The application of logistics regression is suitable for:
Profiling
Classification
Both of them
None of the above
Both of them
Which of the following methods do we use to best fit the data in Logistic Regression?
Least Square Error
Maximum Likelihood
Jaccard distance
Both ‘a’ and ‘b’
Maximum Likelihood
Logistic regression is used to predict _________ valued output?
Continuous
Discrete
Both of them
None of the above
Discrete
Which of the following is true regarding the logistic function for any value “x”? [Logistic(x): is a logistic function of any number “x”] [Logit(x): is a logit function of any number “x”] [Logit_inv(x): is a inverse logit function of any number “x”]
Logistic(x) = Logit(x)
Logistic(x) = Logit_inv(x)
Logit_inv(x) = Logit(x)
None of these
Logistic(x) = Logit_inv(x)
Consider a following model for logistic regression: P (y =1|x, w)= g(w0 + w1x) where g(z) is the logistic function. What would be the range of P in such case?
(-inf, 0)
(0, 1)
(-inf, inf)
(0, inf)
(0, 1)
The logit function (given as l(x)) is the log of odds function. What could be the range of logit function in the domain x=[0,1]?
(-inf, 0)
(0, 1)
(-inf, inf)
(0, inf)
(-inf, inf)
In binary logistic regression:
The dependent variable is continuous.
The dependent variable is divided into two equal subcategories.
The dependent variable consists of two categories.
There is no dependent variable.
The dependent variable consists of two categories.
Large values of the log-likelihood statistic indicate:
That there are a greater number of explained vs. unexplained observations.
That the statistical model fits the data well.
That as the predictor variable increases, the likelihood of the outcome occurring decreases.
That the statistical model is a poor fit of the data.
That the statistical model is a poor fit of the data.
Which one of the following statements is true in the case of logistic regression? 
The -2log(likelihood) is a measure of lack of  fit for the logistic model, the smaller the value the poorer the fit between the observed data and the model.
The -2log(likelihood) is a measure of lack of  fit of a single b coefficient, the smaller the value the poorer the fit between the observed data and the model.
The -2log(likelihood) is a measure of goodness of fit of the logistic model, the smaller the value the closer the fit between the observed data and the model.
The -2log(likelihood) is a measure of goodness of fit of a single b coefficient, the smaller the value the closer the fit between the observed data and the model.
The -2log(likelihood) is a measure of goodness of fit of the logistic model, the smaller the value the closer the fit between the observed data and the model.
The logit is:
an instruction to record the data
the cube root of the sample size
the natural logarithm of the odds ratio
a logarithm of a digit
the natural logarithm of the odds ratio