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Relationships between height and weight – Model Summary Table Looking at the Mod

ID: 3226092 • Letter: R

Question

Relationships between height and weight – Model Summary Table

Looking at the Model Summary table:

What is the value of R?

What does this tell us about the association between height and weight?

What is the value of R Square

What does this tell us about the model including height as a predictor of weight?

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.546a

.298

.297

9.234

a. Predictors: (Constant), weight in kgs

Relationship between height and weight – Anova Table

Looking at the Anova table:

What is the value of the F-Ratio?

What are your conclusions about the model based on this table?

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

26459.777

1

26459.777

310.339

.000b

Residual

62240.468

730

85.261

Total

88700.245

731

a. Dependent Variable: height in cms

b. Predictors: (Constant), weight in kgs

Looking at the Coefficients Table:

What is the Beta value for height?

How can we interpret this value with respect to prediction of weight?

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

133.728

1.896

70.538

.000

weight in kgs

.486

.028

.546

17.616

.000

a. Dependent Variable: height in cms

Looking at the Model Summary table:

What is the value of R?

What does this tell us about the association between height and weight?

What is the value of R Square

What does this tell us about the model including height as a predictor of weight?

Explanation / Answer

Answers-

Value of R is .546 . It suggests that there is moderate poistive relationship between weight and height.

Value of R2 is .298 . It suggests that only 29.8% of the variations in height variable is explianed by the weight variable.

Value of F statistic is 310.34 and crtical value is given by F1,730,.05 = 3.854 ( from F tables )

As F statistic is greater than critical value then we do reject the null hypothesis- Model is not significant.

Thus model is significant.

Value of beta = .486.

It means if weight is increased ( decreased) by 1 unit then weight gets increased ( decreased ) by .486 units

TY!