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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You are studying how a penguin's flipper length (in mm) explains its body mass (in grams) using linear regression. You choose a non-directional alternative to be safe. You calculate an R2=0.7621. What is the interpretation of this value? O76.21% of the variability in body mass is explained by the flipper length. O 76.21% of the variability in flipper length is explained by the body mass. 0 Flipper length and body mass have a positive relationship becase R² is positive O76.21% of the body mass values will be inside the confidence interval
A sports blogger is looking to determine an equation that relates a professional football quarterback's salary with his passer rating, touchdowns thrown, the average number of wins per season, and the number of years the quarterback has been in the league. What is the correct format for a multiple regression equation? Select the correct answer below: yˆ=b0+b1x1+b2x2+b3x3 yˆ=b1x1+b2x2+b3x3 yˆ=b0+b1x1+b2x2+b3x3+b4x4 yˆ=b0+b1x1+b2x2
Interpret the regression coefficient of X3 in relation to the Y ( X3 unit is mile) Y = 58.286 + 13.7604 * X3
Analysis of the relationship between the fuel economy (combinedMPG) and displacement (in liters) for 1262 models of cars produces the regression model combinedMPH = 33.472 - 3.317Displacement. Explain what the slope means.
Explain how the coefficient of multiple determination, R2, is used as a descriptive measure in multiple linear regression.
Explain what the Coefficient of Determination (R2: R squared) is, and explain how the measure is used for Linear Regression models.
Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported as y=-59.97x+67.54 and the r=-0.31 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) .
Explain what a dummy variable is and how it is used in regression analysis
Write down the properties of R2. If R2=53.7%, interpret your data. Distinguish between the correlation and regression with example.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.96 b=35.944 r2=0.736164 r=-0.858 Use this to predict the number of situps a person who watches 10.5 hours of TV can do (to one decimal place)
If P(Z
Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 30 minutes. How does the result compare to the actual tip amount of $4.70? Use a significance level of 0.05. Distance 1.80 12.71 1.32 Time 1.65 8.51 1.40 1.02 2.47 Fare Tip 25.00 27.00 8.00 16.30 36.80 7.80 9.80 31.75 12.30 1.50 0.00 0.00 1.96 2.98 2.46 11.00 31.00 18.00 8.00 18.00 7.80 14.30 2.34 4.29 The regression equation is ŷ =+ (x. (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.)

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