Question

Regression and Correlation Part 1 -Hypothesis test for u a) Use the data provided Perform a complete hypothesis test (show all steps) of Ho: 4 = 42 Use a -.05 Word your conclusion clearly in plain English so that a 14-year-old would understand it. For this part of the assignment, we'll look to see if there's an association between the responses of your two numerical questions PART 2 - Regression and Correlation Preparation. Obtain a scatter plot of your paired data. Show the line of best fit on the plot along with the equation of the line and the coefficient of determination (R). Use Regression analysis in Excel to obtain the test statistic and p-value for the hypothesis test of H,: p=0 vs. H,: p#0. The Analysis State the two numerical questions. Indicate which variable is x, and which is the dependent variable y. Explain why you assigned the variables in this way. Display the scatter plot showing the regression line, regression equation, and coefficient of determination (r). State the regression equation and explain the meaning of the slope. Calculate and state the value of r, the sample correlation coefficient. State the percentage of variability in your "y" variable that is explained by the linear relationship with your "x" variable. Write up the complete hypothesis test to see whether there is significant correlation between your two numerical variables at the .05 level of significance. Put some effort into clearly and correctly wording your conclusion in the context of the two variables that you measured with these questions.

part 1 is done need help with part 2 please

Answer 1

E7 H. 45 180 A4.5 173.2 intercept 45.77723 Slope 39.5 155 2.763103 43.9 168 190 170 47 185 49.1 185.2 V=2.7631x + 45.777 180 R=U.6358 48 181.1 47.9 175 181.9 170 156.8 40.6 171 165 45.5 Linear (v) 46.5 175.5 160 12 43 13 158.5 155 41 14 163 150 10 20 30 40 50 60 39.5 15 155 Forearm length 16 166 43.5 17 41 158 165 48 167 45.5 ेली -46 19 167 162 42 161 46 181 156 45.6 43,9 172 167 Sheet4 Sheeti Sheet2 heetB 10US

SUMMARY OUTPUT Regression Statistics 4 Multiple R 0.797350341 0.635767567 R Square Adjusted R Square Standard Error 0.619211547 5.766253122 Observations 24 10 ANOVA p value F Significance F 3.09E-06 3.09E-06 df SS 1276.82 38.40099 12 Regression. 13 Residual 14 Total 1276.820482 731.4928515 33.24968 22 23 2008.313333 t Stat Lower 95% Upper 95%ower 95.0%pper 95.0% 19.73965712 2.319049 0.030071 4.839682848 86.71477 4.839683 86.71477 0.445888003 6.196853 3.09E-06 1.838387438 3.687818 1.838387 3.687818 Coefficients 45.77722611 P-value Standard Error 17 Intercept 2.76B102558

FORe asm length be herght be the indepemdemt vasiable Cx) and Let the dependent vasiable Cy). (becaus e length hesgnt a depends on the leng th of fouaim fone aom ungth Choose as Indep. and hergut- 163 41 as dependent] (73.2 44.5 155 39.5 (55 166 89-5 43-5 168 L58 43.9 41-0 (70 165 47 42 185-2 167 49.1 45.5 L81.1 162 48 46 (81.9 LGI 47-9 42 I56-8 181 40-6 46 ILI 175.5 156 45.5 45.6 172 46.5 43.9 158-5 167 43 44-) Y=a+bx egesioo eg. is, a-s intescept b- 8l0 pe a= 45.171 = 45.78 b= 2-163 = 2-16 Rco de a= INTERCE PT (Known y's, koown'x's) b= SLOPE CKnowo y's, Known x's) OR Data → data analysıs → Repession→ Chuose values → ok equationy-45.18+ 2.76 x *Rycession Coefficient of detesminatton R- 0.6357 x changes of * 8 lope = 2:16, Is The chamye sate of as Fore as m length) , fos unit Change in x Cie, unit change in e the haight units. Changes 2.16 highly positive cOArelation 0.1973 5 *Sample Cos aelation oeffi; 8 = asso ciation of x4y It is the measare - oF

le, length of dosearm increases thes herght also increases, also derriases thes hught OR length of faearm deereases * here Coefficient o4 detesminaton R- 0-635I = 0-6 36 ase explained by e 63.57 1. of dependent vasl ables Cy-values) vasiables (z- values). the independent variance of depemdent vasia bles R? Repeuents the pescentage that is explained by the model. gnificant conselares these i3 no Ho: f=0 test * we ie want to betweep x and y. Hii $t0 valu of statistic = 3&.400 Sgnificance level = o .05 Heu P valu (fRom table)= 3.09E-06 = .00000309 here p value < Bignificana level signi ficant coruelation there we seject Ho ie, ie Jto between
Conclusion of the test is that we reject the null hypothesis that there is no significant correlation between forearm length and the height. ie., There is a significant correlation between these two variables...that means there is a linear relationship ship between the length of forearm and the height.

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