Answered: You estimated a regression with the…

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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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…
The point (9.1,71) is a point on the scatterplot. Draw the graph of the regression line on the scatterplot on the back of this paper using the y-intercept and the point(I = 3,y = 191). Indicate the residual for z = 9.1 by drawing the vertical line between(9.1, 71) and the corresponding point on the regression line. Will the residual be a positive or negative value?(f) Find the value of the residual for x = 9.1.
The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 1004 952 1237 1116 1177 1249 Temperature (°F) 83.1 76 95.1 87.5 92.1 88.4 What is the regression equation? (^ over y)=_____+_____x (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000chirps per minute? The best-predicted temperature when a bug is chirping at 3000 chirps per minute is ____°F. (Round to one decimal place as needed.) What is wrong with this predicted value? Choose the correct answer below. A. It is…
10.2 V128x 3.
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…
10. You estimated a regression with the following output. Source | SS df MS Number of obs = 333 -------------+---------------------------------- F(1, 331) = 4608.21 Model | 32636494.1 1 32636494.1 Prob > F = 0.0000 Residual | 2344225.8 331 7082.25316 R-squared = 0.9330 -------------+---------------------------------- Adj R-squared = 0.9328 Total | 34980719.9 332 105363.614 Root MSE = 84.156 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 30.79902 .4537022 67.88 0.000 29.90652 31.69153 _cons | 20.85313 42.1964 0.49 0.621 -62.1538 103.8601…
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…
Find an example of regression that lead to an important discovery. Be sure to give proper attribution or credit to the source. Discuss how and why it lead to a discovery in at least 6 sentences.
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…
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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