_class activity march 8.docx

pdf

School

University of Mississippi *

*We aren’t endorsed by this school

Course

351

Subject

Statistics

Date

Apr 3, 2024

Type

pdf

Pages

Uploaded by AmbassadorRockDove22

In-Class Activity March 8 ES 351 Measurement and Statistics in Exercise Science To receive credit for today’s in-class activity, complete all tasks according to instructions. Then, share this worksheet (one per team), via Google Drive, with 1. Connecting content: 1.1. Which of the following correlation coeﬃcients represent the strongest linear relationship between two variables? A. -0.75 B. +0.63 C. 0.00 D. -0.12 2. Data Collection: Today, you will be working with the handgrip strength and forearm circumference data you collected in a previous class. In a previous assignment, you were tasked with correcting the forearm circumference data by converting the measurements in inches to cm. I have created a new datasheet titled es351 grip corrected , with all of the data in the correct units, meaning you do not have to correct the data in this datasheet. Download a copy of this datasheet as a csv file, then open it in Jamovi and complete the application tasks in section 3. 3. Application: Complete the following analyses in Jamovi. 3.1. In your March 6 assignment, for task 2.1, I asked you to hypothesize what you think the correlation is between dominant arm grip strength and forearm circumference. Copy and paste your response for that task here. Response : strongly positive because there is a direct relationship between forearm circumference (indicative muscle mass) and grip strength. As a forearm circumference increases, grip strength tends to increase proportionally. 3.2. Create a bivariate scatterplot between the strength and circumference variables to assess their potential relationship (add boxplots in the margins). Use the screenshot to the right as a guide.

Screenshot of scatter plot : 3.3. Does it look like there is a potential relationship between the two variables? A. Yes B. No 3.4. What type of model/relationship looks to best fit the variables? A. Linear B. Quadratic C. Exponential D. Sinusoidal 3.5. If your answers to 3.3 and 3.4 are both A, run a correlation analysis to quantify the direction and strength of the relationship. Use the screenshot as a guide : N/A

3.6. Report the correlation coeﬃcient (Pearson’s r) as you see it in Jamovi. Response : .208 3.7. Report the 95% confidence interval for the correlation coeﬃcient as you see it in Jamovi. Response : 95% CI Upper: .539 95% CI Lower: -.179 4. Critical Thinking: 4.1. What does the correlation coeﬃcient that you reported in 3.6 tell us about the direction and strength of the linear relationship between grip strength and forearm circumference? Response : It tells us that it is positively correlated (up to the right). It also shows that it has a lower correlation (strength) since .208 isn’t very close to 1. 4.2. Would ﬂipping the order of the variables change the correlation coeﬃcient? Can test it out if you are unsure. Just ﬂip the order of the variables, make x become y, and y become x. To do this, just change the order that you drag them into the box in Jamovi. Response: No, the relationship is still the same. The axis doesn’t affect the strength of the relationship, it will just change the way the graph looks. 4.3. Complete the following statement by filling in the blank: Higher values of forearm circumference are associated with ____________ values of grip strength. Response: Higher 4.4. What exactly does the 95% confidence interval for the correlation coeﬃcient tell us? Response: Because the true population mean is unknown, this range describes possible values that the mean could be. 4.5. Based on the correlation, can we say which variable causes the other? Why or why not? Response: No we cannot say which causes which since there are some outliers or extreme values in our data. As we see there are some samples with smaller forearms but much greater grip strength. But we also see that on the higher end of forearm circumference, we see higher grip strength as well. Since we are unable to account for outside variables such as prior strength training or body composition this limits us to determine causation.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version