Provided here is a dataset from a class. This data was collected from an 8-10am course that met every Tuesday and Thursday.   Answer all of the following questions.  Give your answers in sentences and/or copy/paste your Excel graphs/commands when used.    2019 Data           Gender Age Height (in) Shoe Size Miles Home to school Classes this Semester M 20 77 15 3 4 F 45 63 11 8 3 F 20 62 8.5 22 4 F 19 68 9 7.5 4 M 19 70 9.5 6.5 4 F 18 63 6.5 6.5 5 F 21 62 5.5 2 4 F 19 65 7 7 4 F 18 64 8.5 5.5 5 F 19 60 7 15 3 F 21 60 6.5 5 1 M 19 72 11 15 5 M 42 68.75 11 5 1 M 18 74 11.5 2.5 4 F 18 62 6.5 7.5 4 F 19 60 7.5 3 5 F 20 59 4.5 3.5 5 M 20 78 14 87 5 M 19 72.5 12 0.5 4 M 21 66 8.5 5 4 M 19 76 12 45 5 M 19 71 12 22 5 M 19 69 11 4.5 4 F 19 65 7 2 4 F 25 66 8 1 2 M 19 68 10 5 4 M 19 72 11.5 82 4 F 18 64 8.5 21 4 F 23 70 9.5 4 3 D) Find the mean and standard deviation of the heights of this sample of students. E)  The Empirical Rule states that IF a data set is Normally distributed, that approximately 68% of the observations should fall within ONE standard deviation of the mean.  Typically, heights tend to be normally distributed.  IF this sample of student heights is normally distributed, give the lower and upper boundary of heights where approximately 68% of student heights should lie.  F)  Use simple probability (no technology needed) to find the probability that, if I chose a random student from this list, that student would be between these two boundary heights found in part E.  G)  What can you conclude about this specific sample of students' heights?  Do you believe that they are "normally distributed"?  Why/why not?

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Provided here is a dataset from a class.

This data was collected from an 8-10am course that met every Tuesday and Thursday.   Answer all of the following questions.  Give your answers in sentences and/or copy/paste your Excel graphs/commands when used.   

2019 Data          
Gender Age Height (in) Shoe Size Miles Home to school Classes this Semester
M 20 77 15 3 4
F 45 63 11 8 3
F 20 62 8.5 22 4
F 19 68 9 7.5 4
M 19 70 9.5 6.5 4
F 18 63 6.5 6.5 5
F 21 62 5.5 2 4
F 19 65 7 7 4
F 18 64 8.5 5.5 5
F 19 60 7 15 3
F 21 60 6.5 5 1
M 19 72 11 15 5
M 42 68.75 11 5 1
M 18 74 11.5 2.5 4
F 18 62 6.5 7.5 4
F 19 60 7.5 3 5
F 20 59 4.5 3.5 5
M 20 78 14 87 5
M 19 72.5 12 0.5 4
M 21 66 8.5 5 4
M 19 76 12 45 5
M 19 71 12 22 5
M 19 69 11 4.5 4
F 19 65 7 2 4
F 25 66 8 1 2
M 19 68 10 5 4
M 19 72 11.5 82 4
F 18 64 8.5 21 4
F 23 70 9.5 4 3

D) Find the mean and standard deviation of the heights of this sample of students.

E)  The Empirical Rule states that IF a data set is Normally distributed, that approximately 68% of the observations should fall within ONE standard deviation of the mean.  Typically, heights tend to be normally distributed.  IF this sample of student heights is normally distributed, give the lower and upper boundary of heights where approximately 68% of student heights should lie. 

F)  Use simple probability (no technology needed) to find the probability that, if I chose a random student from this list, that student would be between these two boundary heights found in part E. 

G)  What can you conclude about this specific sample of students' heights?  Do you believe that they are "normally distributed"?  Why/why not?

SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Sq 0.790411473
Standard Error 2.477019721
Observations
0.89325068
0.797896777
29
ANOVA
df
Significance F
SS
MS
Regression
Residual
Total
654.0277343
654.0277343
106.5950989
7.12498E-11
27
165.6619208
6.135626697
28 819.6896552
Lower 95.0%
Upper 95.0%
53.15712398
Coefficients Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
x Variable 1
49.5314004
1.767067776
28.03027766
1.69215E-21
45.90567681
53.15712398
45.90567681
1.891997735
0.18325338
10.32449025
7.12498E-11
1.515992858 2.268002613
1,515992858
2.268002613
Transcribed Image Text:SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Sq 0.790411473 Standard Error 2.477019721 Observations 0.89325068 0.797896777 29 ANOVA df Significance F SS MS Regression Residual Total 654.0277343 654.0277343 106.5950989 7.12498E-11 27 165.6619208 6.135626697 28 819.6896552 Lower 95.0% Upper 95.0% 53.15712398 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept x Variable 1 49.5314004 1.767067776 28.03027766 1.69215E-21 45.90567681 53.15712398 45.90567681 1.891997735 0.18325338 10.32449025 7.12498E-11 1.515992858 2.268002613 1,515992858 2.268002613
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