Use the Sun Coast Remediation data set to conduct a correlation analysis, simple regression analysis, and multiple regression analysis using the correlation tab, simple regression tab, and multiple regression tab respectively. The statistical output tables should be cut and pasted from Excel directly into the final project document. For the regression hypotheses, display and discuss the predictive regression equations if the models are statistically significant. Delete instructions and examples highlighted in yellow before submitting this assignment.

**Correlation: Hypothesis Testing**

Restate the hypotheses from Unit II here.

Example:

*Ho1*: There is no statistically significant relationship between height and weight.

*Ha1*: There is a statistically significant relationship between height and weight.

Enter data output results from Excel Toolpak here.

Interpret and explain the correlation analysis results below the Excel output. Your explanation should include: *r, r2, *alpha level, *p* value, and rejection or acceptance of the null hypothesis and alternative hypothesis.

Example:

The Pearson correlation coefficient of *r *= .600 indicates a moderately strong positive correlation. This equates to an *r2* of .36, explaining 36% of the variance between the variables.

Using an alpha of .05, the results indicate a *p* value of .023 < .05. Therefore, the null hypothesis is rejected, and the alternative hypothesis is accepted that there is a statistically significant relationship between height and weight.

Note: Excel data analysis Toolpak does not automatically calculate the *p* value when using the correlation function. As a workaround, the data should also be run using the regression function. The Multiple *R* is identical to the Pearson* r *in simple regression, *R* Square is shown, and the *p* value is generated. Be sure to show your results using both the correlation function and simple regression function.

**Simple Regression: Hypothesis Testing**

Restate the hypotheses from Unit II here.

*Ho2*:

*Ha2*:

Enter data output results from Excel Toolpak here.

Interpret and explain the simple regression analysis results below the Excel output. Your explanation should include: multiple *R, R* squared, alpha level, ANOVA *F* value, accept or reject the null and alternative hypotheses for the model, statistical significance of the *x* variable coefficient, and the regression model as an equation with explanation.

**Multiple Regression: Hypothesis Testing**

Restate the hypotheses from Unit II here.

*Ho3*:

*Ha3*:

Enter data output results from Excel Toolpak here.

Interpret and explain the simple regression analysis results below the Excel output. Your explanation should include multiple *R, R* squared, alpha level, ANOVA *F* value, accept or reject the null and alternative hypotheses for the model, statistical significance of the *x* variable coefficients, and the regression model as an equation with explanation.

**References**

Include references here using hanging indentations. Remember to remove this example.

Creswell, J. W., & Creswell, J. D. (2018). *Research design: Qualitative, quantitative, and mixed methods approaches* (5th ed.). SAGE.