Write the regression model (include the distribution of the random error term)Based on the output shown above, give the estimated regression line Clipboard/DataFind/ReplaceStandardizeInsertDictionaryD data survivalOSheet1Regression Summary for Dependent Variable: survival (Sheet1 in data_survival)R= .88408257 R'= 78160200 Adjusted R.76377359F(4,49)-43.840 p<.00000 Std.Error of estimate: 70.620b*Multiple RStd.Err.ц49)Std.Err.of bbp-valueRegressN=54of bSumnInterceptRegre iver0.0000000.0354370.0000000.0000000.000190-598.36529.81178.8073613.78119-7.592752.163136.240280.2196010.101520pindexenzymebloodclot0.4719940.5169490.3826650.0756370.0857740.0947834.0573.38034.6850.650200.560898.591196.026904.03726 Clipboard/LAstainsertD data survivalO Sheet1B Multiple R EffectS Regressi Regress.SumnAnalysis of Variance; DV: survival (Sheet1 in data_survival)Sums ofdfMeanSquares218639.8Fp-valueSquares874559443.840260.000000ResidualTotal244372494987.21118931RegreSumnAnaly

There are 3 independent variables. It is multiple regression model. We have to write the regression…

Answered: data survival O Sheet1 Regression…

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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Q5/ Use Linear Regression to fit the following data: X 1 2 4 5 6 Y 4 10 10 9 3
Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…
Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative?
A researcher is interested to measure returns to schooling. He ran the regression below: w = a + b*School where w is the hourly wage, 'School' measures years of schooling and b is the coefficient on schooling. Fill in the missing blanks to make the statement correct. Omitting an important variable а. biases b only if it is not related to the 'School' variable. b. does not affect the estimate of b. It only affects the standard error of the estimated coefficient. C. biases b and affects the standard error of the estimated coefficient. d. biases b only if it is related to the 'School' variable.
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why? Click the icon to view the table of regression equations. Choose the correct answer below. A. The equation CITY=6.86 -0.00131WT -0.258DISP+0.659HWY is best because it has a low P-value and the highest value of R². B. The equation CITY=6.73 -0.00157WT +0.668HWY is best because it has a low P-value and the highest adjusted value of R². C. The equation CITY= -3.15+0.823HWY is best because it has a low P-value and its R² and adjusted R² values are comparable to the R² and adjusted R² values of equations with more predictor variables. O D. The equation CITY=6.86 -0.00131WT-0.258DISP + 0.659HWY is best because it…
Q5/ Use Linear Regression to fit the following data: 1 2 4 5 6. Y 4 9 10 10 8
when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?
Explain the process of Computing the Regression Line?

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