When we fit a linear model, we make several assumptions. These include:

1. Independence: The observations are assumed to be independent of each other. This is a common assumption in many statistical models.

2. Normality of errors: We assume that the errors of the model, i.e., the differences between the observed and predicted values, follow a normal distribution.

3. Equality of variance of errors: This is also known as homoscedasticity. We assume that the variance of the errors is constant across all levels of the independent variables.

The assumptions that σ2 = 0 and β0 = β1 = 0 are not typically made when fitting a linear model. The assumption σ2 = 0 would imply that there is no variability in the outcome variable, which is not realistic in most situations. The assumption β0 = β1 = 0 would imply that there is no relationship between the independent and dependent variables, which contradicts the purpose of building a linear model to understand this relationship.

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When we fit a linear model, we make several assumptions. These include:

1. Independence: The observations are assumed to be independent of each other. This is a common assumption in many statistical models.

2. Normality of errors: We assume that the errors of the model, i.e., the differences between the observed and predicted values, follow a normal distribution.

3. Equality of variance of errors: This is also known as homoscedasticity. We assume that the variance of the errors is constant across all levels of the independent variables.

The assumptions that σ2 = 0 and β0 = β1 = 0 are not typically made when fitting a linear model. The assumption σ2 = 0 would imply that there is no variability in the outcome variable, which is not realistic in most situations. The assumption β0 = β1 = 0 would imply that there is no relationship between the independent and dependent variables, which contradicts the purpose of building a linear model to understand this relationship.

Knowee AI · Accepted Answer

When we fit a linear model, we make several assumptions. These include:

1. Independence: The observations are assumed to be independent of each other. This is a common assumption in many statistical models.

2. Normality of errors: We assume that the errors of the model, i.e., the differences between the observed and predicted values, follow a normal distribution.

3. Equality of variance of errors: This is also known as homoscedasticity. We assume that the variance of the errors is constant across all levels of the independent variables.

The assumptions that σ2 = 0 and β0 = β1 = 0 are not typically made when fitting a linear model. The assumption σ2 = 0 would imply that there is no variability in the outcome variable, which is not realistic in most situations. The assumption β0 = β1 = 0 would imply that there is no relationship between the independent and dependent variables, which contradicts the purpose of building a linear model to understand this relationship.

What do we assume when we fit a linear model? Select all that apply.Group of answer choicesσ2 = 0Independenceβ0 = β1 = 0Normality of errorsEquality of variance of errors

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