Names:

Is There a Correlation between a Person’s Forearm Length and Their Height?

Data Collection Lab

Purpose: The purpose of this activity is to conduct a scientific inquiry using real, student-gathered data.

  1. What question are you seeking to answer in this activity?
  2. What is your hypothesis? (Remember, hypotheses are testable statements.)
  3. What is the null hypothesis for this activity? (Remember, null hypotheses are the default statement that there is no relationship between the variables.This can be the same as your hypothesis.)
  4. What are the 2 variables being explored?
  5. What materials will you use for this investigation?

Procedure:

  1. Each of you will measure the forearm length and height of each of your partners. The forearm extends from the elbow to the wrist.
  2. This will result in 3 measurements for each person, one done by each partner. If you are in a group of 3, someone will have to measure twice. The measurements may not all be the same. This is expected, and is completely fine.
  3. Write these data into the following table, then find the average of each for all group members:

Student Name / Arm Measurement (cm) / Average / Forearm Length (cm) / Average
  1. In the computer lab, log in to Google Classroom and download the Excel file (Arm Length vs. Height.xlsx). Then, click the link to go to the Google Sheets page, where you will insert your data and get the data from the rest of the class.
  2. Input the averages for each student into the Google Sheets table by clicking the link on the Google Classroom Assignment. You must then copy the rest of the class’s data into your own Excel table. As you do so, a graph will automatically be generated for you.
  3. When you are done, click in the graph area.
  4. Go to “Design” and click on “Add Chart Element”
  5. Under “Trendline,” you will select “More Trendline Options.” If it does not automatically do so, select the symbol that looks like 3 parallel lines.
  6. Select “Linear” trendline. Excel will automatically fit it to your data.
  7. Scroll to the bottom of the trendline options and click the check marks for “Display Equation on Chart” and “Display R-squared value on chart.”
  8. Next, label the X-axis and Y-axis, using the proper units on each.
  9. Finally, come up with a descriptive title that explains what the graph is showing. In the title, you should include what the dots represent, and the sample that the data came from (i.e. the class name, period, and year.) The title should not be a question.
  10. Save the data table and graph with the title “Data Collection Lab, Name 1, Name 2, Name 3, etc.,” including all group member’s names. This file should then be turned in to Google Classroom.
  1. Results:
  2. Do all of the data fit directly onto the line?
  3. Were there any extreme outliers (points that did not fall directly on the line)?
  4. If so, did they fall above the trendline or below it?
  5. Mr. Bambrick’s daughter, Zoey, is 62cm tall. Using your equation from the trendline, how long should her forearm be? (Note: 1 inch is 2.54 cm) .Her actual forearm length is 10 cm. Use the equation for percent error to see how far off it is. = ______(The lines mean “absolute value.” You should not get a negative number.)
  6. The coefficient of determination, R-squared, tells you how well the data fits the trendline. The closer to 1 the R-squared value is, the better the data fit the line. A value of 0 shows that there is no correlation between the variables.
  7. What is the R-squared value for your data?

For the purpose of this activity, we will consider anything over .9 to be acceptable to support that there is some relationship between forearm length and height. Using data from your graph, write a 5-7 sentence conclusion to the activity. Use proper grammar (including full sentences), spelling, and punctuation. Start by explaining what the purpose of the investigation was. Next, restate your hypothesis. Concisely explain what you did in the investigation to gather the data, and then state that you used Excel to find a trendline and calculate the R-squared value. Then, tell whether your hypothesis was supported or whether it should be rejected. Use specific data (R-squared values and outliers) to support your conclusion. Explain what sources of error might have impacted your results (i.e. measurement differences, inaccurate metersticks, etc.), then tell what you would do to fix those if you were to redo the investigation.