# Assumptions about Linear Regression Models or Error Term

The linear regression model (LRM) is based on certain statistical assumption, some of which are related to the distribution of random variable (error term) $\mu_i$, some are about the relationship between error term $\mu_i$ and the explanatory variables (Independent variables, *X’s*) and some are related to the independent variable themselves. We can divide the assumptions about linear regression into two categories

- Stochastic Assumption
- None Stochastic Assumptions

These assumptions about linear regression models (or ordinary least square method: OLS) are extremely critical to the interpretation of the regression coefficients.

- The error term ($\mu_i$) is a random real number i.e. $\mu_i$ may assume any positive, negative or zero value upon chance. Each value has a certain probability, therefore error term is a random variable.
- The mean value of $\mu$ is zero, i.e $E(\mu_i)=0$ i.e. the mean value of $\mu_i$ is conditional upon the given $X_i$ is zero. It means that for each value of variable $X_i$
*,*$\mu$ - The variance of $\mu_i$ is constant i.e. for the given value of
*X*, the variance of $\mu_i$ is the same for all observations. $E(\mu_i^2)=\sigma^2$. The variance of disturbance term ($\mu_i$) about its mean is at all values of*X*will show the same dispersion about their mean. - The variable $\mu_i$ has a normal distribution i.e. $\mu_i\sim N(0,\sigma_{\mu}^2$. The value of $\mu$ (for each $X_i$) have a bell shaped symmetrical distribution.
- The random term of different observation ($\mu_i,\mu_j$) are independent i..e $E(\mu_i,\mu_j)=0$, i.e. there is no autocorrelation between the disturbances. It means that random term assumed in one period does not depend of the values in any other period.
- $\mu_i$ and $X_i$ have zero covariance between them i.e. $\mu$ is independent of the explanatory variable or $E(\mu_i X_i)=0$ i.e. $Cov(\mu_i, X_i)=0$. The disturbance term $\mu$ and explanatory variable
*X*are uncorrelated. The $\mu$’s and $X$’s do not tend to vary together as their covariance is zero. This assumption is automatically fulfilled if*X*variable is nonrandom or non-stochastic or if mean of random term is zero. - All the explanatory variables are measured without error. It means that we will assume that the regressors are error free while
*y*(dependent variable) may or may not include error of measurements. - The number of observations
*n*must be greater than the number of parameters to be estimated or alternatively the number of observation must be greater than the number of explanatory (independent) variables. - The should be variability in the
*X*values. That is*X*values in a given sample must not be same. Statistically, $Var(X)$ must be a finite positive number. - The regression model must be correctly specified, meaning that there is no specification bias or error in the model used in empirical analysis.
- There is no perfect or near to perfect multicollinearity or collinearity among the two or more explanatory (independent) variables.
- Values taken by the regressors
*X*are considered to be fixed in repeating sampling i.e.*X*is assumed to non-stochastic. Regression analysis is conditional on the given values of the regressor(s)*X*. - Linear regression model is linear in the parameters, e.g. $y_i=\beta_1+\beta_2x_i +\mu_i$

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