Write down the regression line from this output. Arethe coefficients significant?> summary(lm.fit)Call:Im(formula = medvResiduals:1stat)302.034 24.500Min10 MedianМаx-15.168 -3.990 -1.318Coefficients:(Intercept) 34.553841statEstimate Std. Error t value Pr(>Itl)<2e-16 ***<2e-16 ***0.562630.03873 -24.5361.41-0.95005Signif. codes: 0 **** 0.001 *** 0.01 * 0.05 ' 0.1 ' 1Residual standard error: 6.216 on 504 degrees of freedomMultiple R-squared: 0.5441, Adjusted R-squared: 0.5432F-statistic: 601.6 on 1 and 504 DF, p-value: < 2.2e-16

Given information: The linear regression output is given.

Answered: Write down the regression line from…

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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Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squares regression line. The equation for this line is y=82.25 -0.48x. ^ Birthrate, x (number of births per 1000 people) 35.5 44.9 29.7 19.9 13.7 27.0 51.9 15.0 50.9 49.7 39.6 24.4 Send data to calculator Send data to Excel Female life expectancy, y (in years) 67.9 57.9 61.7 71.4 72.5 73.5 55.4 76.5 58.2 60.6 64.4 74.4 Based on the sample data and the regression line, complete the following. Female life expectancy (in years) 85 80+ 75+ 70- 65+ 60- 55+ 50 x X x ++ 10 15 20 25 (b) According to the regression equation, for an increase of one (birth per 1000 people) in birthrate, there is a corresponding decrease…
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A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. The regression coefficient of x2 suggests this: __________. If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes
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Suppose that in R we have loaded the cps dataset, which contains the variables wage, educ, and exper. We want to run a regression of wage on edục and exper and save the output in a variable called regout. What is the correct command for this operation? O a. regout = 1m(wage = educ + exper, data = cps) O b. 1m(wage educ + exper, data = cps) 1m(wage educ + exper, data = cps) O e. regout <- lm(wage - educ + exper, data = cps) Clear my choice
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Find the multiple regression equation with weight as the response variable and the dummy variable of sex and the variable of age as the explanatory variables.
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Write down the regression line from this output. Are the coefficients significant?