mr. Hall?
Mrs. Phillips?
Mr. Kennedy?
CSI: Catch the Chocolate Thief!!
Task Number 2 - For task 2, complete all work on a Surface Pro Tablet.
 Your group needs to determine who The Chocolate Thief is by analyzing all collected data.
 You may wish to collect more evidence outside of class! Take this into consideration when determining the thief.
 All final products for chapter three are due by _________________________.
 Download the class’s hand span and height data from the Homedirs file within lhssvr02.
 Go to Kruzich’s Folder
 Go to the AP Statistics Folder
 Then open and download the Excel Chapter 3 Chocolate Data
 Using Excel, make a scatter plot of the class data.
 Insert – Scatter
 Go to the Layout Menu
o Fix the Chart Title
o Rescale your horizontal and vertical axes
o Title you horizontal and vertical axes
o Remove the Legend
o Change any other features if they improve the scatter plot.
 Graph a least squares regression line
o Click on one of the points on the graph
o Then right click to Add Trendline
o Select Linear and Display Equation on chart and Display r-squared value on chart
 Find the correlation coefficient (hint: undo the square of r)
 In Class - Collect measurements of the handprint found on Kruzich’s desk.
 Outside of Class – Collect Official statements from all six suspects – Be Professional!!
 Using your graph analysis, can you determine who The Chocolate Thief is?
Final Product
 Your whole group will present your CSI findings to the class as a PowerPoint presentation
o You may use an equivalent like a Google presentation…highly recommended for sharing.
 Include all statistical data - scatter plot showing, correlation coefficient and the fitted least-squares line.
 Include all other data, thoughts or collected information that supports your analysis of who is The Chocolate Thief.
 Of course include your conclusion of The Chocolate Thief ‘s identity.
Stats 3.1 & 3.2
CSI: Catch the Chocolate Thief!!
Stats 3.1 & 3.2
Task Number 3 - Track and Field Day
The table below shows data for 13 students in a statistics class.
Each member of the class ran a 40-yard sprint and then did a
long jump (with a running start).
 For task 3, complete all work on an Nspire Calculator.
 There is an exit ticket to complete for task 3 and 4.
1. Make a scatter plot of the relationship between sprint time
(in seconds) versus long jump
Sprint Time (s)
5.41 5.05 9.49 8.09 7.01 7.17 6.83 6.73 8.01 5.68 5.78 6.31 6.04
distance (in inches).
Long Jump Distance (in) 171 184 48
151 90
65
94
78
71
130 173 143 141
2. Interpret the scatter plot by discussing direction, form, strength and outliers.
3. Determine the correlation r for this data set.
o See page A10 (Appendix B) for Nspire instructions…
o Explain what the r value means in the context of this problem
o What other information did your calculator give you?
o What effect would removing the student at (8.09, 151) have on the correlation?
o What effect would removing the student at (9.49, 48) have on the correlation?
4. Least-Squares Regression Line.
o From your scatter plot page, go to Menu, Analyze, Regression, Show Linear
o Record your equation findings
o Now plot the residual squares onto your scatter plot
 Menu, Analyze, Residuals, Show Residual Squares
o What do these squares mean?
5. Find and interpret the residual for the student who had a sprint time of 8.09 seconds.
CSI: Catch the Chocolate Thief!!
Stats 3.1 & 3.2
Task Number 4 – Used Hondas
The following data shows the number of miles driven and advertised price for 11 used Honda CR-Vs from the 20022006 model years (prices found at www.carmax.com).
For Task 4, complete all work on an Nspire.
1) Make a scatter plot of the relationship between miles driven and cost.
2) Interpret the scatter plot by discussing direction, form, strength and outliers.
 For strength please include the correlation r.
3) Create the Least-Squares Regression Line.
 Identify the slope and y intercept of the regression line.
 Interpret each value in context.
 Show the residual squares.
Thousand
Miles
Driven
22
29
35
39
45
49
55
56
69
70
86
Cost
(dollars)
17998
16450
14998
13998
14599
14988
13599
14599
11998
14450
10998
4) Predict the asking price for a car with 50,000 miles.
 Should we predict the asking price for a used 2002-2006 Honda CR-V with 250,000 miles?
5) Next, calculate the equation of the least-squares regression line by hand, if slope is b  r 

Sy
Sx
and the y-intercept is a  y  bx
Explain what change in price we would expect for each additional 19.3 thousand miles added to the car’s odometer.
6) If a Honda CR-V has 70 thousand miles then what would be the predicted
asking price?
 What is the residual?
 What are all the residuals?
 How can you calculate them quickly?
 Create a residual plot for these Hondas.
7) For the used Hondas data, calculate the standard deviation.
What does this mean?
CSI: Catch the Chocolate Thief!!
Stats 3.1 & 3.2
8) Suppose we wanted to estimate the asking price of a used 2002-2006 Honda CR-V from CarMax but didn’t know the number of
miles. The mean price of the other used CR-Vs would be a reasonable guess. The first scatter plot below shows the prediction
errors when using the mean price y as our prediction. The sum of the squared prediction errors is called the total sum of
squares SST and measures the total variability in the y-variable. In this case, SST = 36,070,000.
However, we could make much better predictions if we know the number of miles driven. How much better? The second scatter
plot shows the prediction errors when using the least-squares regression line. The sum of the squared prediction errors when
using the least-squares regression line is called the sum of squared errors SSE. In this case, SSE = 8,497,000.
8, 497,000
= 23.6% of the variation in asking price is unaccounted for by the least-squares regression line.
36,070,000
8, 497,000
The remaining variation is due to other factors, such as the condition or age of the car. Therefore, 1 –
= 76.4% of
36,070,000
the variability in asking price is accounted for by the least-squares regression line. This last percentage is r2 and should be
interpreted by saying “76.4% of the variation in asking price can be accounted for by the linear model relating asking price to
number of miles driven.”
This means that only
9) Compare your scatter plot with the least-squares regression line for predicting advertised price from number of miles (in
thousands) to a second scatter plot with least-squares regression line for predicting number of miles (in thousands) from
advertised price. Start a new page in your Nspire document.