STA-282QR Pulse Project
Part I Data Cleaning (25 points)
1. You will find your data set in Blackboard under the Project folder (Pulseproject.xlsx) and you should put this data on your desktop. This data set was created in an on-campus sections of STA-282 and will be considered a random sample of all STA-282 students. Note that the data has not been reviewed or cleaned prior to this assignment. That is your responsibility. Read this data set into StatCrunch (Data>Load>From File>On My Computer).
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. But in this case, we will examine the outliers, determined if they make sense or not, and take the appropriate action.
3. Note that there is a column of Pulse Differences (Difference = 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 (BEPL), 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, unless you choose to do so for illustration purposes, 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 attempt to do if the entries were not anonymous. However, examine those outliers or unusual data and decide whether or not these subjects are legitimate or should be removed entirely from the data. Use some common sense. For example, do you expect someone to have a higher before-exercise pulse than an after exercise pulse? Would you expect to find someone in the data that is 10 feet tall and weighs 500 pounds? Remember, if you decide to remove someone, you must 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 which entries were removed and why they were removed.
6. Congratulations. Your data is now cleaned and ready for analysis.
Part II Research (25 points each question x 3 = 75 points)
Assume that the data set we are using represents a random sample of all STA-282 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 STA-282 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.
Research Questions (Any testing or inference should be done at 95% Confidence):
1. 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. (Note: For StatCrunch, in the appropriate testing area, select the variable AEPL where gender=”female” for Sample 1 and the variable AEPL where gender=”male” for Sample 2. This will divide the column AEPL into two different groups. One for females and one for males. Then you can select the appropriate hypothesis test with group 1 being females and group 2 being males. Make sure the pooled variances box stays checked too. )
2. It is hypothesized that overweight people (high BMI) will have a higher pulse difference because their heart must work comparatively harder when exercising. Given this information, can we predict a person’s pulse difference (Y) using BMI (X)? In other words, is BMI a significant predictor of Pulse Difference?
3. It is claimed that one minute of exercise will increase a typical STA-282 student’s pulse (mean pulse difference) by more than 30 beats per minute. At a 95% level of confidence, evaluate this research objective (mean pulse difference is greater than 30 beats per minute).
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STA-282QR Pulse Project