Why Are Each of the Individual Simple Linear Regression Model

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The Answer is Assumption 1:If the model is not correctly specified then the estimates based on it will not be meaningful or true.

Assumption 2:If the data is not collected through random sampling then a model that is specified conditional on the observations not being related to each other then the estimated coefficients will be wrong.

Assumption 3:If the independent variable does not vary then

\hat { \beta } _ { 1 }

is not defined and therefore an estimate cannot be derived.

Assumption 4:If the average value of the error term is not equal to zero then the estimates on average will be wrong on average.

Assumption 5:If the error term is related to the independent variables,then if the independent variable increases by one unit then the error term will increase as well,therefore not allowing for a measurement of how the independent variable affects the dependent variable holding other factors constant (because the error term changed as well).

Assumption 6:If the variance of the error term is not constant then OLS,that treat all observations the same,is not the most preferred method to estimate the regression model.

Why Are Each of the Individual Simple Linear Regression Model

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Why Are Each of the Individual Simple Linear Regression Model

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