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Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. If the dependent variable is dichotomous, then logistic regression should be used.

## What type of model would you use for a continuous dependent variable?

Linear models are the most common and most straightforward to use. If you have a continuous dependent variable, linear regression is probably the first type you should consider.

## Which method is used for predicting continuous dependent variable Mcq?

1 Answer. Logistic Regression is used for classification problems.

## Can logistic regression be used for continuous dependent variable?

The logit regression model is generally used as a method for estimating relationships in which the dependent variable is binary in nature, though it is also useful for estimation when the dependent variable is continuous but bounded on the unit intervals.

## Which algorithm is used to predict continuous values?

1) Linear Regression

Linear regression algorithm is used if the labels are continuous, like the number of flights daily from an airport, etc. The representation of linear regression is y = b*x + c. In the above representation, ‘y’ is the independent variable, whereas ‘x’ is the dependent variable.

## What type of model could be used to model a continuous variable using independent variables?

All Answers (7) A general linear model (i.e. lm in R; PROC GLM in SAS) can be used for a continuous dependent variable and any number of continuous or categorical independent variables and their interactions.

## What is continuous dependent variable?

If a variable can take on any value between its minimum value and its maximum value, it is called a continuous variable; otherwise, it is called a discrete variable.

## Which of the following is the best method to predict the values of the dependent variables?

Least squares regression is used to predict the behavior of dependent variables.

## Which of the following is a method of regression analysis?

Regression Analysis – Linear Model Assumptions

Linear regression analysis is based on six fundamental assumptions: The dependent and independent variables show a linear relationship between the slope and the intercept. The independent variable is not random. The value of the residual (error) is zero.

## Which is are the predictor variable s in this example?

In this example, attendance is the predictor variable. A predictor variable is a variable that is being used to predict some other variable or outcome. In the example we just used now, Mia is using attendance as a means to predict another variable, grade point average.

## Can logistic regression predict continuous variables?

1 Answer. Yes… You can.!! Prediction using Logistic Regression can be done for numerical variables.

## What is stepwise method?

Stepwise regression is a method that iteratively examines the statistical significance of each independent variable in a linear regression model. … The backward elimination method begins with a full model loaded with several variables and then removes one variable to test its importance relative to overall results.

## Can you use continuous variables in linear regression?

In linear regression the independent variables can be categorical and/or continuous. But, when you fit the model if you have more than two category in the categorical independent variable make sure you are creating dummy variables.

## How do you predict a continuous variable?

Regression Analysis. Regression analysis is used to predict a continuous target variable from one or multiple independent variables. Typically, regression analysis is used with naturally-occurring variables, rather than variables that have been manipulated through experimentation.

## Can a neural network predict a continuous variable?

That is one of the key points with using them instead of a linear model. A neural net can , at least theoretically, approximate any continuous function.

## Can neural network predict continuous value?

MNIST data

When you prepare your own dataset, you can train a neural network that estimates continuous values by preparing a dataset that provides a correct value y for image x.