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.

Example:

The Pearson correlation coefficient ofr= .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.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.601841822
R Square	0.362213579
Adjusted R Square	0.360083364
Standard Error	5.51856585
Observations	1503
ANOVA
	df	SS	MS	F	Significance F
Regression	5	25891.88784	5178.378	170.0361	2.1289E-143
Residual	1497	45590.48986	30.45457
Total	1502	71482.3777
	Coefficients	Standard Error	t Stat	P-value	Lower 95%
Intercept	126.8224555	0.623820253	203.2997	0	125.5988009
Frequency (Hz)	-0.0011169	4.7551E-05	-23.4885	4.1E-104	-0.001210174
Angle in Degrees	0.047342353	0.037308069	1.268957	0.204654	-0.025839288
Chord Length	-5.495318335	2.927962181	-1.87684	0.060734	-11.23866234
Velocity (Meters per Second)	0.083239634	0.009300188	8.950317	1.02E-18	0.064996851
Displacement	-240.5059086	16.51902666	-14.5593	5.21E-45	-272.9088041