We use the form ŷ = a + bx for the least-squares line. In some computer printouts, the least-squares equation is not given directly. Instead, the value of the constant a is given, and the coefficient b of the explanatory or predictorvariable is displayed. Sometimes a is referred to as the constant, and sometimes as the intercept. Data from a report showed the following relationship between elevation (in thousands of feet) and average number of frost-free days peryear in a state.A Minitab printout provides the following information.PredictorCoefSE CoefT.Constant317.9728.3111.240.002Elevation-28.5723.511-8.790.003S = 11.8603R-Sq94.2%%3DNotice that "Elevation" is listed under "Predictor." This means that elevation is the explanatory variable x. Its coefficient is the slope b. "Constant" refers to a in the equation ŷ = a + bx.(a) Use the printout to write the least-squares equation.%3D(b) For each 1000-foot increase in elevation, how many fewer frost-free days are predicted? (Round your answer to three decimal places.)(c) The printout gives the value of the coefficient of determination 2. What is the value of r? Be sure to give the correct sign for r based on the sign of b. (Round your answer to four decimal places.)(d) What percentage of the variation in y can be explained by the corresponding variation in x and the least-squares line?What percentage is unexplained?

Answered: Image /qna-images/answer/dbcc77da-d04e-4ddd-b01f-f6556c060c9a.jpg

Answered: Notice that "Elevation" is listed under…

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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The number of banks in a country has been dropping steadily since 1984, and the trend in recent years has been roughly linear. The annual data for the years 1999 through 2008 can be summarized as follows, where x represents the years since 1990 and y the number of banks, in thousands. n=10 Ex-235 ²-5605 Ey-77.564 Ey²=603.80424 Exy-1810.155 a. Find an equation for the least squares line. b. Use your result from part a to predict the number of banks in the year 2020. c. If this trend continues linearly, in what year will the number of banks drop below 6200? d. Find and interpret the correlation coefficient.
Match the coefficient of determination to the scatter diagram. The scales on the x-axis and y-axis are the same for each scatter diagram (a) R2 0.90, (b) R2 0.58, (c) R2 =1 (a) Scatter diagram Explanatory (b) Scatter diagram (c) Scatter diagram Explanatory III Explanatory
How does R treat those observations with missing values? In other words, what role do the observations with missing values play in the regression? No command is needed for this question. You must need to provide an answer or take a guess.
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. | VIF ₂2 = VIF₁ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? OA. No. The VIF for each independent variable is less than 5. B. Yes. The VIF for each independent variable is greater than 5. OC. No. The VIF for each independent variable is greater than 5. OD. Yes. The VIF for each independent variable is less than 5. wwwwwww Table of Data Standby Total Staff Hours Present 245 330 274 358 195 197 117 153 115 275 200 236 336 339 321 303 286 329 352 323 Remote Hours 417 655 524 385 349 350 388 153 278 479 Print Done
Gas mileage actually varies slightly with the driving speed of a car (as well as with highway vs. city driving). Suppose your car averages 29 miles per gallon on the highway if your average speed is 48 miles per hour, and it averages 26 miles per gallon on the highway if your average speed is 65 miles per hour. Answer parts (a) and (b) below. a. What is the driving time for a 2300-mile trip if you drive at an average speed of 48 miles per hour? What is the driving time at 65 miles per hour? The driving time at 48 miles per hour is hours. (Type an integer or decimal rounded to two decimal places as needed.) The driving time is at 65 miles per hour is (Round to two decimal places as needed.) b. Assume a gasoline price of $4.43 per gallon. What is the gasoline cost for a 2300-mile trip if you drive at an average speed of 48 miles per hour? What is the gasoline cost at 65 miles per hour? The gasoline cost at 48 miles per hour is $ (Round to two decimal places as needed.) hours. The…
Using the weights (Ib) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58 9-0.00749x, where x represents weight Complete parts (a) through (d). Click the icon to view the car data. 東 b. What are the specific values of the slope and y-intercept of the regression line? O A. The slope is 58.9 and the y-intercept is 0.007499. B. The slope is -0.00749 and the y-intercept is 58.9. O C. The slope is 58.9 and the y-intercept is -0.00749. O D. The slope is 0.00749 and the y-intercept is 58.9. c. What is the predictor variable? O A. The predictor variable is highway fuel consumption, which is represented by x. B. The predictor variable is weight, which is represented by x. O C. The predictor variable is weight, which is represented by y O D. The predictor variable is highway fuel consumption, which is represented by y. d. Assuming that there is a significant linear correlation between weight and highway fuel…
nd fill in the coefficients for the regression equation: Y= _+ X1+ X2
Jane collects data from 40 elementary school students and finds that the relationship between age (x) and score on a certain test (y) is given by y=3.2x+45. What can she extrapolate about the score a 16-year old will get on the test?
The following equation describes the relationship between output and labor input at a sample of work stations in a manufacturing plant ŷ = 2.35+2.20X. Suppose, for a selected workstation, the labor input is 5, the predicted output is?
Several surveys in the United States and Europe have asked people to rate their happiness on a scale of 3 = "very happy," 2 = "fairly happy," and 1 = "not too happy," and then tried to correlate the answer with the person's income. For those in one income group (making $25,000 to $55,000) it was found that their "happiness" was approximately given by y = 0.065x - 0.613, where x is in thousands of dollars. Find the reported "happiness" of a person with the following incomes (rounding your answers to one decimal place). (a) $30,000 (b) $50,000 (c) $55,000

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