 # Simple Regression Analysis

813 Words 4 Pages
Introduction to Regression Analysis

Student Name

Institution

Regression analysis has been employed as serious evidence by lawyers and other individuals in the legal field. For instance, in the 1964 Civil Rights Act under Title VII, it was used to prove contract actions damages, biasness with regard to race in litigating death penalties and others.
The difference between multiple and simple regression is that for multiple regression, earnings are affected by much more factors in addition to years spent in school while simple regression just assumes that an individual’s earnings are affected solely by the years spent schooling.
Usually omitted variables are likely to result from simple regression because not all variables
One such alternative include minimizing the summation of errors in absolute quantities.
According to Sykes, unbiasedness is when a parameter’s true value equals the mean of probability distribution. He states that consistency is comparison of different estimators that are unbiased and finding the lowest variance. He also defines consistency as generation of accurate estimates by taking advantage of extra data.
The difference in interpretation of coefficient estimates is that multiple regression has the coefficients γX while simple regression does not
Lower variance is an attractive property for an estimator because it lowers the probability of an estimate being far from the true value.
The assumptions about the noise term which makes the estimator obtained by application of the minimum SSE criterion BLUE is that it is taken from a distribution with a mean of zero and also the distributions from which the noise terms are derived have the same variance.
According to Sykes, the logic behind the t-test is that we formulate a hypothesis. We can either accept or reject the hypothesis depending on where the t-statistic falls, that is, in the uppermost of lowermost tail of the