Multinomial logistic regression can be used on: a)Categorical predictor variables only. b)Both categorical and continuous predictor...

Multinomial logistic regression can be used on:

a)Categorical predictor variables only.

b)Both categorical and continuous predictor variables.

c)Continuous predictor variables only.

d)Ordinal predictor variables only.

Solutions

Expert Solution

b)Both categorical and continuous predictor variables,

EXPLANATION

Extension of logistic regression to multiclass dependent variable

dependent variable is nominal and has more than 2 categories

Example Grade of cement

Grade 1 grade2 grade3

predictor variables are independent variables(X's)

x can be numeric or catgorical

categorical can be converted to factors.

Multinomial Logistic Regression Data Considerations

Data. The dependent variable should be categorical. Independent variables can be factors or covariates. In general, factors should be categorical variables and covariates should be continuous variables.

orchestra answered 1 year ago

Next > < Previous

Related Solutions

Logistic regression predicts a 1._______, 2._, 3.________from one or more categorical or continuous predictor variables.

Logistic regression predicts a 1._____________, 2._____________, 3.______________from one or more categorical or continuous predictor variables.

Logistic Regression In logistic regression we are interested in determining the outcome of a categorical variable....

Logistic Regression In logistic regression we are interested in determining the outcome of a categorical variable. In most cases, we deal with binomial logistic regression with the binary response variable, for example yes/no, passed/failed, true/false, and others. Recall that logistic regression can be applied to classification problems when we want to determine a class of an event based on the values of its features. In this assignment we will use the heart data located at http://archive.ics.uci.edu/ml/datasets/Statlog+%28Heart%29 Here is the...

how can correlational tests and logistic regression be used to understand the relationships between variables

6) Suppose a multinomial regression model has two continuous explanatory variables ?1 and ?2 ,and they...

6) Suppose a multinomial regression model has two continuous explanatory variables ?1 and ?2 ,and they are represented in the model by their linear and interaction terms. a) For a ? unit increase in ?1, derive the corresponding odds ratio that compares a category ? response to a category 1 response. Show the form of the variance that would be used in a Wald confidence interval. b) Repeat this problem for a proportional odds regression model.

Question 2 Dummy variables can be used to represent categorical data ___ a) only when the...

Question 2 Dummy variables can be used to represent categorical data ___ a) only when the categorical data is used as a response variable b) only when the categorical data is used as an explanatory variable c) when the categorical is used as either the response or explanatory variable d) Dummy variables can never be used to represent categorical data Question 3 Consider the following OLS regression equation: predicted y = b0 + b1X1 + b2d. The "X1" refers to...

1) True or False? In multinomial logistic regression analysis, the model fit is given by the...

1) True or False? In multinomial logistic regression analysis, the model fit is given by the value of R-squared. In linear regression analysis, the overall significance test is an F test. In logistic regression analysis, the overall significance test is an F test. When building a model for regression analysis, the type of data for the outcome variable guides you to choose linear, logistic, or multinomial logistic. Two variables that covary might not have a causal relationship. Causation implies correlation....

Hair Color and Job Title are examples of: continuous variables categorical variables quantitative variables ordinal variables...

Hair Color and Job Title are examples of: continuous variables categorical variables quantitative variables ordinal variables numerical variables discrete variables none of the above

When should logistic regression be used for data analysis? What is the assumption of logistic regression?...

When should logistic regression be used for data analysis? What is the assumption of logistic regression? How to explain odds ratio?

Consider a logistic regression model with a linear predictor that includes an interaction term, say x’β...

Consider a logistic regression model with a linear predictor that includes an interaction term, say x’β = β0 + β1x1 +β2x2+ β12x1x2. Does the odds ratio associated with the regressor x2 have the same interpretation as in the case where the linear predictor does not have the interaction term? Provide mathematical development to support your answer.

Define what categorical and continuous variables are and provide several examples of each.

Subjects