You used multiple linear regression analysis to predict community reintegration (Reintegration to Normal Living Index RNLI;...

You used multiple linear regression analysis to predict community reintegration (Reintegration to Normal Living Index RNLI; interval scale) from depression (Geriatric Depression Scale or GDS; interval scale) and balance (Berg balance scale; interval scale) in a sample of 200 individuals with stroke. The results are as follows:

Model Summary

R	R Square	Adjusted R Square	Std. Error of the Estimate
.670	.449	.431	14.40081

Predictors: (Constant), berg, depression

ANOVA(c)

	Sum of Squares	df	Mean Square	F	Sig.
Regression	10156.489	2	5078.244	24.487	.000
Residual	12443.002	197	207.383
Total	22599.491	199

Coefficients(a)

		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta	t	Sig.

	(Constant)	-6.662	14.146		-.471	.639
Berg	1.750	.288	.585	6.078	.000
	GDS	-.905	.320	-.272	-2.825	.006

a Dependent Variable: RNLI

Write up the unstandardized regression equation for predicting RNLI score for an individual with a Berg score of 35 and depression score of 11 (1 mark).
Identify the most important predictor of RNLI and provide justifications (1 mark)
Discuss implications of the results on clinical assessment (1 mark) and treatment (1 mark).
Discuss two implications of the results on future research .

Expert Solution

Please rate my answer and comment for doubt

orchestra answered 3 years ago

Develop a simple linear regression model to predict the Cost of Living Index based upon Restaurant...

Develop a simple linear regression model to predict the Cost of Living Index based upon Restaurant Price Index using a 95% level of confidence. Write the reqression equation. Discuss the statistical significance of the model as a whole using the appropriate regression statistic at a 95% level of confidence. Discuss the statistical significance of the coefficient for the independent variable using the appropriate regression statistic at a 95% level of confidence. Interpret the coefficient for the independent variable. What percentage...

Describe how simple linear regression analysis and multiple regression are used to support areas of industry...

Describe how simple linear regression analysis and multiple regression are used to support areas of industry research, academic research, and scientific research.

Linear Regression Linear regression is used to predict the value of one variable from another variable....

Linear Regression Linear regression is used to predict the value of one variable from another variable. Since it is based on correlation, it cannot provide causation. In addition, the strength of the relationship between the two variables affects the ability to predict one variable from the other variable; that is, the stronger the relationship between the two variables, the better the ability to do prediction. What is one instance where you think linear regression would be useful to you in...

The owner of Showtime Movie Theaters, Inc., used multiple regression analysis to predict gross revenue ()...

The owner of Showtime Movie Theaters, Inc., used multiple regression analysis to predict gross revenue () as a function of television advertising () and newspaper advertising (). Weekly Gross Revenue (s) Televison Advertising (s) Newspaper Advertising (s) 97 6 2.5 90 3 2 95 5 2.5 92 2.5 3.5 96 3 4.3 94 3.5 3.3 95 3.5 4.2 95 4 3.5 The estimated regression equation was . The computer solution provided . a. Compute (to 3 decimals). Compute (to 3 decimals). b....

The owner of Showtime Movie Theaters, Inc., used multiple regression analysis to predict gross revenue (y)...

The owner of Showtime Movie Theaters, Inc., used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x 1) and newspaper advertising (x2). The estimated regression equation was Weekly Gross Revenue ($1000s) Televison Advertising ($1000s) Newspaper Advertising ($1000s) 96 5 2.5 90 3 3 96 5 1.5 92 3.5 2.5 96 4 4.3 94 4.5 2.3 95 3.5 5.2 95 4 3.5 ŷ = 76.3 + 3.41 x 1 + 1.33 x 2 The computer...

1.Develop a multiple linear regression model to predict the price of a house using the square...

1.Develop a multiple linear regression model to predict the price of a house using the square feet of living area, number of bedrooms, and number of bathrooms as the predictor variables Write the reqression equation. Discuss the statistical significance of the model as a whole using the appropriate regression statistic at a 95% level of confidence. Discuss the statistical significance of the coefficient for each independent variable using the appropriate regression statistics at a 95% level of confidence....

1. A multiple linear regression model should not be used if: A The variables are all...

1. A multiple linear regression model should not be used if: A The variables are all statistically significant. B The coefficient of determination R2 is large. C Both of the above. D Neither of the above. 2. Consider a multiple linear regression model where the output variable is a company's revenue for different months, and the purpose is to investigate how the revenue depends upon the company's advertising budget. The input variables can be time-lagged so that the first input...

A multiple linear regression was performed using birth weight and gestational age to predict thyroid volume....

A multiple linear regression was performed using birth weight and gestational age to predict thyroid volume. Gestational age was included in the model because it is highly related to both thyroid volume and birth weight. The overall model was significant (p=.0001). Interpret the following regression results. Be sure to talk about all three parameters. Parameter Estimates Source DF Parameter Estimates Standard Error t-value p-value Intercept 1 336.31 168.35 2.00 0.0506 Birth Weight 1 0.1555 0.046 3.41 0.0012 Gestational Age 1...

Regression Make a distinction between simple linear and multiple linear regression. Can you think of examples...

Regression Make a distinction between simple linear and multiple linear regression. Can you think of examples in your business world where these techniques are or should be applied? Share the details, where possible.

Given the general MULTIPLE linear regression model with normal error, derive the LSE and MLE for...

Given the general MULTIPLE linear regression model with normal error, derive the LSE and MLE for the regression coefficients and show they are equivalent. I need help on this question. it needs to be for a multiple linear regression model. thank you in advance.

Question