# We Helped With This Statistics with R Programming Homework: Have A Similar One?

SOLVED Category Programming R | R Studio Graduate Solved Assignment Statistics

## Assignment Description

1. This problem is a follow up on problem 1 of assignment 5 about the regression through the origin model of the form

Y =β1x+ε, where ε is the standard normal distribution.

.      (a)  First, generate a predictor x as a random vector of length n = 100 from the standard normal and a response y as follows. y = 2x + ε

where ε is also a random vector of length n = 100 from the standard normal

that takes exactly the same values as x.

.            (b)  Perform a simple linear regression of y onto x, without an intercept.

Report the coefficient estimate βˆ1, the standard error of this coefficient estimate, and the t-statistic and p-value associated with the null hypothesis H0 : β1 = 0. Comment on these results. (You can perform regression without an intercept using the command lm(y x + 0).)

.      (c)  Now perform a simple linear regression of x onto y without an intercept. Report the coefficient estimate βˆ1, the standard error of this coefficient estimate, and the t-statistic and p-value associated with the null hypothesis H0 : β1 = 0. Comment on these results.

.      (d)  What are the relationships between (a), the results obtained in (b) and the ones in (c)?

.           (e)  For the regression of Y onto X without an intercept, the t-statistic for

H0 : β1 = 0 takes the form

T= B1 − β1 / S/ √ Sxx,

where S2= S S E / n-1    and

Note that these formulas are slightly different from those given in (slide 29, LinearRegression-1), since here we are performing regression without an in- tercept.

(f) Show algebraically (to receive bonus points), and confirm numerically in R, that the t-statistic above can be written as (values) .         (g)  Using the results from (d), argue that the t-statistic for the regression of y onto

x is the same as the t-statistic for the regression of x onto y.

.      (h)  In R, show that when regression is performed with an intercept, the tstatistic forH0 1 =0 is the same for the regression of y onto x as it is for the regression of x onto y. 4. The data set low bwt.txt contains information for a sample of 100 low birth weight infants. The variables are

sbp : maternal systolic blood pressuresex : gender of the baby

toxemia : toxemia during pregnancy (yes or no) germ.hem : germinal matrix hemorrhage (yes or no) gest.age : gestational age in weeksapgar5 : five-minute APGAR score

.      (a)  Using germinal matrix hemorrhage as the response, fit a logistic regression model where the predictor variable x1 is the 5-minute APGAR score. Write the equation and interpret β1, the estimated coefficient of

Apgar score.

.         (b)  What is the estimate and 95% confidence interval for the slope

(coefficient for apgar5) in the odds ratio scale? Interpret the estimate

(what does the odds ratio mean?).

.      (c)  At the 0.05 level of significance, test the null hypothesis: H0 : β1 = 0 where β1 is the coefficient for apgar5.

.      (d)  If a new infant from the population has an APGAR score of 3 what is the predicted probability that this child will experience a brain hemorrhage? What is the probability if the child’s score is 7?

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