A safety engineer uses simple logistic regression to study the relationship between occupancy years (x) and evacuation decision (y), under a major hurricane forecast. Each occupant will evacuate (Y = 1) or stay (Y = 0). Sample data provides the estimated logit function: w = 2.83 − 0.24x. Estimate the odds that a homeowner of 10 years will evacuate.

A safety engineer uses simple logistic regression to study the relationship between occupancy years (x) and evacuation decision (y), under a major hurricane forecast. Each occupant will evacuate (Y = 1) or stay (Y = 0). Sample data provides the estimated logit function: w = 2.83 − 0.24x. Estimate the odds that a homeowner of 10 years will evacuate.

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

You are studying how a penguin's flipper length (in mm) explains its body mass (in grams) using linear regression. You choose a non-directional alternative to be safe. You calculate an R2=0.7621. What is the interpretation of this value? O76.21% of the variability in body mass is explained by the flipper length. O 76.21% of the variability in flipper length is explained by the body mass. 0 Flipper length and body mass have a positive relationship becase R² is positive O76.21% of the body mass values will be inside the confidence interval
You are the manager of a firm that sells a leading brand of alkaline batteries. Click on the link below to access data on the demand for your product. Specifically, the file contains data on the natural logarithm of your quantity sold, price, and the average income of consumers in various regions around the world.Use the information provided in the excel spreadsheet to perform a log-linear regression. LN Quantity LN Price LN Income 1.07 1.15 4.4 1.07 1.09 4.39 1.08 0.97 4.4 1.08 1.04 4.39 1.09 0.92 4.39 1.08 0.96 4.39 1.1 0.77 4.39 1.1 0.79 4.39 1.08 1.06 4.39 1.07 1.13 4.39 1.08 0.96 4.4 1.08 1.02 4.39 1.09 0.82 4.39 1.07 1.1 4.39 1.09 0.9 4.39 1.1 0.77 4.39 1.09 0.91 4.4 1.08 1.07 4.4 1.09 0.88 4.39 1.07 1.16 4.39 1.08 1.08 4.4 1.08 1 4.39 1.09 0.88 4.39 1.07 1.13 4.4 1.07 1.09 4.39 1.11 0.64 4.39 1.09 0.9 4.39 1.07 1.19 4.4 1.06 1.27 4.39 1.08 0.94 4.39 1.07 1.1 4.4 1.13 0.28 4.39 1.08 0.97 4.4 1.09 0.86 4.4…
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 92 80 79 O Left Arm 175 169 182 149 148 E Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y =+x. (Round to one decimal place as needed.)
Which of the following variables could be the dependent variable in a Logistic Regression? BMI Survival (yes or no) Grade in School All of the above
Pls help ASAP. Pls show all work.
Studies have shown that people who suffer sudden cardiac arrest have a better chance of survival if a defibrillator shock is administered very soon after cardiac arrest. How is survival rate related to the time between when cardiac arrest occurs and when the defibrillator shock is delivered? The accompanying data give y = survival rate (percent) and x = mean call-to-shock time (minutes) for a cardiac rehabilitation center (in which cardiac arrests occurred while victims were hospitalized and so the call-to-shock time tended to be short) and for four communities of different sizes. Mean call-to-shock time, x 2 6 79 12 Survival rate, y (a) Construct a scatterplot for these data. y 12. 10 O 8 6 20 40 60 91 44 32 6 4 80 100 y 100 80 60 ELL 40 20 2 4 6 8 X 12 10 8 6 4 20 40 60 (b) Find the equation of the least-squares line. (Round your values to four decimal places.) ŷ= Jx 80 100 10 12 (c) Use the least-squares line to predict survival rate for a community with a mean call-to-shock time of…
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 91 76 76 5 Left Arm 175 170 146 147 146 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = +x (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
You are studying the relationship between age and lung function measured as forced expiratory volume in 1 min. In a regression analysis is lung function the best option for Y? O Yes O No
What is the mathematical representation for the multiple logistic regression model in photo?
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 4141 inches. Is the result close to the actual weight of 273273 pounds? Use a significance level of 0.05. Chest size (inches) 4040 5353 3838 4343 4444 5858 Weight (pounds) 227227 360360 153153 206206 234234 414414 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. Question content area bottom Part 1 What is the regression equation? ModifyingAbove y with caretyequals=enter your response hereplus+enter your response herex (Round to one decimal place as needed.) Part 2 What is the best predicted weight of a bear with a chest size of 4141 inches? The best predicted weight for a bear with a chest size of 4141 inches is enter your response here pounds. (Round to one…
Critical Values of the Pearson Correlation Coefficientr a= 0.05 a= 0.01 NOTE: To test H, p-0 against H,: p#0, reject H, jr the absolute value of r is preater than the critical yalue in the table. 0.950 0.990 0.959 0.917 0.875 5 0.878 6 0.811 0.754 7 0.834 0.798 8 0.707 0.666 0.632 0.602 9 10 0.765 11 0.735 12 0.576 0.708 13 0.553 0.684 0.532 0.514 0.497 0.482 14 0.661 15 0.641 16 0.623 17 18 0.606 0.590 0.468 19 0.456 0.444 0.575 20 0.561 25 0.396 0.505 0.463 30 0.361 35 0.335 0.430 40 0.312 0.402 45 0.294 0.279 0.378 0.361 50 60 0.254 0.330 70 80 0.236 0.305 0.286 0.220 90 0.207 0.196 0.269 100 0.256 a= 0.05 a=0.01
The total number of thousands of tons of coal produced per year over a 10-year period for a certain region is provided in the accompanying dataset. Use double exponential smoothing to determine which pairs of values for a and ß minimize MAD for this dataset. α = 0.1, p=0.9; a=0.5, ß= 0.2; x = 1, ß= 0.6 Click the icon to view the coal production data. First find the MAD for each pair of values, a and ß. (Type integers or decimals rounded to two decimal places as needed.) X В MAD 0.9 0.2 0.6 0.1 0.5 1 123 1 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 A Year 1 2 3 4 5 6 7 8 9 10 B Coal Production (thousands of tons) C 434333 434332.9 420420 420419.9 439047 439046.9 477192 477191.9 504182 504181.9 526959 526958.9 546824 546823.9 564878 564877.9 556704 556703.9 570973 570972.9 D