Answer to Question #267306 in Statistics and Probability for Matilda

Question #267306

A sociologist wants to know whether the number of children in the family is linearly dependent on the age of the mother at her wedding. He interviewed 9 housewives and the results are shown below.


Age at wedding (X)


21


15


22


22


21


25


30


18


24


No. of children (Y)


4


8


3


4


2


3


1


5


6


Find the following:


a.) Estimate the regrision equation


b.) Estimate the number of children at age 27


c.) Compute the sample correlation coefficient r and the sample coefficient of determination r² and interpret the result.


1
Expert's answer
2021-11-17T17:41:32-0500

a)


"\\def\\arraystretch{1.5}\n \\begin{array}{c:c:c:c:c:c}\n & X & Y & XY & X^2 & Y^2\\\\\n \\hline\n & 21 & 4 & 84 & 441 & 16\\\\\n & 15 & 8 & 120 & 225 & 64\\\\\n & 22 & 3 & 66 & 484 & 9\\\\\n & 22 & 4 & 88 & 484 & 16\\\\\n & 21 & 2 & 42 & 441 & 4\\\\\n & 25 & 3 & 75 & 625 & 9\\\\\n & 30 & 1 & 30 & 900 & 1\\\\\n & 18 & 5 & 90 & 324 & 25\\\\\n & 24 & 6 & 144 & 576 & 36\\\\\n Sum= & 198 & 36 & 739 & 4500 & 180\\\\\n\\end{array}""\\sum_iX_i=198, \\sum_iY_i=36"

"\\sum_iX_iY_i=739,\\sum_iX_i^2=4500, \\sum_iY_i^2=180""\\bar{X}=\\dfrac{1}{n}\\sum_iX_i=\\dfrac{198}{9}=22"

"\\bar{Y}=\\dfrac{1}{n}\\sum_iY_i=\\dfrac{36}{9}=4"

"SS_{XX}=\\sum_iX_i^2-\\dfrac{1}{n}(\\sum_iX_i)^2=739-\\dfrac{(198)^2}{9}"

"=144"

"SS_{YY}=\\sum_iY_i^2-\\dfrac{1}{n}(\\sum_iY_i)^2=180-\\dfrac{(36)^2}{9}"

"=36"

"SS_{XY}=\\sum_iX_iY_i-\\dfrac{1}{n}(\\sum_iX_i)(\\sum_iY_i)"

"=739-\\dfrac{198(36)}{9}=-53"

"m=slope=\\dfrac{SS_{XY}}{SS_{XX}}"

"=\\dfrac{-53}{144}=-0.368056"

"n=\\bar{Y}-m\\bar{X}"

"=4-(-0.368056)(22)"

"=12.097232"

Therefore, we find that the regression equation is:


"Y=12.097232-0.368056X"


b)


"Y=12.097232-0.368056(27)=2"

The number of children at age 27 is 2.



c) Correlation coefficient:


"r=\\dfrac{SS_{XY}}{\\sqrt{SS_{XX}}\\sqrt{SS_{YY}}}=\\dfrac{-53}{\\sqrt{144}\\sqrt{36}}"

"=-0.736111"


Negative strong correlation.

"r^2=\\dfrac{(SS_{XY})^2}{SS_{XX}SS_{YY}}=\\dfrac{(-53)^2}{144(36)}"

"=0.541860"

54.186% of the data fit the regression model.


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