Answered: The regression line for (-3,4),…

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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Beachcomer Ltd is a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model. SALES = a+BAGE + yMIL + SENG Where SALES = amount that a vehicle is sold for (000's), AGE = age of vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained: The regression equation is ***** Coef StDev t-ratio p-value Predictor Constant 1.7586 0.2525 6.9648 0.0000 AGE 0.2124 0.3175 * 0.5042 MIL -0.7527 0.3586 -2.0991 ** ENG 4.8124 0.6196 7.7664 0.0000…
If the equation for the regression line is y = 6x + 4, then a value of x = –3 will result in a predicted value for y of -14 6 -6 4
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…
-20= z/10 -22
Find the least regression line of (0, -3), (1, –1), (2, 1) and (3, 3)
4. In multiple regression, why do we prefer our IVs to be uncorrelated with each other (i.e., we want to be 0)?
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 = 82.17 -0.47x. Birthrate, x (number of births per 1000 people) 14.3 27.4 51.1 46.8 24.9 29.9 18.2 41.6 49.4 14.1 33.9 49.3 Send data to calculator Female life expectancy, y (in years) 75.6 70.5 58.2 59.0 73.3 62.7 73.6 65.2 62.4 74.3 67.0 53.9 Send data to Excel Female life expectancy (In years) Based on the sample data and the regression line, answer the following. 85+ 80+ 75+ 70+ 65 60 55+ 50 (a) From the regression equation, what is the predicted female life expectancy (in years) when the birthrate is 29.9 births per 1000 people? Round your answer to…
Heights (in centimeters) and weights (in kilograms) of 7 supermodels are given below. Find the regression equation, letting the first variable be the independent (x) variable, and predict the weight of a supermodel who is 171 cm tall. \begin{array}{c|ccccccc} \mbox{Height} & 174 & 166 & 176 & 176 & 178 & 172 & 172 \cr \hline \mbox{Weight} & 55 & 47 & 55 & 56 & 58 & 52 & 53 \cr \end{array} The regression equation is \hat{y} = + x . The best predicted weight of a supermodel who is 171 cm tall is
Consider the points in the plane: (1,2) (2,3) (3,5) (4,4) (5,7) (7,8) (i) Compute the correlation coefficient. (ii) Compute the equation for the regression line.
A researcher collected statistics on the sales amount of a product in 120 different markets and the advertising budgets used in TV, radio and newspaper media channels for each of these markets. The sales amount are expressed in 1000 units, and the budgets are expressed in 1000 $. The researcher wants to create a simple linear regression model by choosing one among the TV, radio and newspaper advertising budgets to explain the amount of sales.a) Which variable should this researcher choose as an independent variable to the simple regression model? Explain your decision by providing its statistical basis.(use formula)b) Construct the simple linear regression model using the argument you chose and write the equation of the model. Comment b0 and b1.(not excel use formula please)c) Test whether there is a statistically significant, linear relationship between the independent variable and the dependent variable by establishing the relevant hypotheses at the level of α = 0.05 significance.…
Beachcomer Ltd is a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model. SALES = a+BAGE + yMIL + SENG Where SALES = amount that a vehicle is sold for (000's), AGE = age of vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained: The regression equation is ***** Coef StDev t-ratio p-value Predictor Constant 1.7586 0.2525 6.9648 0.0000 AGE 0.2124 0.3175 * 0.5042 MIL -0.7527 0.3586 -2.0991 ** ENG 4.8124 0.6196 7.7664 0.0000…
2. In your own words, please describe the difference between the regression equation ŷ = b + b₁x and the regression equation y = P + ₁x (Section 10-3).

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The regression line for (-3,4), (-2,3),(-1,3),(0,7),(1,5),(2,6),(3,1)