UNL PSYC 451 - Preparation for the Story Problem Portion of Quiz #1

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Preparation for the Story Problem Portion of Quiz #1Correlations, Bivariate Regression b-weights & Multiple Regression b-weights1. Tell how to interpret each of the following correlations- + r for a quantitative (continuous) predictor variable- nsig r for a quantitative (continuous) predictor variable- -r for a quantitative (continuous) predictor variable- + r for a binary predictor variable- nsig r for a binary predictor variable- -r for a binary predictor variable2. Tell how to interpret each of the following simple regression weights- + b for a quantitative (continuous) predictor variable- nsig b for a quantitative (continuous) predictor variable- -b for a quantitative (continuous) predictor variable- + b for a binary predictor variable- nsig b for a binary predictor variable- -b for a binary predictor variable3. Tell how to interpret each of the following multiple regression weights- + b for a quantitative (continuous) predictor variable- nsig b for a quantitative (continuous) predictor variable- -b for a quantitative (continuous) predictor variable- + b for a binary predictor variable- nsig b for a binary predictor variable- -b for a binary predictor variable4. 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 1a. interpreting 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)b. 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 scorec. interpreting 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 !!! Supressor variablePractice #1Correlation & Multiple RegressionCorrelations.357 .013 .826 -.354.035 .891 .000 .036120 120 120 120Pearson CorrelationSig. (2-tailed)NRATING2AGE GENDER SALARY NFRNDSModel Summary.828a.685 .674 2.28317Model1R R SquareAdjustedR SquareStd. Error ofthe EstimatePredictors: (Constant), NFRNDS, SALARY, AGE,GENDERa. a. What are the viable individual predictors?b. Interpret the simple correlation of age.ANOVAb1304.528 4 326.132 62.563 .000a599.481 115 5.2131904.009 119RegressionResidualTotalModel1Sum ofSquares df Mean Square F Sig.Predictors: (Constant), NFRNDS, SALARY, AGE, GENDERa. Dependent Variable: RATING4b. c. Interpret the simple correlation of gender.d. Does the model work? What did you look at to decide?e. How well does the model work? Coefficientsa.352 1.704 .206 .837.027 .042 .034 .643 .52210.244 .425 .030 24.57 .001.030 .000 .826 15.74 .003-.402 .074 .001 5.343 .040(Constant)AGEGENDERSALARYNFRNDSModel1BStd.ErrorUnstandardizedCoefficientsBetaStandardizedCoefficientst Sig.Dependent Variable: RATING4a. f. Which predictors contribute to the model? What did you look at to decide?g. Would the model “do as well” if age were dropped from the model? Explain your answer.h. Would the model “do as well” if salary were dropped from the model? Explain your answer.i. That is the most likely reason that age is not contributing to the model?j. Interpret the


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UNL PSYC 451 - Preparation for the Story Problem Portion of Quiz #1

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