Pulse Project

Due date: Dec 13 (after exam)

Part I Data Cleaning (25 points)

1. You will find your data set in Canvas under the Pulse Project folder.  The name of the data set is pulseproject.xlsx.  This data set was created in our section of MATH-201 and will be considered a random sample of all MATH-201 students.  Read this data set into Statcrunch.

2. It is always a good idea to review your data set for extreme values that might be suspicious.  These extreme values can be due to typing errors during data entry or might be legitimate. If we identified an outlier, it might be possible to investigate each specific outlier to see if that person really exists.

3. Create a column of Pulse Differences (AEPL – BEPL). This will provide the change in pulse after 1 minute of exercise.

4. Create boxplots of all the variables (Height, Weight, BMI, Before Exercise Pulse (BEPL), After Exercise Pulse (AEPL), and Pulse Differences.   If the boxplots are difficult to interpret, you can also examine the summary statistics for these variables.  You do not need to include this information (boxplots and summary statistics) with your submission, but you should use them to identify any unusual data or outliers.

5. If there are any outliers, we won’t be able to identify and investigate the specific person in the data.  That is what we would normally do.  However, examine those outliers or unusual data and decide whether or not these subjects are legitimate or should be removed entirely from the data.  If you decide to remove someone, justify your decision.  There should be some basis in fact for the removal other than your feelings or opinion.

You should note any outliers and the entries you have deleted under your first report section “Data Cleaning”. Make sure you discuss why any of these entries were removed.

6. Congratulations.  Your data is now cleaned and ready for analysis.

Part II Research (20 points each question x 3 = 60 points)

Assume that the data set we are using represents a random sample of all MATH-201 students.  Further assume that the populations are normally distributed (they are).  That way we don’t have to worry about normal probability plots if any of our samples are below 25.  Thus, we will be basing our inference and conclusions on the population of MATH-201 students.

Below you will find three research questions.  You can use any statistical method you want (some are much better than others) to answer these questions, but you must outline your methodology (hypothesis testing, some type of graph, confidence interval, regression analysis, etc.).  After the “Data Cleaning” section, have a separate section for each research question.  State your research question first, followed by your answer to the question. You can then quickly discuss your methodology and provide the StatCrunch output as backup for your analysis and conclusions.

Spelling and Grammar (15 points)

Spell Checker is a good tool, but you need to look thru your paper for spelling, typos, grammar errors etc.

Research Questions (Any testing or inference should be done at 95% Confidence).

Also, make certain that you can do any required testing (normality issues, sample size, normal prob plots etc.).  If you are not able to answer the question for these reasons, just say so and move on to the next question.

1. (HINT: Chapter 11)

It is claimed that due to smaller lung capacity, a female’s heart must beat faster during exercise.  Given this information, do females have a higher mean after exercise pulse rate (AEPL) than men?  Perform. this test at a 95% level of confidence.

2. (HINT: Chapter 11)

It is claimed that one minute of exercise will increase a typical MATH-201 student’s pulse (mean pulse difference) by more than 15 beats per minute.  At a 95% level of confidence, evaluate this research objective (mean pulse difference is greater than 15 beats per minute).

Final Comments:

3. (HINT: Chapter 4)

a. Create a scatter diagram using BMI as the dependent variable and weight as the independent variable.  Describe the relationship between BMI and weight.

b. Using the fitted line plot function in StatCrunch, find the equation of the least-squares regression line.

c. Interpret the slope of the least-squares regression line for this model.

d. Interpret the intercept of the least-squares regression line for this model.

e. Predict the BMI of a student whose weight is 159 lb.

f. Predict the BMI of a student whose weight is 300 lb.

I am most interested that you use correct statistical analysis techniques and are able to communicate your results in a clear fashion.  Your brief report should be in Microsoft Word and be easy to follow and contain no typos, spelling, or grammar errors.  Points will be taken off for poorly written reports.  You can use the rubric given on the next page as a guide.