Question 1 2 Points A variable such as income levels can be a dependent variable and a dichotomy. In which regression model would this variable be deployed? A Logistic Regression B Simple Regression C Multiple Regression D Poisson Regression Question 2 2 Points In logistic regression, when the correlation coefficient between two predictor variables is very closely correlated, what would you do to these predictor variables in the model? remove both of the closely correlated predictors B leave both of the predictor variables alone C remove one of the predictor variables D find other predictor variables that are closely correlated with each otherQuestion 3 2 Points Non-parametric statistical techniques are considered distribution free. This means that: A the scores on the dependent variable approximate to a normal distribution. B the scores on the dependent variable are drawn from a population where the variable can be assumed to be normally distributed. C the scores on the dependent variable show homogeneity of variance between groups of participants. the techniques do not involve any assumptions about the distribution of the population from which the sample dependent variable measures are drawn. Question 4 2 Points The shape of a variable's distribution can be evaluated in terms of its: A heterogeneity of variance B skewness and kurtosis C level of significance and p value D level and quantumQuestion 5 2 Points The statistical methodology that utilizes the relation between two or more quantifiable variables so that a response or outcome variable can be predicted is called: A a correlation analysis B a regression analysis C the scatter plot D the trial Question 6 2 Points Multiple regression is used to answer the following question(s): A What is the relative importance of the predictor variables included in the analysis? B Can one make an even better prediction if one includes an additional variable in the equation? C Given two alternative sets of predictors, which one is the more effective? D All of the above