Preparation for the Story Problem portion of Psyc451 Exam #1Some conceptual stuff to start…1a. Tell how to interpret each of the following correlations+ r for a quantitative (continuous) predictor variablensig r for a quantitative (continuous) predictor variable-r for a quantitative (continuous) predictor variable+ r for a binary predictor variablensig r for a binary predictor variable-r for a binary predictor variableb. Tell how to interpret each of the following simple regression weights+ b for a quantitative (continuous) predictor variablensig b for a quantitative (continuous) predictor variable-b for a quantitative (continuous) predictor variable+ b for a binary predictor variablensig b for a binary predictor variable-b for a binary predictor variablec. Tell how to interpret each of the following multiple regression weights+ b for a quantitative (continuous) predictor variablensig b for a quantitative (continuous) predictor variable-b for a quantitative (continuous) predictor variable+ b for a binary predictor variablensig b for a binary predictor variable-b for a binary predictor variabled. When one considers the correlation of a specific predictor with the criterion and that predictor's contribution to a multiple regression, there are nine possibilities. Specify each of them (there might be a "special name" or maybe just a description. Correlation Multiple Regression significant - non-significant significant + Weightsignificant - non-significant significant +Answers to Some conceptual stuff to startinterpreting correlationsquant predictors+r direct relationship -- those with higher scores on the predictor tend to have higher scores on the criterion (and vice versa)nsig r no reliable relationship between pred and crit -- knowing value of one tells you nothing about value of the other-r indirect relationship -- those with higher scores on the predictor tend to have lower scores on the criterion (and vice versa)binary predictors+r group with higher coded value has higher mean score on the criterion (and vice versa)nsig r no reliable mean difference on the criterion between the groups-r group with the higher coded value has lower mean score on the criterion (and vice versa)interpreting simple regression weightsquant predictors+b direct relationship -- each 1-point increase in the predictor is expected to be associated with an increase in the predicted criterion score equal to "b"nsig b no reliable prediction about the change in the predicted criterion score based on changes in that predictor, -b indirect relationship -- each 1-point increase in the predictor is expected to be associated with an decrease in the predicted criterion score equal to "b"binary predictors+b group with higher coded value had a mean on the criterion score "b" higher than the group with the lower coded scorensig b no reliable mean difference on the criterion between the groups-b group with higher coded value had a mean on the criterion score "b" lower than the group with the lower coded scoreinterpreting multiple regression weightsquant predictors+b direct relationship -- each 1-point increase in the predictor is expected to be associated with an increase in the predicted criterion score equal to "b", if the values of the other predictors are held constant (controlled for) (and vice versa)nsig b no reliable prediction about the change in the predicted criterion score based on changes in that predictor, ", if the values of the other predictors are held constant (controlled for) (and vice versa)-b indirect relationship -- each 1-point increase in the predictor is expected to be associated with an decrease in the predicted criterion score equal to "b", if the values of the other predictors are held constant (controlled for) (and vice versa)binary predictors+b group with higher coded value had a mean on the criterion score "b" higher than the group with the lower coded score, if the values of the other predictors are held constant (controlled for) (and vice versa)nsig b no reliable mean difference on the criterion between the groups, if the values of the other predictors are held constant (controlled for) -b group with higher coded value had a mean on the criterion score "b" lower than the group with the lower coded score, if the values of the other predictors are held constant (controlled for) (and vice versa)Considering correlations and regression weights Correlation Multiple Regression significant - non-significant significant + Weightsignificant - *** !!! !!! non-significant ^^^ boring variable ^^^significant + !!! !!! ****** good correlate & direct contributor ^^^ good correlate, but collinear with other predictors !!! Suppressor variableMultiple Regression Practice #1 1. Here's a set of correlations and a full-model regression with "Therapeutic Outcome" (larger scores are "better") for "Type of Therapy" (1=conventional 2=experimental). Predictor ==> Initial Amount Number of Age Wellness Prior Current Type of Therapy Sessions Therapy correlation .42 .38 -.43 .18 .45 (p-value) (.03) (.04) (.03) (.21) (.03) reg. weight -3.21 2.21 -1.89 .512 8.24 (p-value) (.01) (.89) (.14) (.04) (.04)a. Based on the simple correlations, which are viable single predictors?b. How would you interpret the correlation of the following predictors and the criterion variable?AgeAmount of Prior TherapyType of Therapyc. Which predictors are contributing to the full model?d. How would you interpret the multiple regression weight of the following predictors?AgeInitial WellnessNumber of Current sessionsType of Therapye. What is the most likely reason that Initial Wellness is not contributing to the full model?f. What is the most likely reason that Type of Therapy is contributing to the full model?g. Any suppressor variables? How would you NOT want to interpret the regression weight of that variable?2. Consider these output and answer the questions.