ISYS 4193 Test 2

Be able to clearly and succinctly define r and R2 and be able to discuss the difference between and their use.

Coefficient of correlation is "R" value which is given in the summary table in the Regression output. R square is also called coefficient of determination. Multiply R times R to get the R square value. In other words Coefficient of Determination is the square of Coefficient of Correlation. R square or coeff. of determination shows percentage variation in y which is explained by all the x variables together. Higher the better. It is always between 0 and 1. It can never be negative - since it is a squared value.It is easy to explain the R square in terms of regression. It is not so easy to explain the R in terms of regression.

Be able to explain dummy variables: why they are used; how they are coded; how they are interpreted. Be able to transform a variable into dummy variable(s).

Dummy or indicator variables are categorical independent variables.`

Be able to explore and explain normality assumptions.

Linear relationship, multivariate normality, no or little multicollinearity, no autocorrelation, homoscedasticity

Understand what logistic regression is and in what situation(s) we would use it. Be able to interpret its output.

Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.

Be able to define, discussion, evaluate multicollinearity.

Multicollinearity refers to the correlation among the independent variables. When the independent variables are highly correlated it is not possible to determine the separate effect of any particular independent variable on the dependent variable.

In reading or interpreting SAS output from a regression model, be able to explain the difference between an F test and t test?

The F test shows an overall significance, the t test is used to determine whether each of the individual independent variables is significant.

Be able to identify and interpret the point estimate of the difference between two population means given SAS EG output. Understand how this procedure is completed in SAS EG and be able to complete it.

The difference between the two population means is m1-m2. The point estimator of the difference between means of the populations 1 and 2 is x1-x2(x bars). (ch10 ppt slides 4-11)

Be able to evaluation a data set, given the DV and IVs of interest, and evaluate. This could be in the form of a problem you must evaluate in SAS EG or output from analysis for you to interpret.

The variable being predicted is called the dependent variable and is denoted by y. The variables being used to predict the value of the dependent variable are called the independent variables and are denoted by x.

Know the difference between paired/matched samples and not matched.

With a matched-sample design each sampled item provides a pair of data values. This design often leads to a smaller sampling error than the independent-sample design because variation between sampled items is eliminated as a source of sampling error.

Be able to calculate a predicted value given the estimated regression equation.

Y=B0 + B1x1 + B2x2 + E

Be able to discussion the difference between R2 and Adjusted R2.

R-squared measures the proportion of the variation in your dependent variable (Y) explained by your independent variables (X) for a linear regression model. Adjusted R-squared adjusts the statistic based on the number of independent variables in the model. The reason this is important is because you can "game" R-squared by adding more and more independent variables, irrespective of how well they are correlated to your dependent variable. Obviously, this isn't a desirable property of a goodness-of-fit statistic. Conversely, adjusted R-squared provides an adjustment to the R-squared statistic such that an independent variable that has a correlation to Y increases adjusted R-squared and any variable without a strong correlation will make adjusted R-squared decrease. That is the desired property of a goodness-of-fit statistic. About which one to use...in the case of a linear regression with more than one variable: adjusted R-squared. For a single independent variable model, both statistics are interchangeable.

Know how to calculate the interval estimate and do hypothesis tests of a population variance. Understand the use of the chi squared distribution. Be able to run it in SASEG and interpret the results.

The chi-squared distribution is the sum of squared standardized normal random variables. The chi-square distribution is based on sampling from a normal population. We can use the chi-square distribution to develop interval estimates and conduct hypothesis tests about a population variances.