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trapecia [35]
3 years ago
14

Thirteen students entered the business program at Sante Fe College 2 years ago. The following table indicates what each student

scored on the high school SAT math exam and their​ grade-point averages​ (GPAs) after students were in the Sante Fe program for 2 years.
Student A B C D E F G
SAT Score 421 375 585 693 608 392 418
GPA 2.93 2.87 3.03 3.42 3.66 2.91 2.12
Student H I J K L M
SAT Score 484 725 506 613 706 366
GPA 2.50 3.24 1.97 2.73 3.88 1.58
The​ least-squares regression equation that shows the best relationship between GPA and the SAT score is:_____.
Mathematics
1 answer:
gizmo_the_mogwai [7]3 years ago
7 0

Answer:

y=1.003009+0.003453x

or

GPA=1.003009+0.003453(SAT Score)

Step-by-step explanation:

The least square regression equation can be written as

y=a+bx

In the given scenario y is the GPA and x is SAT score because GPA depends on SAT score.

SAT score (X) GPA (Y) X²         XY

421           2.93 177241 1233.53

375          2.87         140625 1076.25

585          3.03 342225 1772.55

693          3.42  480249 2370.06

608          3.66 369664 2225.28

392          2.91  153664 1140.72

418           2.12  174724 886.16

484          2.5    234256 1210

725             3.24  525625  2349

506          1.97   256036 996.82

613           2.73  375769 1673.49

706          3.88 498436 2739.28

366           1.58  133956 578.28

sumx=6892

sumy=36.84

sumx²=3862470

sumxy=20251.42

n=13

b=\frac{(nsumxy)-(sumx)(sumy)}{nsumx^{2}-(sumx)^{2}  }

b=9367.18/2712446

b=0.003453

a=ybar-b(xbar)

ybar=sum(y)/n

ybar=2.833846

xbar=sum(x)/n

xbar=530.1538

a=2.833846-0.003453*(530.1538)

a=1.003009

Thus, required regression equation is

y=1.003009+0.003453x.

The​ least-squares regression equation that shows the best relationship between GPA and the SAT score is

GPA=1.003009+0.003453(SAT Score)

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6*2=12
32-12=20
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Hope this helps!
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Define z_alpha to be a z-score with an area of alpha to the right. For Example: z_0.10 means P(Z &gt; z_0.10) = 0.10. We would a
Reptile [31]

Answer:

a) P(-z_0.025 < Z < z_0.025)

For this case we want a quantile that accumulates 0.025 of the area on the tails of the normal standard distribution, and for this case we can calculate the z value with the following excel codes:

"=NORM.INV(0.025,0,1)"

"=NORM.INV(0.025,0,1)"

And for this case the two values are :z_{crit}= \pm 1.96

b) P(-z_{\alpha/2} < Z < z_{\alpha/2})

For this case we want a quantile that accumulates \alpha/2 of the area on the tails of the normal standard distribution, and for this case we can calculate the z value with the following excel codes:

"=NORM.INV(alpha/2,0,1)"

"=NORM.INV(alpha/2,0,1)"

c) For this case we want to find a value of z that satisfy:

P(Z > z_alpha) = 0.05.

And we can use the following excel code:

"=NORM.INV(0.95,0,1)"

And we got z_{\alpha/2}=1.64

Step-by-step explanation:

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".  

Solution to the problem

Part a

P(-z_0.025 < Z < z_0.025)

For this case we want a quantile that accumulates 0.025 of the area on the tails of the normal standard distribution, and for this case we can calculate the z value with the following excel codes:

"=NORM.INV(0.025,0,1)"

"=NORM.INV(0.025,0,1)"

And for this case the two values are :z_{crit}= \pm 1.96

Part b

P(-z_{\alpha/2} < Z < z_{\alpha/2})

For this case we want a quantile that accumulates \alpha/2 of the area on the tails of the normal standard distribution, and for this case we can calculate the z value with the following excel codes:

"=NORM.INV(alpha/2,0,1)"

"=NORM.INV(alpha/2,0,1)"

Part c

For this case we want to find a value of z that satisfy:

P(Z > z_alpha) = 0.05.

And we can use the following excel code:

"=NORM.INV(0.95,0,1)"

And we got z_{\alpha/2}=1.64

6 0
3 years ago
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