4. Trying to determine the number of students to accept is a tricky task for universities.The Admissions staff at a small private college wants to use data from the past fewyears to predict the number of students enrolling in the university from those who areaccepted by the university.R INPUT:accepted <- c(2440,2800,2720,2360,2660,2619)enrolled <-c(611,708,637,584,614,624)fit <- lm(enrolledaccepted)summary(fit)R OUTPUT:Coefficients:Estimate Std. Error t value Pr(>|t])(Intercept)accepted88.88272182.365840.4870.65150.208010.070022.9710.0411 *Signif. codes:***' 0.001 **' 0.01 '*' 0.05 '.' 0.1 ' 1Residual standard error: 26.35 on 4 degrees of freedom0.6881,Adjusted R-squared:0.6101Multiple R-squared:F-statistic: 8.824 on 1 and 4 DF, p-value: 0.04112(a) Write an equation for the regression line.(b) What is the slope of the least-squares regression line? Interpret this in the contextof the application.(c) Interpret the intercept of the line in context. Does the interpretation make sense?

4. Trying to determine the number of students to accept is a tricky task for universities. The Admissions staff at a small private college wants to use data from the past few years to predict the number of students enrolling in the university from those who are accepted by the university. R INPUT: accepted <- c(2440,2800,2720,2360,2660,2619) enrolled <-c(611,708,637,584,614,624) fit <- 1m(enrolled accepted) summary(fit) R OUTPUT: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 88.88272 182.36584 0.487 0.6515 accepted 0.20801 0.07002 2.971 0.0411 * --- Signif. codes: O **** 0.001 '**' 0.01 (*' 0.05 '.’ 0.1 ' ' 1 Residual standard error: 26.35 on 4 degrees of freedom Multiple R-squared: F-statistic: 8.824 on 1 and 4 DF, 0.6881, Adjusted R-squared: 0.6101 p-value: 0.04112 (a) Write an equation for the regression line. (b) What is the slope of the least-squares regression line? Interpret this in the context of the application. (c) Interpret the intercept of the line in context. Does the interpretation make sense?

4. Trying to determine the number of students to accept is a tricky task for universities. The Admissions staff at a small private college wants to use data from the past few years to predict the number of students enrolling in the university from those who are accepted by the university. R INPUT: accepted <- c(2440,2800,2720,2360,2660,2619) enrolled <-c(611,708,637,584,614,624) fit <- 1m(enrolled accepted) summary(fit) R OUTPUT: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 88.88272 182.36584 0.487 0.6515 accepted 0.20801 0.07002 2.971 0.0411 * --- Signif. codes: O ** 0.001 '' 0.01 (*' 0.05 '.’ 0.1 ' ' 1 Residual standard error: 26.35 on 4 degrees of freedom Multiple R-squared: F-statistic: 8.824 on 1 and 4 DF, 0.6881, Adjusted R-squared: 0.6101 p-value: 0.04112 (a) Write an equation for the regression line. (b) What is the slope of the least-squares regression line? Interpret this in the context of the application. (c) Interpret the intercept of the line in context. Does the interpretation make sense?

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Please refer to the picture attached.
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