As per the given results model can be written as
y = 12.325 + 389*math score - 2.010 female + 0.050 social studies score + 335 * reading score.
And R-square = 0.489
The model explains the 48.9% variability between the data.
Dependent variable is not given in the question, hence we have to check for the dependent variable for explaining the constants and coefficients of the model.
i) With increase in 1 unit of math score the dependent variable increases by 389 units
ii) the female has a negative effect on the dependent variable. If it is number of females then increasing in 1 unit of female reduces the dependent variable by 2.01 units
iii) With increase in 1 unit of social studies score the dependent variable increases by 0.050 units which is negligible.we may consider there is no effect of social studies score on the dependent variable.
iv) With increase in 1 unit of reading score the dependent variable increases by 335 units.
v) The meaning of constant can only be drawn by knowing the type of dependent variable.
Advanced statistic using R please solve on paper Please summarize the SPSS output below. What else...
Please solve on paper advanced statistic using R Please summarize the SPSS output below. What else do you need to know before relying on the results? (25 pts) 3- Model Summary Adjuste Std. Error of Model R Square R Square the Estimate 699a 489 479 a. Predictors: (Constant, reading score, female, social studies score, math score Coefficients Unstandardized Standardze oeffcentsCcients 35% Cortoerce imeni for B Model 8 624 535 5 061070 3859 0 12325 I 3194 389 07 2010 02310...
Advanced statistic using R please solve on paper 4- Please summarize the code and the related output below. (20 pts) > hyp.out <-glm(hypev-age_p+sex+sleep+bmi, data-NH11, family-"binomial") coef (summary (hyp.out)) Estimate Std. Error z value Pr(1zI) (Intercept) -4.26947 0.056495 -75.57 0.00e+00 age p 0.06070 0.000823 73.78 0.00e+00 sex2 Female-0.14403 0.026798 -5.37 7.68e-08 -0.00704 0.001640-4.29 1.78e-05 0.01857 0.000951 19.53 6.49e-85 cbind (predDat, predict (hyp.out, type "respons e", se. fit TRUE, interval="confidence", = newdata predDat)) age p sex bmi sleep fit se.fit residual.scale 1...