Arizona State University Statistics Discussion

1. Create a correlation matrix of Pearson r correlations to determine the relationships between each of the performance variables. Paste an image of the correlation matrix below.
2. Which variable should be used as a predictor for sprint time?
3. Perform a linear regression analysis between sprint time and the variable you selected in #5 above. Report the regression equation below, along with the 95% confidence interval for the standardized B and intercept.

KIN 4040 – Module 5 Lab: Correlation, Prediction, & Mean Differences
Context: You are a the newly-appointed Sport Science Director at a mid-range NCAA DI
university. As one of the pioneers in your discipline, you hope to show the coaches and athletic
directors that scientific evidence and data analytics will lead to a more evidence-based practice
(EBP) approach, and benefit student-athletes long-term.
After implementing daily session RPE scores (Module 2 Lab), your next task is to set about
implementing a standardized performance testing process—known as an athlete monitoring
program—to collect and analyze performance data from the athletes under your care.
You decide that you want to collect data to assess the following parameters: strength, power,
and speed. Next, you determine that an ideal way to test these fitness characteristics is through
an isometric mid-thigh pull, a vertical jump test, and through a 30m sprint test. Unfortunately
for you, the university you work for does not have the funds to supply you with reliable timing
gates, and as a sport scientist you know that hand-timing is notoriously unreliable.
You decide to borrow timing gates from a colleague at another local school to use in your initial
testing, and then to “predict” sprint time based on either strength or jump height after that.
KIN 4040 – Module 5 Lab: Correlation, Prediction, & Mean Differences
This way, you can have an estimate of speed (predicted sprint time), and true values for power
(jump height) and strength (isometric peak force).
Finally, in this first testing timepoint you hope to assess whether there is a statistically
significant difference in mean scores between the male and female athletes you are testing.
Instructions: Using Excel and SPSS or JASP, answer the questions below. Show all of your work
in Excel using a new tab in the dataset file you download. Then type out the answers to the
questions below into this document, and use the “screenshot” function to paste in tables and
figures.* Keep the questions formatted how they are, and use black font for your answers.
*note that typically screenshot .jpeg files of tables and figures would not be an acceptable
format for research, but for the labs in this class they are fine.
1. Within the dataset, determine the mean, standard deviation, and range for each
performance metric. Report these statistics below:
2. Now, determine the mean, standard deviation, and range for each performance metric
for males only and females only (i.e. split by sex). Report these statistics below:
3. Is there a statistically significant difference between males and females on each of the
performance variables (hint, you are comparing differences between males and females
on 3 different variables)? Report the p-value, 95% confidence interval, and effect size for
each comparison.
4. Create a correlation matrix of Pearson r correlations to determine the relationships
between each of the performance variables. Paste an image of the correlation matrix
below.
5. Which variable should be used as a predictor for sprint time?
6. Perform a linear regression analysis between sprint time and the variable you selected
in #5 above. Report the regression equation below, along with the 95% confidence
interval for the standardized B and intercept.
7. Given your regression equation, predict the sprint times of the following athletes given
their jump height:
a. 0.53 m
b. 33 cm
c. 64 cm
d. 25 in
e. 0.13 m
8. Using previous research articles we’ve read in class, write up a results section based on
the statistics you’ve run for this lab. Be sure to report your statistics in APA format. Here
is a guide for correlations, and here is one for t-tests.