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Regression and Predictions. Exercises 13鈥28 use the same data sets as Exercises 13鈥28 in Section 10-1. In each case, find the regression equation, letting the first variable be the predictor (x) variable. Find the indicated predicted value by following the prediction procedure summarized in Figure 10-5 on page 493.

Using the diameter/circumference data, find the best predicted circumference of a marble with a diameter of 1.50 cm. How does the result compare to the actual circumference of 4.7 cm?

Short Answer

Expert verified

The regression equation is\(\hat y = - 0.00396 + 3.14x\).

The best predicted circumference of marble with a diameter of 1.50 cm is 4.7 cm.

Step by step solution

01

Given information

Values are given on three variables namely, diameter, circumference, and volume.

02

Calculate the mean values

Let x represent the diameter.

Let y represent thecircumference.

Themean value of xis given as,

\(\begin{array}{c}\bar x = \frac{{\sum\limits_{i = 1}^n {{x_i}} }}{n}\\ = \frac{{7.4 + 23.9 + .... + 9.7}}{8}\\ = 12.375\end{array}\)

Therefore, the mean value of x is 12.375.

Themean value of yis given as,

\(\begin{array}{c}\bar y = \frac{{\sum\limits_{i = 1}^n {{y_i}} }}{n}\\ = \frac{{23.2 + 75.1 + .... + 30.5}}{8}\\ = 38.888\end{array}\)

Therefore, the mean value of y is 38.888.

03

Calculate the standard deviation of x and y

The standard deviation of x is given as,

\(\begin{array}{c}{s_x} = \sqrt {\frac{{\sum\limits_{i = 1}^n {{{({x_i} - \bar x)}^2}} }}{{n - 1}}} \\ = \sqrt {\frac{{{{\left( {7.4 - 12.375} \right)}^2} + {{\left( {23.9 - 12.375} \right)}^2} + ..... + {{\left( {9.7 - 12.375} \right)}^2}}}{{8 - 1}}} \\ = 8.371\end{array}\)

Therefore, the standard deviation of x is 8.371.

The standard deviation of y is given as,

\(\begin{array}{c}{s_y} = \sqrt {\frac{{\sum\limits_{i = 1}^n {{{({y_i} - \bar y)}^2}} }}{{n - 1}}} \\ = \sqrt {\frac{{{{\left( {23.2 - 38.888} \right)}^2} + {{\left( {75.1 - 38.888} \right)}^2} + ..... + {{\left( {30.5 - 38.888} \right)}^2}}}{{8 - 1}}} \\ = 26.307\end{array}\)

Therefore, the standard deviation of y is 26.307.

04

Calculate the correlation coefficient

Thecorrelation coefficient is given as,

\(r = \frac{{n\left( {\sum {xy} } \right) - \left( {\sum x } \right)\left( {\sum y } \right)}}{{\sqrt {\left( {\left( {n\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}} \right)\left( {\left( {n\sum {{y^2}} } \right) - {{\left( {\sum y } \right)}^2}} \right)} }}\)

The calculations required to compute the correlation coefficient are as follows:

The correlation coefficient is given as,

\(\begin{array}{c}r = \frac{{n\left( {\sum {xy} } \right) - \left( {\sum x } \right)\left( {\sum y } \right)}}{{\sqrt {\left( {\left( {n\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}} \right)\left( {\left( {n\sum {{y^2}} } \right) - {{\left( {\sum y } \right)}^2}} \right)} }}\\ = \frac{{8\left( {5391.3} \right) - \left( {99} \right)\left( {311.1} \right)}}{{\sqrt {\left( {\left( {8 \times 1715.6} \right) - {{\left( {99} \right)}^2}} \right)\left( {\left( {8 \times 16942} \right) - {{\left( {311.1} \right)}^2}} \right)} }}\\ = 0.999999\end{array}\)

Therefore, the correlation coefficient is 0.999999.

05

Calculate the slope of the regression line

The slopeof the regressionline is given as,

\(\begin{array}{c}{b_1} = r \times \frac{{{s_Y}}}{{{s_X}}}\\ = 0.999999 \times \frac{{26.307}}{{8.371}}\\ = 3.143\end{array}\)

Therefore, the value of slope is 3.14.

06

Calculate the intercept of the regression line

The interceptis computed as,

\(\begin{array}{c}{b_0} = \bar y - {b_1}\bar x\\ = 38.888 - \left( {3.143 \times 12.375} \right)\\ = - 0.00396\end{array}\)

Therefore, the value of intercept is -0.004.

07

Form a regression equation

Theregression equationis given as,

\(\begin{array}{c}\hat y = {b_0} + {b_1}x\\ = - 0.004 + 3.14x\end{array}\)

Thus, the regression equation is \(\hat y = - 0.00396 + 3.143x\).

08

Analyze the regression model

Referring to exercise 27 of section 10-1,

1)The scatter plot shows a linear relationship between the variables.

2)The P-value is 0.000.

As the P-value is less than the level of significance (0.05), this implies the null hypothesis is rejected.

Therefore, the correlation is significant.

Referring to figure 10-5, the criteria for a good regression model are satisfied.

Therefore, the regression equation can be used to predict the value of y.

The best predicted circumference of marble with a diameter of 1.50 cm is computed as,

\(\begin{array}{c}\hat y = - 0.00396 + \left( {3.14 \times 1.50} \right)\\ = 4.70604\end{array}\)

Therefore, the best predicted circumference of marble with a diameter of 1.50 cm is 4.7 cm.

09

Compare the result with the actual circumference of 4.7 cm

The predicted circumference of marble with a diameter of 1.50 cm is the same as the actual circumference.

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