Suppose a logistic regression model is fitted for the probability of car ownership for residentsof a certain city in Oman (Y=1 if a resident owns a car, Y=0 if a resident does not own a car).Suppose the explanatory variables used are x1=no. of years a resident spent in schooling andx2 is gender of the resident of the city (x2=1 for a male and x2=0 for a female resident)a) Interpret el and e82b) if BO= -1.6, B1=0.4 and B2=3, estimate the probability of a resident in the city owning a car.

From the given information, The dependent binary variable is 'ownership for residents of a certain…

Answered: Suppose a logistic regression model is…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Two random variables have the regression equations : 3 X+2Y- 26 = 0 6X+ Y- 31 = 0 | Fina the mean value and the coefficient of correlation between X and Y. If the variable of X = 25, find the standard deviation of Y from the data given above.
A study was done to investigate the relationship between amount of protix (a new protein-vitamin•-mineral supplement) org a fortified- vitamin rice, known as FVR, and the gain in weight of children. Ten randomly chosen sections of grade one pupils were fed with FVR containing protix; different amounts X of protix were used for the 10 sections. The increase in the weight of each child was measured after a given period. The average gain Y in weight for each section with a prescribed protix level x is as follows; Section(i) Protix(X) Gain(Y) Section(i) Protix(X) Gain(Y) 1 2 3 4 5 50 60 70 80 90 91.6 96.5 95.5 101.3 104.8 6 7 8 9 10 100 110 120 130 140 105.2 107.9 107.4 109.2 109.8 Compute the prediction equation to predict the gain in the weight of a child.
Suppose you are examining a multi-variable linear regression model that was designed to predict the weight of a person, measured in kg, using 3 predictor variables. One of the variables used in this analysis is "height", with the coefficient of this variable being equal to 3.96, with a standard error of the coefficient equal to 1.168. There are 300 datapoints in the dataset. Using this information, what would be the test statistic (t-ratio) for the test to see if the variable "height" is significant? Only round final answer. Round to two decimal places.
A) A linear regression has a =6 and b=5 what is y predicted as when x=9? B) A linear regression has b=3 and a=4.What is the predicted Y for x=7?
Calculate the estimate of σε What is the estimated resting heart rate for someone who is a smoker (Smoke=1) and exercises 1 hour a day (Exercise=1)?
10) The following results are from a regression where the dependent variable is COST OF COLLEGE and the independent variables are TYPE OF SCHOOL which is a dummy variable = 0 for public schools and = 1 for private schools, FIRST QUARTILE SAT which is the average score of students in the top quartile of SAT’s, THIRD QUARTILE SAT which is the average score of students in the 3rd quartile, and ROOM AND BOARD which is the cost of room and board at the school. The first set of results includes all the independent variables whereas the second set of results excludes the THIRD QUARTILE SAT variable. a) Based on these two sets of data, does there appear that multicollinearity is a problem (specifically, does it appear that THIRD QUARTILE SAT is highly collinear with the other independent variables? Explain. b) Calculate the VIF for THIRD QUARTILE SAT. c) Based on the VIF, do you think that multicollinearity is a problem? Explain.
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. What is the subpopulation mean when x = 7 servings per day?
4
The maintenance manager at a trucking company wants to build a regression model to forecast the time (in years) until the first engine overhaul based on four predictor variables: (1) annual miles driven (in 1,000s of miles), (2) average load weight (in tons), (3) average driving speed (in mph), and (4) oil change interval (in 1,000s of miles). Based on driver logs and onboard computers, data have been obtained for a sample of 25 trucks. A portion of the data is shown in the accompanying table. Time Miles Load Speed Oil 7.7 42.9 22.0 44.0 16.0 0.8 98.3 20.0 47.0 34.0 6.3 61.1 22.0 62.0 15.0 E Click here for the Excel Data File b. Estimate the regression model. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Time = Miles Load Speed oil + + d. What is the predicted time before the first engine overhaul for a particular truck driven 60,000 miles per year with an average load of 25 tons, an average driving speed of 53 mph, and 21,000 miles…
A catalog company builds a logistic regression (LR) model to predict the probability that a customer will buy from the catalog during a particular campaign (mailing). The LR model contains 2 independent variables: X1 spend per year in 1000's of dollars (so $2000 will be coded as X1 2) and X2= does customer possess a loyalty card (X2 1 means customer has loyalty card, X2 = 0 means customer does not have the card). Once the model is fitted, the LR coefficients are provided below: = Constant term (BETAO): - 3.5 (negative 3.5) X1 coeff. (BETA1): 0.6 X2 coeff.(BETA2): 1.5 What is the probability that a customer who spends $6000/year and who does NOT have a loyalty card will respond to the campaign? = O About 53% O About 15% About 33% O About 45%
A paper gives data on x = change in Body Mass Index (BMI, in kilograms/meter2) and y = change in a measure of depression for patients suffering from depression who participated in a pulmonary rehabilitation program. The table below contains a subset of the data given in the paper and are approximate values read from a scatterplot in the paper. BMI Change (kg/m²) Depression Score Change S = The accompanying computer output is from Minitab. Depression score change 15- 10- -0.5 S Fitted Line Plot Depression score change = 6.577 +5.440 BMI change 20- 5.30586 Coefficients T 0.0 0.5 -0.5 R-sq 25.96% - 1 Term Coef Constant 6.577 BMI change 5.440 % 0.5 BMI change 1.0 SE Coef 2.28 2.90 9 0 0.1 0.7 0.8 1 1.5 4 T-Value 2.88 1.87 Interpret this estimate. s is the typical amount by which the ---Select--- line. 4 5 Regression Equation Depression score change = 6.577 +5.440 BMI change P-Value 0.0164 0.0906 S 5.30586 25.96% R-Sq R-Sq (adj) 18.56% 8 (b) Give a point estimate of o. (Round your answer to…
The regression lines between two random variables X and Y is given by 3X + Y = 10 and3X + 4Y = 12 .Find the correlation between X and Y.

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