2022 Project Part 6 –Correlation, Regression, and Prediction
Points on Rubric: 20 Submit as a file upload
For this portion of the project, use the data from your two quantitative variables to
estimate the best fitting line through your data. Select one of your variables (the one you
would like to predict) as the dependent variable, “y” and the other variable as the
independent variable,” x”.
You can use Excel, Stat crunch, or a TI83/84 calculator to perform a linear regression
analysis. If you are using a computer program, such as Excel or Stat Crunch, you may want to
copy/paste your regression table results into your report. If you are using Excel, there is an
option to display the “trend line” with the equation and R-sq. value directly on the scatterplot.
Your project should be submitted in a semi-professional looking report, preferably typed
in Word. Include the following in your report.
1. Introduction: Briefly describe your sample and your two quantitative variables, including
their units of measurement. State that the purpose of your analysis is to use linear regression to
predict “y” (fill in what your “y variable” is) for any given value of “x” (fill in what your x
variable is).
2. Insert a scatter plot. Make sure your “x” (independent) variable is on the “x-axis” and the
“y” (dependent) variable is on the “y-axis”.
3. Find & practically interpret the correlation coefficient (r)
4. Find & practically interpret the coefficient of determination (R )
5. Find the equation on of the least-squares line (be specific )
6. Find & practically interpret the slope (be specific )
7. Find & interpret the y-intercept if an interpretation is possible. If it’s not possible, just
report the y-intercept and explain why it cannot be interpreted practically
8. Brief conclusion or discussion of the results (e.g. assumptions, potential problems, are the
results what you expected? use the model to make a prediction, etc.)
Note: Regression line, line of best fit, and least-squares line all refers to the same thing.
Suggestions: Take a moment to think about which variable you would like to make your
dependent (y) variable and which variable you would like to make your independent (x) variable.
The y variable is the one you would like to predict based on x. Make a note of which one you
selected for y and which for x, then when going through your interpretations, don’t mix them up.
There is a tutorial on how to create scatter plots and find the least-squares regression line in Stat
Crunch and Excel. While the tutorial will walk you through how to get your results, it does not
show you how to do the interpretations. I would recommend re-watching the videos on
regression (or viewing the example attached). In Stat Crunch, there is an option to “group by”
when doing a regression analysis. Do NOT use this option.
Type
Ford edge
Ford Escape
Ford Expedition
Ford Explorer
Ford fusion
Ford Mustang
Chevy Blazer
Chevy Trax
Chevy Bolt
Chevy Camaro
Chevy Corvette
Chevy Equinox
Chevy Impala
Chevy Malibu
Chevy Sonic
Chevy Spark
Chevy Suburban
Chevy Tahoe
Chevy Traverse
Audi A3
Audi A4
Audi A5
Audi A6
Audi A7
Audi A8
Audi E-Tron
Audi Q3
Audi Q5
AudiQ7
Audi Q8
Audi TT
Milages Seating
24
5
16
5
23
9
25
7
22
5
17
4
19
5
25
5
119
5
20
4
20
2
31
5
22
5
32
5
28
5
33
4
16
9
20
9
25
8
27
5
27
5
17
4
25
5
25
5
21
5
74
5
22
5
24
5
20
5
19
5
26
4
milages
seating
sample size
mean
SD
min
Q1
median
Q3
max
31
31
27.87096774 5.290322581
19.71419444 1.553352254
16
2
20
5
24
5
26.5
5
119
9
Type
Ford
Chevy
Audi
Total
Frequency
Proportion
6
0.1935
13
0.4194
12
0.3871
31
1
Frequency BarChart Type Vehicle
14
12
10
8
6
4
2
0
Ford
Chevy
95% CI
Mode
n
x
25
31
9
p-hat
0.290323
SE
0.081525
E
0.159786
Lower
Upper
0.131
0.450
y BarChart Type Vehicle
Chevy
Audi
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