Akaike information criterion

Examples of Akaike information criterion in the following topics:

• Stepwise Regression

  • Usually, this takes the form of a sequence of $F$-tests; however, other techniques are possible, such as $t$-tests, adjusted $R$-square, Akaike information criterion, Bayesian information criterion, Mallows's $C_p$, or false discovery rate.
  • Forward selection involves starting with no variables in the model, testing the addition of each variable using a chosen model comparison criterion, adding the variable (if any) that improves the model the most, and repeating this process until none improves the model.
  • Backward elimination involves starting with all candidate variables, testing the deletion of each variable using a chosen model comparison criterion, deleting the variable (if any) that improves the model the most by being deleted, and repeating this process until no further improvement is possible.
  • This problem can be mitigated if the criterion for adding (or deleting) a variable is stiff enough.

• Statistical Power

  • The Statistical Significance Criterion Used in the Test: A significance criterion is a statement of how unlikely a positive result must be, if the null hypothesis of no effect is true, for the null hypothesis to be rejected.
  • One easy way to increase the power of a test is to carry out a less conservative test by using a larger significance criterion, for example 0.10 instead of 0.05.
  • An unstandardized (direct) effect size will rarely be sufficient to determine the power, as it does not contain information about the variability in the measurements.
  • Let's say we look for a significance criterion of 0.05.

• Introduction to Multiple Regression

  • In simple linear regression, a criterion variable is predicted from one predictor variable.
  • In multiple regression, the criterion is predicted by two or more variables.
  • In multiple regression, it is often informative to partition the sums of squares explained among the predictor variables.
  • Specifically, they are the differences between the actual scores on the criterion and the predicted scores.
  • It is assumed that the relationship between each predictor variable and the criterion variable is linear.

• Defining conditional probability

  • It is useful to think of the condition as information we know to be true, and this information usually can be described as a known outcome or event.
  • Suppose we were provided only the information in Table 2.13 on the preceding page, i.e. only probability data.
  • Then if we took a sample of 1000 people, we would anticipate about 47% or 0.47 × 1000 = 470 would meet our information criterion.
  • Similarly, we would expect about 28% or 0.28 × 1000 = 280 to meet both the information criterion and represent our outcome of interest.
  • The complement still appears to work when conditioning on the same information.

• Glossary

  • In general, the criterion variable is the dependent variable.
  • The degrees of freedom of an estimate is the number of independent pieces of information that go into the estimate.
  • Two variables are said to be independent if the value of one variable provides no information about the value of the other variable.
  • In multiple regression, the criterion is predicted from two or more predictor variables.
  • In the following example, the criterion (Y) is predicted by X, X2 and, X3.

• Remarks on the Concept of “Probability”

  • This is a natural idea but nonetheless unreasonable if we have further information relevant to whether it will rain tomorrow.
  • Two people might attach different probabilities to the election outcome, yet there would be no criterion for calling one "right" and the other "wrong."

• Inferential Statistics for b and r

  • The column X has the values of the predictor variable and the column Y has the values of the criterion variable.
  • We now have all the information to compute the standard error of b:

• Measurement

  • Finally, if a test is being used to select students for college admission or employees for jobs, the higher the reliability of the test the stronger will be the relationship to the criterion.
  • Items that are either too easy so that almost everyone gets them correct or too difficult so that almost no one gets them correct are not good items: they provide very little information.

• Hypothesis testing exercises

  • (e) If your hypothesis test and confidence interval suggest a significant increase in revenue per customer, why might you still not recommend that the restaurant owner extend the happy hour based on this criterion?
  • No information is provided about the skew.
  • (d) There is not enough information to tell.
  • No information is provided about the skew, so this is another item we would typically ask about.