Predictions: Onc. Predict the cost of creating an app involving 6 developers, 650 hours and $23,000 insoftware and testing costs.$Accessibility: InvestigateSearchSAMSUNGI t Predictions: OnA startup team is interested in creating a productivity app. In estimating the cost ofcreating the app, they collected sample data and developed the following regressionequation:where number of developersb2number of hours spent creating the appIs cost of software and testing ($'000)y-total cost of creating app ($'000)a. What is the value of b2 in the equation?y = -8.006-19.271x1 +0.8462 +1.786x3b. Does it make sense to interpret bo in this equation?YesNoAccessibility: InvestigateSearchOW5I

The regression equation is y = -8.006+19.271x1+0.846 x2+1.786x3 a. The value of b2 is b2 =0.846.

Answered: A startup team is interested in…

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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Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05.
I’m taking a statistics and probability class. Please get this correct because I want to learn. I have gotten wrong answers on here before
Using your favorite statistic software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported as y = - 10.71 x + 65.32 and the r = 0.044. What proportion of the variation in y can be explained by the variation in the values of x? r^2 = % % to one decimal
Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported asy=−71.07x+16.92y=-71.07x+16.92and the r=−0.52r=-0.52.What percentage of the variation in y can be explained by the variation in the values of x?r² = % (Report exact answer, and do not enter the % sign)
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy).The results of the regression were: y=ax+b a=-0.904 b=24.378 r2=0.64 r=-0.8 Use this to predict the number of sit ups a person who watches 9.5 hours of TV can do, and please round your answer to a whole number.
The money raised and spent (both in millions of dollars) by all congressional campaigns for 8 recent 2-year periods are shown in the table. The equation of the regression line is y = 0.940x + 34. 128. Find the coefficient of determination and interpret the result. 785.8 784.5 1032.7 963.8 1211.5 Money raised, x Money spent, y 456.6 654.6 733.8 449.4 691.4 734.6 764.4 739.3 1018.6 930.3 1172.9 Find the coefficient of determination and interpret the result. 2 = 0.989 (Round to hree decimal places as needed.)
A regression was run to determine if there is a relationship between hours of study per week (X) and the test scores (y ). The results of the regression were:y=ax+ba=5.931b=20.1r2=0.748225r=0.865Use this to predict the final exam score of a student who studies 3 hours per week, and please round your answer to a whole number.
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
An e-commerce retailer was trying to understand how customer engagement correlates to customer purchases. They built the following regression model: Probability of Purchase = 0.1619 + 0.07 Emails Opened. If someone opens 5 emails how likely are they to purchase? If someone has a 75% chance of purchase how many emails have they opened? If someone opens no emails how likely are they to purchase? What is the slope here and what does it tell us?
A regression was run to determine if there is a relationship between hours of TV watched per day, x, and number of sit-ups, y, a person can do in a minute. The results of the regression were: y=ax+b a=-1.255 b=21.239 Use this to predict the number of sit-ups a person who watches 11.5 hours of TV can do in a minute. Round to the nearest whole number.
For the advertised price and number of miles data, the equation of the regression line is price = 18,773-86.18(thousands of miles) Predict the asking price for a car with 50,000 miles. $ type your answer...
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why? E Click the icon to view the table of regression equations. Choose the correct answer below. O A. The equation CITY = 6.65 - 0.00161WT + 0.675HWY is best because it has a low P-value and the highest adjusted value of R2. O B. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it has a low P-value and the highest value of R?. OC. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it uses all of the available predictor variables. O D. The equation CITY = - 3.14 + 0.823HWY is best because it has a low P-value and its R2 and adjusted R? values are comparable to…

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