Question #285261

the following are the ages of husbands and wives for six couples. husbands age: (43, 57, 28, 19, 35, 39) wifes age: (37, 51, 32, 20, 33, 38)


1
Expert's answer
2022-01-07T07:37:58-0500

a. We expect the ages of husbands and wives to be positively related.


b.


By looking at the scatter diagram, we expect the correlation coefficient between these two variables to be close to 1.1.


c.


XYXYX2Y24337159118491369575129073249260128328967841024192038036140035331155122510893938148215211444Sum=221211841189897927\def\arraystretch{1.5} \begin{array}{c:c:c:c:c:c} & X & Y & XY & X^2 & Y^2 \\ \hline & 43 & 37 & 1591 & 1849 & 1369 \\ \hdashline & 57 & 51 & 2907 & 3249 & 2601 \\ \hdashline & 28 & 32 & 896 & 784 & 1024 \\ \hdashline & 19 & 20 & 380 & 361 & 400 \\ \hdashline & 35 & 33 & 1155 & 1225 & 1089 \\ \hdashline & 39 & 38 & 1482 & 1521 & 1444 \\ \hdashline Sum= & 221 & 211 & 8411 & 8989 & 7927 \\ \hdashline \end{array}

Xˉ=1ni=1nXi=2216=36.833333\bar{X}=\dfrac{1}{n}\displaystyle\sum_{i=1}^nX_i=\dfrac{221}{6}=36.833333

Yˉ=1ni=1nYi=2116=35.166667\bar{Y}=\dfrac{1}{n}\displaystyle\sum_{i=1}^nY_i=\dfrac{211}{6}=35.166667

SSXX=i=1nXi21n(i=1nXi)2=898922126SS_{XX}=\displaystyle\sum_{i=1}^nX_i^2-\dfrac{1}{n}(\displaystyle\sum_{i=1}^nX_i)^2=8989-\dfrac{221^2}{6}

=848.833333=848.833333

SSYY=i=1nYi21n(i=1nYi)2=792721126SS_{YY}=\displaystyle\sum_{i=1}^nY_i^2-\dfrac{1}{n}(\displaystyle\sum_{i=1}^nY_i)^2=7927-\dfrac{211^2}{6}

=506.833333=506.833333

SSXY=i=1nXiYi1n(i=1nXi)(i=1nYi)SS_{XY}=\displaystyle\sum_{i=1}^nX_iY_i-\dfrac{1}{n}(\displaystyle\sum_{i=1}^nX_i)(\displaystyle\sum_{i=1}^nY_i)

=8411221(211)6=639.666667=8411-\dfrac{221(211)}{6}=639.666667

Correlation coefficient:


r=SSXYSSXXSSYYr=\dfrac{SS_{XY}}{\sqrt{SS_{XX}}\sqrt{SS_{YY}}}

=639.666667848.833333506.8333330.974474=\dfrac{639.666667}{\sqrt{848.833333}\sqrt{506.833333}}\approx 0.974474



The value of rr is consistent with what we expected in parts a and b.

r=0.974474,r=0.974474, strong positive correlation.


d. The following null and alternative hypotheses need to be tested:

H0:ρ=0H_0:\rho=0

H1:ρ0H_1:\rho\not=0

where ρ\rho corresponds to the population correlation.

The sample size is n=6,n = 6, so then the number of degrees of freedom is df=n2=62=4.df = n-2 = 6 - 2 = 4.

The corresponding critical correlation value rcr_c for a significance level of α=0.05,\alpha = 0.05, for a two-tailed test is:


t=rn21r2t=r\sqrt{\dfrac{n-2}{1-r^2}}

=0.974474621(0.974474)2=0.974474\sqrt{\dfrac{6-2}{1-(0.974474)^2}}

The p-value for two-tailed, df=4df=4 degrees of freedom, t=8.681268,t=8.681268, is computed as follows:


p=P(t>8.681286)p=P(|t|>8.681286)

=0.000969=0.000969

Since we have that p=0.000969<0.05=α,p = 0.000969 < 0.05=\alpha, it is concluded that the null hypothesis H0H_0 is rejected.

Therefore, based on the sample correlation provided, it is concluded that there is enough evidence to claim that the population correlation ρ\rho is different than 0,0, at the α=0.05\alpha = 0.05 significance level.


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