The correct statement about linear regression is: "Outcome variable is continuous".

Linear regression is a statistical method used to understand the relationship between two or more variables. In the context of linear regression, the outcome variable (also known as the dependent variable) is the variable that we are trying to predict or estimate.

In linear regression, the outcome variable is continuous, meaning it can take on any value within a certain range. Examples of continuous variables include height, weight, temperature, and time.

On the other hand, the input variables (also known as independent variables or predictors) in a linear regression model can be either continuous or discrete. Discrete variables are countable and have a finite number of possible values, while continuous variables can take on any value within a certain range.

Therefore, the statements "All input variables must be discrete" and "All input variables must be continuous" are incorrect, as input variables in linear regression can be either discrete or continuous. The statement "Outcome variable is discrete" is also incorrect, as the outcome variable in linear regression is continuous.

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The correct statement about linear regression is: "Outcome variable is continuous".

Linear regression is a statistical method used to understand the relationship between two or more variables. In the context of linear regression, the outcome variable (also known as the dependent variable) is the variable that we are trying to predict or estimate.

In linear regression, the outcome variable is continuous, meaning it can take on any value within a certain range. Examples of continuous variables include height, weight, temperature, and time.

On the other hand, the input variables (also known as independent variables or predictors) in a linear regression model can be either continuous or discrete. Discrete variables are countable and have a finite number of possible values, while continuous variables can take on any value within a certain range.

Therefore, the statements "All input variables must be discrete" and "All input variables must be continuous" are incorrect, as input variables in linear regression can be either discrete or continuous. The statement "Outcome variable is discrete" is also incorrect, as the outcome variable in linear regression is continuous.

Knowee AI · Accepted Answer

The correct statement about linear regression is: "Outcome variable is continuous".

Linear regression is a statistical method used to understand the relationship between two or more variables. In the context of linear regression, the outcome variable (also known as the dependent variable) is the variable that we are trying to predict or estimate.

In linear regression, the outcome variable is continuous, meaning it can take on any value within a certain range. Examples of continuous variables include height, weight, temperature, and time.

On the other hand, the input variables (also known as independent variables or predictors) in a linear regression model can be either continuous or discrete. Discrete variables are countable and have a finite number of possible values, while continuous variables can take on any value within a certain range.

Therefore, the statements "All input variables must be discrete" and "All input variables must be continuous" are incorrect, as input variables in linear regression can be either discrete or continuous. The statement "Outcome variable is discrete" is also incorrect, as the outcome variable in linear regression is continuous.

Which statement about linear regression is correct?All input variables must be discreteAll input variables must be continuousOutcome variable is continuousOutcome variable is discrete

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