Correlation and Regression Analysis Using Sun Coast Data SetUsing the Sun Coast data set, perform a correlation analysis, simple regression analysis, and multiple regression analysis, and interpret the results.Please follow the template (attached) to complete your assignment.You will utilize Microsoft Excel ToolPak for this assignment.Example:Correlation AnalysisRestate the hypotheses.Provide data output results from Excel Toolpak.Interpret the correlation analysis results Simple Regression AnalysisRestate the hypotheses.Provide data output results from Excel Toolpak.Interpret the simple regression analysis results Multiple Regression AnalysisRestate the hypotheses.Provide data output results from Excel Toolpak.Interpret the multiple regression analysis results. The title and reference pages do not count toward the page requirement for this assignment. This assignment should be no less than two pages in length, follow APA-style formatting and guidelines, and use references and citations as necessary.Running head: INSERT TITLE HERE

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Data Analysis: Hypothesis Testing

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.

Correlation: Hypothesis Testing

Restate the hypotheses:

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.

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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:
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 square, 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:
Ha3:
Ha3:
Enter data output results from Excel Toolpak here.
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Interpret and explain the simple regression analysis results below the Excel output. Your
explanation should include: multiple R, R square, 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.
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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.). Thousand Oaks, CA: Sage.
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