1 5 Data Analysis: The Sun Coast Remediation Data Set Insert Your Name Here Insert University Here Course Name Here Instructor Name Here Date Data Analysis: Hypothesis Testing In this project, we are going to use the data set: Sun Coast Remediation in Microsoft Excel using the Data Analysis Toolpack to explore the correlation of variables and conduct regression analysis. The results of the analysis will be displayed here directly from Microsoft Excel and the resulting predictive regression equations will be discussed. Correlation: Hypothesis Testing Hypotheses: i. Microns versus mean annual sick days per employee Ho1: There is no significant linear relationship/correlation between microns and mean annual sick days per employee. Ha1:There is a significant linear relationship/correlation between microns and mean annual sick days per employee. microns mean annual sick days per employee microns 1 mean annual sick days per employee -0.715984185 1 Regression Statistics Multiple R 0.715984185 R Square 0.512633354 Adjusted R Square 0.507807941 Standard Error 1.327783455 Observations 103 ANOVA df SS MS F Significance F Regression 1 187.2953239 187.3 106.236 1.89059E-17 Residual 101 178.0638994 1.763 Total 102 365.3592233 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 10.08144483 0.315156969 31.989 1.17E-54 9.456258184 10.70663 9.456258 10.70663148 microns -0.522376554 0.050681267 -10.307 1.89E-17 -0.62291455 -0.42184 -0.62291 -0.421838554 The Pearson correlation coefficient is r = -0.71598 when rounded to 4 decimal places. Interpretation: It indicates there is a strong negative correlation between the two variables. The value of the coefficient of determination, r2 is 0.5126. Interpretation: About 51.26% of the variation between microns and mean annual sick days per employee is explained by the relationship. From the results the p-value is 1.89E-17, a very small value. By using the alpha level of significance to be 0.05 then the p-value is less than the alpha value i.e., 1.89E-17 < 0.05. As a result, we reject the null hypothesis and accept the alternative hypothesis. Therefore, we conclude that there is a statistically significant linear relationship between mean annual sick days per employee Simple Regression: Hypothesis Testing Hypotheses: Ho2:β1 = 0 (The regression is not significant) Ha2:β1 ≠ 0 (The regression is significant) SUMMARY OUTPUT Regression Statistics Multiple R 0.939559 R Square 0.882772 Adjusted R Square 0.882241 Standard Error 161.303 Observations 223 ANOVA df SS MS F Significance F Regression 1 43300521 43300521 1664.211 7.7E-105 Residual 221 5750122 26018.65 Total 222 49050644 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1753.602 30.36296 57.75465 2.6E-135 1693.764 1813.44 1693.764 1813.44 lost time hours -6.15739 0.150936 -40.7947 7.7E-105 -6.45485 -5.85994 -6. ...