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 of r 600 indicates a moderately strong positive correlation This equates to an r 2 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

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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,

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