HW 6 - FA21-1

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Arizona State University, Tempe *

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Apr 3, 2024

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HW 6 Assignment HW 6 Chapter 16 1. What does regression allow researchers to assess that correlation does not? ( 1pt ) 2. The difference between an actual Y-score and the predicted Y-score is termed the error in prediction. (1pt) 3. Discuss the “setup” for linear regression, or the necessary aspects for running a linear regression. Specifically: (2pts) a. The independent variable (IV) should be (i.e. nominal/ordinal/interval/ratio): b. The dependent variable (DV) should be (i.e. nominal/ordinal/interval/ratio): 4. Make the appropriate conclusions regarding the relationship between X and Y’ using the following SPSS output box. ( 3pts ) a. Was the regression coefficient significant or not significant? How do you know? interval or ratio levels of measurement (mostly). In special cases, nominal/ ordinal levels are used. interval or ratio levels of measurement. Yes the regression coefficient was significant as the value of .000 is <.05. Regression allows researchers to predict the value of one variable from another, while correlation only reveals the relationship between two variables. Correlation does equal causation.

HW 6 Assignment b. Explain the relationship between X and Y’ based on the results in the output table. (Use the Unstandardized coefficient): 5. What is multiple regression and what makes it different from simple/linear regression? ( 1pt) 6. What are two of the rules for choosing multiple predictor variables for multiple regression? ( 2pts ) According to the unstandardized coefficient, X and Y have a positive linear regression relationship as Y= 134.140 + .096 In multiple regression you are able to have multiple predictor variables and predict outcomes from two independent variables, whereas in a simple/linear regression you’d only be able to predict an outcome from 1 independent variable. 1. Select a predictor variable (X) that is related/ has something in common with the criterion variable (Y) 2. When selecting multiple predictor variables (X1 and X2), ensure that they are independent from ONE ANOTHER but still related to the predicted Y variable.

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