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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A popular television show recently released a video previewing the upcoming season on his website as fans of the show discovered the video the number of views of the preview video has grown each day during the last two weeks the number of days in the release of the video and the natural log of the number of video views as shown in the scatterplot. based on the scatterplot and residual plot, what type of model would be appropriate for summarizing the relationship between days and numbers of views? an exponential model is appropriate because the Residual plot does not show a clear pattern A linear model is appropriate because of scatterplot shows a strong positive linear relationship A power model is appropriate because the scatterplot of days a natural log of use is strong and linear an exponential model is appropriate because the relationship between the days in the natural log of views in linear and the residual plot does not show a clear pattern
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…
Ocean currents are important in studies of climate change, as well as ecology studies of dispersal of plankton. Drift bottles are used to study ocean currents in the Pacific near Hawaii, the Solomon Islands, New Guinea, and other islands. Let x represent the number of days to recovery of a drift bottle after release and y represent The distance from point of release to point of recovery in km/100. The following data a representative of one study using drift bottles to study ocean currents. x day 72 79 32 96 203 y km/100 14.8 19.6 5.3 11.8 35.1 Σx=482 Σy=86.6 Σx2=62874 Σy2=2002.54 Σxy=11041.7 r=.93787 use a 1% level of significance to test the claim p>0.(use 2 decimal places) t=critical t= Se=4.4913 a=1.4966 b=.1641 D) find the predicted distance (km/100) when a drift bottle has been floating for 80 days.(use 2 decimal places) find a 90% confidence interval for your prediction of part D. (use 2 decimal places) lower limit=___km/100upper limit=___km/100 he was a 1% level of…
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…
2. You estimated a regression with the following output.       Source |       SS           df       MS      Number of obs   =       332 -------------+----------------------------------   F(1, 330)       =      2.32        Model | 71599.1822         1 71599.1822   Prob > F        =    0.1284     Residual | 10170207.7       330 30818.8111   R-squared       =    0.0070 -------------+----------------------------------   Adj R-squared   =    0.0040        Total | 10241806.9       331 30942.0147   Root MSE        =    175.55 ------------------------------------------------------------------------------            Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] -------------+----------------------------------------------------------------            X |   .7575183   .4969893     1.52   0.128   -.2201484    1.735185        _cons |   28.87757   42.80371     0.67   0.500    -55.32498    113.0801…

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