A survey was conducted and asked: “Do you consider your health to be poor?" This was coded as 1 for "yes" and 0 for "no."The effect of age (in years) and sex (1 = male, 0 = female) on the outcome was examined using logistic regression. Thefitted logistic model is:logit (p) == -0.544 + ß* sex + 0.0331 * ageold?(a) What are the odds of responding "yes" for a female subject who is 60 years|(b) What is the estimated odds ratio corresponding to the agevariable? Interpret this estimate.(c) Suppose that, based on the data from the survey, the odds of responding "yes" for males are 3.79% greater than theodds for females, adjusting for age. What is the value of Â?

Given that: The fitted logistic model is given as, logitp^=-0.544+β^×sex+0.0331×age

Answered: A survey was conducted and asked: "Do…

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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Your company just became international by offering Its products in both the United States and Canada. Experts in your analytics department belleve that tastes for your product differ In those two countries, and have carefully collected data on prices and quantity demanded in both countries. They then present you with the results of two regressions, one for each country, as follows: Log Price regressed on Log Quantity (United States): Standard Error 10.81051303 Coefficients t stat P-value Intercept Log Quantity Lower 95% 31.48708283 -7.685279168 Upper 95% 74.0239705 52.75573994 4.88040363 -4.597932039 1.67605E-06 6.15253E-06 -5.382266173 1.17e5841es -3.079253177 Log Price regressed on Log Quantity (Canada): Standard Error 10.64507785 Coefficients Intercept Log Quantity 22.8707593 -2.09578827s t stat 2.148482109 -1.818112644 0.069981782 P-value 0.0324256e3 Lower 95% 1.9275se2 upper 95% 43.8139384 0.17209336 1.152727409 -4.363669916 Assume you have adequate statistical significance for…
An investigator modeled the log-odds of getting stomach cancer as a function of number of servings of vegetables per week and the number of servings of red mean per week using logistic regression. The odds that a person gets stomach cancer if they have 4 servings of red meat a week are 1.2 times the odds of cancer for someone who has 3 servings of red meat a week. Suppose the fitted regression model is given as log-odds = -6 + 0.182 M – 0.105 V, where M = #servings of red meat per week and V = #servings of vegetables per week. What is the odds ratio representing the change in odds for an increase in one serving of vegetables? Give the answer to three decimal places.
1 Acup of soup is left on a countertop to cool. The cable below gives the temperatures, in degrees Fahrenheit, of the soup recorded over a 10-minute period. Time in Minutes () Temperature in F() 180.2 165.8 146.3 135.4 127.7 110.5 2. 8. 10 Write an exponential regression equation for the data, rounding all values to the nearest thousandth.
Table 4.8 shows results of an eight-center clinical trial to compare a drug to placebo for curing an infection. At each center, subjects were randomly assigned to treatments. Analyze these data, available in the Infection data file at the text website. Using logistic regression, describe and make inference about the treatment effect.
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A researcher is attempting to explore the relationship between study duration per day (0 - 8) and the passing of the science course (Pass=1, Fail=0). For this purpose, a sample of 36 students from Alabama University are chosen. Summary of the logistic regression model has been included. Please help me understand and choose from the following below, which is the correct representation of the related logistic regression model in the summary photo attached. - PASS = 0.57 + 3.69 X SDURATION - PASS= 3.69 +0.57 X SDURATION - PASS/FAIL=3.69 + 0.57 x SDURATION - P(PASS/F(FAIL)=3.69+ 0.57 X SDURATION - PASS/FAIL= 0.57 + 3.69 x SDURATION - ln{P(PASS/F(FAIL)}=0.57 + 3.69 X SDURATION - ln{P(PASS/F(FAIL)}= 3.69+ 0.57 X SDURATION
A regression of average monthly expenditure (AME, measured in euros) on average monthly income (AMI, measured in euros) using a random sample of college-educated full-time workers earnings €100 to €1.5 million yields the following: AME = 710.7 + 8.8 × AMI, R2 = 0.030, SER = 540.30 d. What does the regression predict will be the expenditure of a person with an income of €100? With an income of €200? e. Will the regression give reliable predictions for a person with an income of €2 million? Why or why not? ( f. Given what you know about the distribution of earnings, do you think it is plausible that the distribution of errors in the regression is normal? (Hint: Do you think that the distribution is symmetric or skewed? What is the smallest value of earnings, and is it consistent with a normal distribution?).
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To determine characteristics associated with Y = whether a cancer patient achieved remission (1 = yes), a study used logistic regression. The most important explanatory variable was a labeling index (LI) that measures proliferative activity of cells after a patient receives an injection of tritiated thymidine. It represents the percentage of cells that are “labeled.” Table 4.8 shows the grouped data. Software reports Table 4.9 for a logistic regression model using LI to predict π = P (Y = 1). A. Show how software obtained π ̂ = 0.068 when LI = 8. B. Show that π ̂ = 0.50 when LI = 26.0 D. The lower quartile and upper quartile for LI are 14 and 28. Show that π ̂ increases by 0.42, from 0.15 to 0.57, between those values. E. When LI increases by 1, show the estimated odds of remission multiply by 1.16
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Table 4.18 shows estimated effects for a fitted logistic regression model with squamous cell esophageal cancer (1 = yes, 0 = no) as the response variable Y. Smoking status (S) equals 1 for at least one pack per day and 0 other- wise, alcohol consumption (A) equals the average number of alcoholic drinks consumed per day, and race (R) equals 1 for blacks and 0 for whites. A. To describe the race-by-smoking interaction, construct the prediction equation when R = 1 and again when R = 0. Find the fitted YS conditional odds ratio for each case. Similarly, construct the prediction equation when S = 1 and again when S = 0. Find the fitted YR conditional odds ratio for each case. Note that, for each association, the coefficient of the cross-product term is the difference between the log odds ratios at the two fixed levels for the other variable. B. In Table 4.18, explain what the coefficients of R and S represent, for the coding as given above. What hypotheses do the P -values refer to for…

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