Problem 5Suppose that we are working with some doctors on heart attack patients. The dependentvariable is whether the patient has had a second heart attack within 1 year (yes=1). We havetwo independent variables, one is age of the patient and the other is a score on anxiety scale (ahigher score means more anxious). After applying logistic regression model, we have thefollowing output:Deviance Residuals:10 MedianMin30Мах-1.0640.0000.0000.0001.446Coefficients:Estimate Std. Error z value Pr (>|z|)|(Intercept)AgeAnxiety-471.441 223186.509 -0.0020.9986.3943057.3490.0020.9981.347611.4700.0020.998(Dispersion parameter for binomial family taken to be 1)Null deviance: 27.7259 on 19Residual deviance: 3.7087 on 17 degrees of freedomAIC: 9.7087degrees of freedomNumber of Fisher Scoring iterations: 23a. Determine the estimated logistic regression equation.b. Calculate the odds ratio and interpret.

Answered: Image /qna-images/answer/ce668c82-4045-4039-8fe7-c4afba7fe272.jpg

Suppose that we are working with some doctors on heart attack patients. The dependent variable is whether the patient has had a second heart attack within 1 year (yes=1). We have two independent variables, one is age of the patient and the other is a score on anxiety scale (a higher score means more anxious). After applying logistic regression model, we have the following output: Deviance Residuals: 10 Median Min 30 Маx |-1.064 0.000 0.000 0.000 1.446 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -471.441 223186.509 -0.002 0.998 Age 6.394 3057.349 0.002 0.998 Anxiety 1.347 611.470 0.002 0.998 | (Dispersion parameter for binomial family taken to be 1) Null deviance: 27.7259 on 19 degrees of freedom Residual deviance: 3.7087 on 17 degrees of freedom AIC: 9.7087 Number of Fisher Scoring iterations: 23 a. Determine the estimated logistic regression equation. b. Calculate the odds ratio and interpret.

Suppose that we are working with some doctors on heart attack patients. The dependent variable is whether the patient has had a second heart attack within 1 year (yes=1). We have two independent variables, one is age of the patient and the other is a score on anxiety scale (a higher score means more anxious). After applying logistic regression model, we have the following output: Deviance Residuals: 10 Median Min 30 Маx |-1.064 0.000 0.000 0.000 1.446 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -471.441 223186.509 -0.002 0.998 Age 6.394 3057.349 0.002 0.998 Anxiety 1.347 611.470 0.002 0.998 | (Dispersion parameter for binomial family taken to be 1) Null deviance: 27.7259 on 19 degrees of freedom Residual deviance: 3.7087 on 17 degrees of freedom AIC: 9.7087 Number of Fisher Scoring iterations: 23 a. Determine the estimated logistic regression equation. b. Calculate the odds ratio and interpret.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

If the estimated intercept of the regression equation is negative, we can say the estimated correlation coefficient between the two variables is also negative. O True O False
A recent study showed that the hours a person exercised in a week affected the individual'sresting heart rate. It was computed that r = -.68 and the least squares regression line was?̂ = 83-1.4x, where x is the hours exercised and y is the resting heart rate. d. What percentage of variability in resting heart rate can be explained by variability inhours exercised?
The adjusted R-squared accounts for the amount of variance explained while also adjusting for the number of independent variables in the linear regression equation. True or False
Tom has been gathering data concerning the cost of a spa treatment, y', during the before Valentine's Day. The only independent variable that he has considered is the number of minutes, "x," in the treatment. Suppose Tom collects data on the relationship between the number of minutes in a treatment and the resulting cost of we the treatment. Tom finds that the correlation between cost and number of minutes is strong and positive. Therefore, he has performed a linear regression analysis on his data. His results are that the constant "a" is 35, and the coefficient "b1" for the independent variable is 1.3. Which of the following is the correct linear regression equation that would allow Tom to predict the cost of a spa treatment given the number of minutes? Oy = 78x + 35 Oy' = 1.3x + 35 %3D Oy = 78x - 1.3 %3D OY = -1.3x - 35
what is the regression equation for all predictors? I
If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x. True or false?
What is C,D and E? And how do i calculate it??
** Based on the regression results, answer the following questions ** A sample of data is collected (from 1999 and 2000) concerning the compensation of the executives (compensation is measured in 1000’s of $’s) of a number of public companies along with other firm-specific data. The dependent variable is total compensation, CEOANN is a dummy variable =1 for an individual who is a CEO and =0 for individuals who are not CEO’s, EMPL is total employees, MKTVAL is the natural logarithm of the market value of the firm, EPSIN is earnings per share, YEAR is a dummy variable = 1 for the year 2000 and =0 for year 1999, and ASSETS is the natural logarithm of the total assets of the company. Based on the regression results, answer the following questions b) What is the estimated regression equation? c) What percentage of the variation in income in explained by the regressors? d) What is the standard error of the error term in the regression equation?
Two lines of regression are 2x - y - 20 = 0, 2y - x + 4 = 0. The correlation coefficient is
If you have a b of 0.56 in a regression equation, what does this mean? For every one-unit increase in x, you get an increase of 0.56 in y r = .31 On average, the variability of real scores around the regression line is 0.56 For every 1 standard deviation increase in x, you get an increase of 0.56 standard deviations in y
An investigation into the relationship between an adolescent mother's age x in years and the birth weight y of her baby in grams yielded the regression equation y= - 1163.45 + 245.15x as well as r = .88369, r2= .78091, SSE = 337212.45, and s= 205.30844 1) What is the predicted birth weight for a baby brn to a 17 year old woman? 2) What is the propotion of the variability in the weights of babies born to adolescent mothers that is accounted for by the mother's age? 3) For every additional year in the mother's age that mean birth weight of the baby? (a) increases by about 245g (b) decreases by about 245g (c) increases by about 1163g (d) increases by about 1163g (e) changes by an amount that cannot be determined from the information given.
I want to solve this. Help me plz.