1.

Using the MM207 Student Data Set: a) What is the correlation between student cumulative GPA and the number of hours spent on school work each week? Be sure to include the computations or StatCrunch output to support your answer. My answer :

0.27817234

(from StatCrunch):

Correlation between Q10 What is your cumulative Grade Point Average at Kaplan University? and Q11 How many hours do you spend on school work each week? is: 0.27817234

b) Is the correlation what you expected? My answer: No. I expected the correlation to be much higher because the more hours you study should equate to a much higher GPA – in theory that is.

c) Does the number of hours spent on school work have a causal relationship with the GPA? My answer: Yes. I was going to say no (because of the low correlation above), until I did a scatter plot. This shows that

MM207 Final Project there definitely is a casual relationship between study time and GPA.

yuck. There are 2 points on the right that most likely could be excluded. d) What would be the predicted GPA for a student who spends 16 hours per week on school work? Be sure to include the computations or StatCrunch output to support your prediction. My answer: 3.6

from StatCrunch Group by: Q11 How many hours do you spend on school work each week? Q11 How many hours do you spend on school work each week? 3 4 5 6 7 8 10 11

Mean 3.6666667 2 3.3775 3.0714285 3.75 3.352 2.9693334 3.6466668

n 3 1 8 7 2 5 30 3

Variance 0.33333334 NaN 0.3129357 0.42641428 0.125 0.26252 1.6706271 0.14423333

MM207 Final Project

12 13 14 15 16 hours

3.290909 4 3.93 3.7127273 3.6

11 2 2 11 3

1.4214091 0 0.0098 0.11040182 0.07

2.

Select a continuous variable that you suspect would not follow a normal distribution. a) My answer: my continuous variable is “Age” b) Create a graph for the variable you have selected to show its distribution. My answer:

MM207 Final Project a) Explain why these data might not be normally distributed. My answer: These may not be normally distributed due to the fact that people of all ages go to school – you will notice that all values are not tightly gathered around the mean.

b) Select a second continuous variable that you believe would approximate a normal distribution My answer: my continuous variable is “Height” c) Create a graph to show its distribution. My answer:

MM207 Final Project d) Explain why these data might be normally distributed. My answer: People are different heights of course, however you see an obvious tighter grouping around the mean; suggesting these values are closer to a normal distribution.

3.

Jonathan is a 42 year old male student and Mary is a 37 year old female student thinking about taking this class. Based on their relative position, which student would be farther away from the average age of their gender group based on this sample of MM207 students? My answer: Jonathan from StatCrunch Summary statistics for Q2 How old are you?: Group by: Gender Gender Female Male n Mean Variance Std. Dev. Std. Err. Median Range Min Max Q1 Q3 37 35 44 38 21 24 65 62 30 28 46 51

138 37.746376 104.015495 10.198799 0.86817944 35 38.8 160.28235 12.660267 2.1399755

4.

4. If you were to randomly select a student from the set of students who have completed the survey, what is the probability that you would select a male? Explain your answer. My answer: 0.2 35 males+138 females+2 no gender listed=175 students total That makes the probability equal to 35 males/175 total students or 35/175 = 0.2 Calculations Calculator says 0.2 Turn that into a percent = 20%

MM207 Final Project

from StatCrunch Frequency table results for Gender: = 173 count + 2 that did not list gender = 175 students total Group By: Gender Results for Gender=Female Gender Frequency Relative Frequency Female 138 1

Results for Gender=Male Gender Frequency Relative Frequency Male 35 1

5.

Using the sample of...