I need help solving this problem. The aim is conduct a simple linear regression with stress as the independent variable (IV) and timedrs as the dependent variable (DV). The purpose is trying to predict the number of visits a person takes to the doctor (timedrs) with how much stress they have in their life (stress). From the table below, please provide the following.

1. Report the overall model significance using the F-test results.

2. Report R and interpret the percentage of variance explained: Model fit: R = [value], indicating that [percentage]% of the variance in [Dependent Variable] is explained by [Independent Variable].

3.Report the regression equation using the intercept and slope coefficients: Dependent Variable] = [intercept] + [slope] * [Independent Variable]

4.Interpret each term: The intercept (value) represents the predicted [dependent variable] when [independent variable] is zero. The slope (value) indicates how much each one-unit increase in [independent variable], the dependent variable is predicted to [increase/decrease] by [value] units.

Linear Regression

Model Fit Measures
	Overall Model Test
Model	R	R	F	df1	df2	p
1		0.294		0.0864		42.6		1		451		<.001>

Model Coefficients - timedrs
Predictor	Estimate	SE	t	p
Intercept		3.2952		0.60834		5.42		<.001>
stress		0.0166		0.00255		6.53		<.001>

Q-Q Plot

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