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Please help to find some data with at least 100 cases that to run a simple regre

ID: 2921587 • Letter: P

Question

Please help to find some data with at least 100 cases that to run a simple regression into the Spss software firstly, and making the SPSS output tables as a screen shot. Then use to answer questions by typed in detailly. Thank you so much!(please to answer all the 4 question by typed in detailly, and show the SPSS output table, please)

Questions:

1. Describe the variables. What are they? How are they coded?(detailly)

2. What is your prediction of the relationship between these variables? What do you expect to see?(detailly)

3. Compute and report the regression equation. What is the equation? Was your prediction supported? How much variance was explained?(detailly)

4. What are the practical implications of this relationship? If you collected additional data, do you think the relationship would replicate? Why or why not?(detailly)

Explanation / Answer

1. Describe the variables. What are they? How are they coded?(detailly)

Answer:

For this study, we choose two correlated variables such as number of study hours during test and test score. As we know that the score of test depends on the study time. Here, we consider the dependent variable or response variable as test score and independent variable or explanatory variable as the number of study hours. For the given two variables we use ratio scale of measurement. Numbers are coded as the nearest integers and no fractional number consider for the given two variables.

2. What is your prediction of the relationship between these variables? What do you expect to see?(detailly)

Answer:

We predict that there would be a strong positive linear relationship or association exists between the two variables number of study hours and test score. We expect to see the strong positive correlation between the dependent variable and independent variable.

3. Compute and report the regression equation. What is the equation? Was your prediction supported? How much variance was explained?(detailly)

Answer:

The required regression equation is given as below:

Test score = -4.961 + 9.986*Number of study hours

Y = -4.961 + 9.986*X

From the given regression output, our prediction is supported. The correlation coefficient between the given two variables is given as 0.993 which indicate a strong positive linear relationship or association exists between the two variables number of study hours and test score. The coefficient of determination or the value of R square is given as 0.986, which means about 98.6% of the variance or variation in the dependent variable test score is explained by the regression or independent variable number of study hours.

Regression

Descriptive Statistics

Mean

Std. Deviation

N

Test score

44.1700

23.02965

100

Number of study hours

4.9200

2.29043

100

Correlations

Test score

Number of study hours

Pearson Correlation

Test score

1.000

.993

Number of study hours

.993

1.000

Sig. (1-tailed)

Test score

.

.000

Number of study hours

.000

.

N

Test score

100

100

Number of study hours

100

100

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.993a

.986

.986

2.70074

a. Predictors: (Constant), Number of study hours

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

51791.301

1

51791.301

7100.564

.000a

Residual

714.809

98

7.294

Total

52506.110

99

a. Predictors: (Constant), Number of study hours

b. Dependent Variable: Test score

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-4.961

.643

-7.721

.000

Number of study hours

9.986

.119

.993

84.265

.000

a. Dependent Variable: Test score

4. What are the practical implications of this relationship? If you collected additional data, do you think the relationship would replicate? Why or why not?(detailly)

Answer:

Practically, we know that there is a relationship exists between the total time spend for study and test score. There would be some exceptional cases. Practically, this relationship indicates that if we need more test score, then we need to spend more time for study. If we collected additional data or completely new data, the relationship would be replicate and there would be no significant difference between the correlation coefficients; because a sample result always implies the population parameters.

Descriptive Statistics

Mean

Std. Deviation

N

Test score

44.1700

23.02965

100

Number of study hours

4.9200

2.29043

100

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