1. 1. Please use JASP and G Power as needed.
2. 2. Provide a context of the data set in the supplied data file. Specifically, imagine that you are a teacher studying how well scores on Quiz 1 (X1), GPA (X2), and the total points in the course (X3) predict the final grade in the course (Y). Identify your predictor variables, the outcome variable, and the scales of measurement for each variable. Specify the sample size of the data set.
3. 3. Specify a research question for the overall regression model. Articulate a null hypothesis and alternative hypothesis for the overall regression model. Specify a research question for each predictor. Articulate the null hypothesis and alternative hypothesis for each predictor. Specify the alpha level.
4. 4. Test the four assumptions of multiple regression.
   • . Begin with the four histograms on X1, X2, X3, and Y, and provide visual interpretations of normality.
   • . Next, Provide scatter plots and interpret them in terms of linearity and bivariate outliers.
   • . Next, provide a correlation matrix (Pearson's r) and interpret it to check the multicollinearity assumption.
   • . Finally, paste the plot of standardized residuals and interpret it to check the homoscedasticity assumption.
5. 5. Begin with a brief statement reviewing assumptions.
   • . Run a linear regression using the Enter method.
   • . Next, paste the Model Summary. Report R and R2 in the correct format; interpret R2 effect size.
   • . Next, paste the ANOVA output. Report the F test for p value and interpret them against the null hypothesis.
   • . Next, paste the Coefficients output. For each predictor, report the standardized coefficient and the t-test results, including interpretation against the null hypothesis, the semipartial squared correlation effect size, and the interpretation of effect size.
   • . Next, rerun the regression analysis choosing Backward rather than entry. Report which variable or variables were entered into the equation and which were removed from the equation. Report the R, R squared, adjusted R squared, F test, and p value of the final model that best predicts the variance in the outcome variable.
   •  
     • . provide a table that displays your multiple regression results following the example below.
   • . Discuss your conclusions of the multiple regression as they relate to your stated research questions for the overall regression model and the individual predictors. Conclude with an analysis of the strengths and limitations of multiple regression.

Table 1

Results of Multiple Linear Regression with Final Score as a Dependent Variable (N = 200)

Variables	Final Score	Quiz 1	Quiz 2	Quiz 3	b (95% CI)	t-statistics (t-test)	sr2 unique
Quiz 1	.612*				5.9 (5.2-8.9)	4.53 (t)	0.198
Quiz 2	.479*	0.152**			3.3 (2.2-4.3)	3.53 (t)	0.462
Quiz 3	.875*	.797*	.318*		6.3 (5.2-6.9)	5.22 (t)	0.531
					Intercept = -.472
Means	61.84	6.00	2.86	100.09
SD	7.64	2.48	0.71	13.43
ANOVA						236.67* (F)
Model						.936 (R)
Model						.875 (R2)
Model						.872 (adj R2)

Note: CI = confidence interval; SD = standard deviation

*p<.001

**p <.05