The administrator of a school board in a large county was analyzing the average mathematics test scores in the schools under her control.  She noticed that there were dramatic differences in scores among the schools.  In an attempt to improve the scores of all the schools, she isolated factors that could account for the differences and were easily obtained from HR.  Accordingly, she took a random sample of 40 schools across the county and for each determined: Test Score, Y:     the mean test score last year at the school sampled Math Dgr, X1:   the percentage of teachers in each school who have at least one university                              degree in math Age, X2:               the mean age of the math teachers at the school sampled Income, X3:        the mean annual income of the math teachers at

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The administrator of a school board in a large county was analyzing the average mathematics test scores in the schools under her control.  She noticed that there were dramatic differences in scores among the schools.  In an attempt to improve the scores of all the schools, she isolated factors that could account for the differences and were easily obtained from HR.  Accordingly, she took a random sample of 40 schools across the county and for each determined:

Test Score, Y:     the mean test score last year at the school sampled

Math Dgr, X1:   the percentage of teachers in each school who have at least one university

                             degree in math

Age, X2:               the mean age of the math teachers at the school sampled

Income, X3:        the mean annual income of the math teachers at the school sampled     

Data:

Test Score Math Dgr Age Income
73.9 77 52 44.4
59.4 48 32 49
64.6 33 50 52.6
59.8 25 43 39.6
58.8 25 40 40.7
58.7 39 33 38
68.5 71 37 25.1
54.7 24 48 46.5
61.7 25 47 38.4
81.6 49 50 44.6
83.7 56 59 54.8
62.5 46 40 38.5
68.2 75 41 44.8
51.1 25 51 39.9
44 46 38 55.6
65.7 68 45 44.1
69.6 27 49 47.5
54.5 23 31 40
56.5 48 38 40.2
44.8 41 38 28.3
72.6 79 39 44.8
65.3 76 43 34.5
64.9 31 39 28
69.8 63 49 51.4
75.3 72 49 55.2
57 30 23 24.6
52.8 62 34 29.7
71.3 75 36 32.1
55.2 43 35 32.6
62.6 64 50 37.4
58.2 44 45 42.9
71.8 46 29 34.4
69.9 35 39 45.6
70.1 63 34 52.9
66.1 56 36 22.1
71.7 57 59 55.1
68.8 41 40 33.7
45 27 40 19.8
61.9 37 44 48.2
56 36 56 55.9

a. Conduct a simple linear regression analysis for each of the variables predicting mean test score and fill in the table below. (See picture attachment, I tried adding the equations but I'm unsure if that was right.) 

 

Variable
Equation
Math Dgr
50.651+0.2634X
Age
47.305+0.3786X
Income
51.27+0.2925X
Transcribed Image Text:Variable Equation Math Dgr 50.651+0.2634X Age 47.305+0.3786X Income 51.27+0.2925X
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