Regression Statistics Multiple R 094898 R Square 090056 Adju

Regression Statistics

Multiple R

0.94898

R Square

0.90056

Adjusted R

Square

0.82101

Standard Error

6.24921

Observations

10

ANOVA

df

SS

MS

F

Significance

F

Regression

4

1768.3366

442.0841

11.3202

0.0101

Residual

5

195.2634

39.0527

Total

9

1963.6000

Coefficients

Standard

Error

t Stat

P-value

Lower 95%

Intercept

48.3628

14.1772

3.4113

0.0190

11.9192

weight

-21.3770

9.7575

-2.1908

0.0300

-46.4595

height

-12.4787

7.3110

-1.7068

0.1486

-31.2723

power

-4.2240

8.8810

-0.4756

0.6544

-27.0534

speed

0.2849

0.3917

0.7272

0.4997

-0.7221

Using the above Excel summary output results, answer the following questions.

                     a) How many samples were collected to generate this data?

    b) What is the correlation for this multiple regression, and what does it indicate?

Please show detailed calculations.

Regression Statistics
Multiple R	0.94898
R Square	0.90056
Adjusted R
Square	0.82101
Standard Error	6.24921
Observations	10
ANOVA
df	SS	MS	F	Significance F
Regression	4	1768.3366	442.0841	11.3202	0.0101
Residual	5	195.2634	39.0527
Total	9	1963.6000
Coefficients	Standard Error	t Stat	P-value	Lower 95%
Intercept	48.3628	14.1772	3.4113	0.0190	11.9192
weight	-21.3770	9.7575	-2.1908	0.0300	-46.4595
height	-12.4787	7.3110	-1.7068	0.1486	-31.2723
power	-4.2240	8.8810	-0.4756	0.6544	-27.0534
speed	0.2849	0.3917	0.7272	0.4997	-0.7221

Solution

a) yo need 1 sample of 10

b)the correlation for this multiple regression is 94% and it is a measure of how well a given variable can be predicted using a linear function of a set of other variables. In this time it is a very good prediction.