Question

Suppose a researcher RW decides to give his nine participants a series of 50 photographs. He asks the participants to rank order (from 1-50) how well-dressed the individuals in the photograph were and to rank order (from 1-50) how intelligent they thought the individuals in the photograph were. One of the photographs was of a well known actor. The well-dressed rankings and the intelligence rankings for this one photo are as follows.

WELL-DRESSED                                INTELLIGENT

14                                                        12

35                                                        39

16                                                        17

41                                                        43

39                                                        40

25                                                        21

26                                                        27

11                                                        9

24                                                        25

RW performs a simple linear regression (with WELL DRESSED as the Independent Variable and INTELLIGENT as the Dependent Variable) and obtains the following results.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.988a	.975	.972	2.10093
a. Predictors: (Constant), welldressed

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	1215.992	1	1215.992	275.492	.000b
	Residual	30.897	7	4.414
	Total	1246.889	8
a. Dependent Variable: intelligent
b. Predictors: (Constant), welldressed

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	-3.180	1.886		-1.686	.136
	welldressed	1.133	.068	.988	16.598	.000
a. Dependent Variable: intelligent

From these results, he concludes

1. that being well dressed causes people to view you as intelligent.

2. that the linear relationship between well-dressed ratings and intelligence ranks for the photos is Intelligence=-3.180(Well Dressed) + 1.133.

3. that the null hypothesis (b=0) has a probability =.000

4. that since it is desirable to get an R²=1, it may be necessary to add more Independent Variables to the analysis.

5. that with this type of analysis one doesn’t have to worry about being overly influenced by extreme values.

Your task is to answer the following about the above project.

Do you see any problems with the design of the study, the data analysis run, or the results reported?

Are there analyses that should have been run that weren’t? What would you have done? Why?

Do you see any errors in the conclusions reached? If so, what should have been (or should not have been) concluded?

If you feel that other alternative statistical analyses should have been performed, then do them. If you do alternative analyses include any results you have obtained

Answer 1

Answer:

Regression model is :

Intelligents =

Intelligents = - 3.180 + 1.133 * welldressed

the intelligents predicted to 1.133 welldressed incresed by per one unit of intelligents.

The intelligents -3.180 decresed by the one unit of intelligents. IF welldressed zero so mean of intelligents is -3.180

97.5% indicates that the model explains all the variability of the intelligents data around its mean.

This model is very good fit for the given data set.

Regession sum of square is significant of 0.05 level of alpha

NOTE:: I HOPE YOUR HAPPY WITH MY ANSWER....***PLEASE SUPPORT ME WITH YOUR RATING...

***PLEASE GIVE ME "LIKE"...ITS VERY IMPORTANT FOR ME NOW....PLEASE SUPPORT ME ....THANK YOU