Question

Use the regression model I created below for U5MR [No. of deaths of children 0-5 yrs old per 1000 live births] and Female Youth LR [Percent of females 15-24 literate] to answer this question. Interpret the slope estimates that is interpret the impact Female Youth LR has on U5MR, then interpret the r square for example what % of what variation can be explained by what variable. Lastly calculate the predicted values U5MR when Female Youth LR= 80.

Regression Statistics
Multiple R	0.784325
R Square	0.6151657
Adj R Square	0.6122054
St Error	24.310128
Observations	132
ANOVA
	df	SS	MS	F	Sign F
Regression	1	122810.6827	122810.7	207.8077	9.78E-29
Residual	130	76827.70366	590.9823
Total	131	199638.3864
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	183.3557	10.07001258	18.20809	1.405E-37	163.4334	203.278	163.433384	203.27802
98.85624013	-1.635329	0.113442129	-14.4155	9.781E-29	-1.85976	-1.4109	-1.8597609	-1.4108975

Answer 1

From the regression summary we have

intercept =183.3557

slope =-1.635329

so

U5MR = 183.3557 -1.635329*Female Youth LR

1)

Interpretation of slope: the slope is interpreted as the amount by which dependent variable changes on unit change in independent variable so here we have dependent variable U5MR and Independent variable is Female Youth LR

so in our case

the slope is interpreted as when we increase Female Youth LR by 1 unit then U5MR will decrease by1.635329

2)

R2 is the percentage by which variation in the dependent variable explained by the independent variable

so in our case

61.51657% of the variation in U5MR is explained by Female youth LR

3)

we have to find value of U5MR for female youth LR=80

so

U5MR = 183.3557 -1.635329*Female Youth LR

= 183.3557 -1.635329*80

=52.52938