When we fit a logistic regression model with a single explanatory term, x, we often assume that logit (pi) Bo + ₁x₂ where, for the ith observation, p; is the probability of a trial being successful and *; is the value of the explanatory variable. Which of the following is a good reason for using the logit link function? We can't use more than one explanatory term unless we use this link function. It allows us to model nonconstant variance in the response variable. It ensures that the probability of success is between 0 and 1. It ensures that the variance of the response is the same for all observations.

Suppose a logistic regression model is fitted for the probability of car ownership for residents of 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 and x2 is gender of the resident of the city (x2=1 for a male and x2=0 for a female resident) a) Interpret el and e82 b) if BO= -1.6, B1=0.4 and B2=3, estimate the probability of a resident in the city owning a car.

Please answer both parts

The equation Y= α+ βx+ε describes a population regression line. Which of the statements regarding this equation is true.    A. ε is the coefficient of standard deviation  B. α,β are sample statistics that can be calculated by the ordinary least squares method  C. α,β are usually unknown since they are population quantities  D. None of the statements above are true

You did not really answer my question.  You only included dummy variables for MSS.  You need to also include dummy variables for BOROUGH and the make the model  variables for both MSS and BOROUGH. You were asked to help with analysis of birth weights (BW) of 10,000 infants born in NYC during a certain period of time. The aim of the analysis is to see whether the birth weights of the infants are associated with mothers AGE at birth (continuous variable in years) and mothers smoking status (the maternal smoking status MSS contains 4 categories “Non-smoker”, “Past-smoker”, “Passive-smoker”, “Smoker”) and NYC boroughs (the BOROUGH variable contains 5 categories “Manhattan”, “Bronx”, “Brooklyn”, “Queens” and “Staten Island”). Questions: 1.Write down the population model that estimates BW based on variables AGE and BOROUGH(with the dummy variables). Make sure that it is clear what each predictor means. 2.How many parallel lines are computed by model from population model you created above?…

give a situation (real life situation) that best explain the given

please help me

What is the mathematical representation for the multiple logistic regression model in photo?

The relationship between rainfall and the probability of flooding is given by the logistic regression p = 1 1+e-(a+br) Use the following data and regression method with a confidence level of 95% to select all true expressions about the model. r= Rainfall (mm) 200 250 280 300 325 350 380 400 p= Probability of flooding 0.02 0.09 0.12 0.3 0.4 0.7 0.9 0.93 Select one or more: O 1. The correlation of p and r is a strong negative correlation. 2. The estimation of p at r=360 is 0.78 3. The maximum error for the data happens at r=325 4. a = 10.8431 and b = -0.0337 5. R-squared=0.99

Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the date and the least-squares regression line. The equation for this line is y=82.84-0.49x. Birthrate, x (number of births per 1000 people) 24.6 50.1 40,6 20.0 50.7 28.1 143 49.0 31.9 14.8 33.6 46.5 Send data to calculator Send data to Excel Female life expectancy, y (in years) 74.3 54.4 66.2 73.6 58.9 72.7 76.1 61.5 62.4 71.4 68.0 57:5 Based on the sample data and the regression line, complete the following. Female life expectancy (in years) Birthrate (number of births per 1000 people)