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Using the diameter/volume data from the preceding exercise, find the best predicted volume of a marble with a diameter of 1.50 cm. How does the result compare to the actual volume of 1.8 cm3?

Short Answer

Expert verified

The regression equation is\(\hat y = - 2010.67 + 347.30x\).

The best predicted volume of marble with a diameter of 1.50 cm is \( - 1489.71\;{\rm{c}}{{\rm{m}}^3}\). The measure of volume cannot be negative, which cannot be compared to the actual volume \(1.8\;{\rm{c}}{{\rm{m}}^3}\).

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 thediameter.

Let y represent thevolume.

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,

\(\begin{array}{c}\bar y = \frac{{\sum\limits_{i = 1}^n {{y_i}} }}{n}\\ = \frac{{212.2 + 7148.1 + .... + 477.9}}{8}\\ = 2287.2\end{array}\)

Therefore, the mean value of y is 2287.2.

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 yis given as,

\(\begin{array}{c}{s_y} = \sqrt {\frac{{\sum\limits_{i = 1}^n {{{({y_i} - \bar y)}^2}} }}{{n - 1}}} \\ = \sqrt {\frac{{{{\left( {212.2 - 2287.2} \right)}^2} + {{\left( {7148.1 - 2287.2} \right)}^2} + ..... + {{\left( {477.9 - 2287.2} \right)}^2}}}{{8 - 1}}} \\ = 2971.994\end{array}\)

Therefore, the standard deviation of y is 2971.994.

04

Calculate the correlation coefficient

The correlation 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,

\(\begin{array}{c}r = \frac{{n\left( {\sum {xy} } \right) - \left( {\sum x } \right)\left( {\sum y } \right)}}{{\sqrt {\left( {\left( {n\sum {{x^2}} } \right) - {{\left( {\sum x } \right)}^2}} \right)\left( {\left( {n\sum {{y^2}} } \right) - {{\left( {\sum y } \right)}^2}} \right)} }}\\ = \frac{{8\left( {396776} \right) - \left( {99} \right)\left( {18297.6} \right)}}{{\sqrt {\left( {\left( {8 \times 1715.6} \right) - {{\left( {99} \right)}^2}} \right)\left( {\left( {8 \times 103679501} \right) - {{\left( {18297.6} \right)}^2}} \right)} }}\\ = 0.9782\end{array}\)

Therefore, the correlation coefficient is 0.9782.

05

Calculate the slope of the regression line

The slopeof the regression line is given as,

\(\begin{array}{c}{b_1} = r\frac{{{s_Y}}}{{{s_X}}}\\ = 0.9782 \times \frac{{2971.994}}{{8.371}}\\ = 347.30\end{array}\)

Therefore, the value of slope is 347.30.

06

Calculate the intercept of the regression line

The interceptis computed as,

\(\begin{array}{c}{b_0} = \bar y - {b_1}\bar x\\ = 2287.2 - \left( {347.30 \times 12.375} \right)\\ = - 2010.67\end{array}\)

Therefore, the value of intercept is -2010.67.

07

Form a regression equation

Theregression equationis given as,

\(\begin{array}{c}\hat y = {b_0} + {b_1}x\\ = - 2010.67 + 347.30x\end{array}\)

Thus, the regression equation is \(\hat y = - 2010.67 + 347.67x\).

08

Analyze the model

Referring to exercise 28 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 good regression model are satisfied.

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

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

\(\begin{array}{c}\hat y = - 2010.67 + \left( {347.30 \times 1.50} \right)\\ = - 1489.71\end{array}\)

Therefore, thebest predicted volume of marble with a diameter of 1.50 cm is

\( - 1489.71\;{\rm{c}}{{\rm{m}}^3}\).

09

Compare the result with the actual volume of 1.8

Since the predicted volume of marble witha diameter of 1.50 cm is negative, this makes no sense.

Therefore, there is no comparison between the predicted and actual volume.

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