ou estimated the following regression. Which of the following is the estimated regression line? Source | SS df MS Number of obs = 379 -------------+---------------------------------- F(1, 377) = 24045.16 Model | 401185353 1 401185353 Prob > F = 0.0000 Residual | 6290117.58 377 16684.662 R-squared = 0.9846 -------------+---------------------------------- Adj R-squared = 0.9845 Total | 407475471 378 1077977.44 Root MSE = 129.17 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 10.08326 .065026 155.07 0.000 9.955396 10.21111 _cons | 87.07298 7.855763 11.08 0.000 71.62637 102.5196 ------------------------------------------------------------------------------ Group of answer choices Y = 10.08 + 87.07X Y = 87.07 + 10.08X Y = 7.86 + 0.07X Y = 0.07 + 7.86X

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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1. You estimated a regression with the following output.       Source |       SS           df       MS      Number of obs   =       210 -------------+---------------------------------- F(1, 208)       =    940.28        Model | 5529353.01         1 5529353.01   Prob > F        =    0.0000     Residual |   1223155.7       208 5880.55624   R-squared       =    0.8189 -------------+----------------------------------   Adj R-squared   =    0.8180        Total | 6752508.71       209 32308.6541   Root MSE        =    76.685 ------------------------------------------------------------------------------            Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] -------------+----------------------------------------------------------------            X |   13.66711   .4457062    30.66   0.000     12.78843    14.54579        _cons |   103.7139   41.86814     2.48   0.014     21.17362    186.2542…
4. You estimated the following regression. Which of the following is the estimated regression line?       Source |       SS           df       MS      Number of obs   =       309-------------+----------------------------------   F(1, 307)       > 99999.00       Model | 3.4298e+09         1 3.4298e+09   Prob > F        =    0.0000    Residual | 5008792.71       307 16315.2857   R-squared       =    0.9985-------------+----------------------------------   Adj R-squared   =    0.9985       Total | 3.4348e+09       308    11152012   Root MSE        =    127.73------------------------------------------------------------------------------           Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]-------------+----------------------------------------------------------------           X |   39.83786   .0868877   458.50   0.000     39.66689    40.00883       _cons |    55.9075   9.036526     6.19   0.000     38.12613…
You estimated a regression with the following output.       Source |       SS           df       MS      Number of obs   =       248 -------------+----------------------------------   F(1, 246)       > 99999.00        Model | 1.2864e+09         1 1.2864e+09   Prob > F        =    0.0000     Residual | 980713.166       246 3986.63889   R-squared       =    0.9992 -------------+----------------------------------   Adj R-squared   =    0.9992        Total | 1.2874e+09       247 5212005.66   Root MSE        =     63.14 ------------------------------------------------------------------------------            Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] -------------+----------------------------------------------------------------            X |   38.34444   .0675026   568.04   0.000     38.21148     38.4774        _cons |   49.99803   5.986441     8.35   0.000     38.20681    61.78925…
Predict the average lifespan of an animal in years when the incubation/gestation time is 12 months. Animal: Nile crocodile, Aldabra giant tortoise, Lake sturgeon,Galápagos tortoise, Bowhead whale Average Lifespan (in Years):100 152 152 177 211 Incubation/ Gestation (in Months)3 8 0.25 4.7 regression equation and regression line if needed
You estimated a regression with the following output. Source | SS df MS Number of obs = 325 -------------+---------------------------------- F(1, 323) = 42850.36 Model | 285905003 1 285905003 Prob > F = 0.0000 Residual | 2155111.65 323 6672.17228 R-squared = 0.9925 -------------+---------------------------------- Adj R-squared = 0.9925 Total | 288060115 324 889074.429 Root MSE = 81.683 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 11.83842 .0571895 207.00 0.000 11.72591 11.95093 _cons | 52.14457 5.949458 8.76 0.000 40.43999 63.84915…
You estimated a regression with the following output. Source | SS df MS Number of obs = 246 -------------+---------------------------------- F(1, 244) = 16642.70 Model | 187647307 1 187647307 Prob > F = 0.0000 Residual | 2751112.55 244 11275.0514 R-squared = 0.9856 -------------+---------------------------------- Adj R-squared = 0.9855 Total | 190398419 245 777136.405 Root MSE = 106.18 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 13.79687 .106947 129.01 0.000 13.58621 14.00753 _cons | 17.60822 9.208341 1.91 0.057 -.5297613 35.7462…
You estimated a regression with the following output. Source | SS df MS Number of obs = 494 -------------+---------------------------------- F(1, 492) = 38566.69 Model | 803403712 1 803403712 Prob > F = 0.0000 Residual | 10249120.6 492 20831.546 R-squared = 0.9874 -------------+---------------------------------- Adj R-squared = 0.9874 Total | 813652832 493 1650411.42 Root MSE = 144.33 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 23.00296 .1171325 196.38 0.000 22.77281 23.2331 _cons | 34.71944 13.12788 2.64 0.008 8.925808 60.51307…
You estimated a regression with the following output. Source | SS df MS Number of obs = 423 -------------+---------------------------------- F(1, 421) = 267.80 Model | 8758968.84 1 8758968.84 Prob > F = 0.0000 Residual | 13769523.8 421 32706.7074 R-squared = 0.3888 -------------+---------------------------------- Adj R-squared = 0.3873 Total | 22528492.7 422 53385.0537 Root MSE = 180.85 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.150402 .3758334 16.36 0.000 5.411658 6.889145 _cons | -8.022201 24.02003 -0.33 0.739 -55.23632 39.19192…
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
The slope, b represents * O predicted value of Y when X = 0. O the estimated average change in Y per unit change in X. O variation around the line of regression. O the predicted value of Y.
You estimated a regression with the following output. Source | SS df MS Number of obs = 208 -------------+---------------------------------- F(1, 206) > 99999.00 Model | 2.3001e+09 1 2.3001e+09 Prob > F = 0.0000 Residual | 4378568.43 206 21255.1865 R-squared = 0.9981 -------------+---------------------------------- Adj R-squared = 0.9981 Total | 2.3045e+09 207 11132679.3 Root MSE = 145.79 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 36.19925 .1100424 328.96 0.000 35.9823 36.41621 _cons | 12.65388 12.36197 1.02 0.307 -11.71832 37.02608…
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