You estimated the following regression. What value would you predict for Y, if X = 71? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 493 -------------+---------------------------------- F(1, 491) = 2097.64 Model | 16182052 1 16182052 Prob > F = 0.0000 Residual | 3787776.41 491 7714.41225 R-squared = 0.8103 -------------+---------------------------------- Adj R-squared = 0.8099 Total | 19969828.4 492 40589.0821 Root MSE = 87.832 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 10.79424 .2356821 45.80 0.000 10.33117 11.25731 _cons | -8.867688 16.20543 -0.55 0.584 -40.70824 22.97286 ------------------------------------------------------------------------------

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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A regression was run to determine if there is a relationship between hours of study per week (z) and the final exam scores (y). The results of the regression were: y=ax+b a=6.578 b-20.35 r-0.3721 r=0.61 Use this to predict the final exam score of a student who studies 7 hours per week, and please round your answer to a whole number.
You estimated the following regression. What value would you predict for Y, if X = 47? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 412 -------------+---------------------------------- F(1, 410) > 99999.00 Model | 1.0479e+09 1 1.0479e+09 Prob > F = 0.0000 Residual | 1194171.65 410 2912.61377 R-squared = 0.9989 -------------+---------------------------------- Adj R-squared = 0.9989 Total | 1.0491e+09 411 2552469.57 Root MSE = 53.969 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 38.33473 .0639116 599.81 0.000 38.20909 38.46036 _cons | 30.69754 5.301749 5.79 0.000…
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…
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-a+bx a=37,525 b=-1.215 =0,790321 T==0,889 Use this to predict the number of situps a person who watches 6.5 hours of TV can do (to one decimal place) 19.7 Question Help: o Message instructor Submit Question MacBook Pro esc 288 F4 F3 F7 #3 & 2 6 7 Q T Y A S G C V B * LO
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…
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=-1.328 b=37.715 r?=0.788544 r=-0.888 Use this to predict the number of situps a person who watches 0.5 hours of TV can do (to one decimal place)
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…
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 = 26.699 -0.639*x R² = 0.5 r = -0.704 Predict the number of situps a person who watches 6 hours of TV can do (round your answer to three decimal places).
You estimated the following regression. What value would you predict for Y, if X = 43? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 271 -------------+---------------------------------- F(1, 269) = 39445.93 Model | 784422094 1 784422094 Prob > F = 0.0000 Residual | 5349336.53 269 19886.0094 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 789771430 270 2925079.37 Root MSE = 141.02 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 29.25192 .1472832 198.61 0.000 28.96195 29.5419 _cons | 50.45089 13.14528 3.84 0.000…
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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