Our regression on the Titanic data returned the following result:coef std errtP>|tl[0.0250.975]Intercept1.31390.07717.0320.0001.1621.465C(sex) [T.male]-0.48350.032-15.2990.000-0.546-0.421alone [T.True]0.02250.0320.7060.480-0.0400.085pclass-0.20170.023-8.9320.000-0.246-0.157age-0.00560.001-5.0040.000-0.008-0.003fare0.00010.0000.3220.748-0.0010.001From this regression results, which of the following independent variable(s) do nothave a statistically significant relationship with the dependent variable? Please selectall that apply.agepclassfarealone

For the considered regression output, the independent variables are:sex, alone, pclass, age, fareIn…

Answered: Our regression on the Titanic data…

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 TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.773 b=24.3 r2=0.5776 r=-0.76 Use this to predict the number of situps a person who watches 10 hours of TV can do (to one decimal place)
The following are the summations of the Age(X) and Blood Pressure (Y). n = 10 ∑X = 345 ∑Y = 819 ∑XY = 47,634 ∑X2 = 20,399 a. What is the intercept of the regression formula? b. What is the slope of the regression formula? c. What is the value of Y when X = 60
Interpret the intercept and the coefficients of D1 and D2 in the regression above.
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=-0.971 b=21.794 r²-0.872356 r=-0.934 Use this to predict the number of situps a person who watches 5 hours of TV can do (to one decimal place)
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=-0.889 b=31.442 r²=0.3721 r=-0.61 Use this to predict the number of situps a person who watches 4 hours of TV can do (to one decimal place)
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…
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 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 1118 796 1161 918 1103 1219 Temperature (F) 82 72.2 92.2 73 87.5 94.4
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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