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the annual profits for a company are given in the following table, wher…

Question

the annual profits for a company are given in the following table, where x represents the number of years since 2002, and y represents the profit in thousands of dollars. write the linear regression equation that represents this set of data, rounding all coefficients to the nearest hundredth. using this equation, find the projected profit (in thousands of dollars) for 2010, rounded to the nearest thousand dollars.

years since 2002 (x)profits (y) (in thousands of dollars)
1160
2157
3192
4192
5227

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answer attempt 1 out of 3

regression equation:

final answer: thousand dollars

Explanation:

Step1: Calculate sums

Let \(n = 6\) (number of data - points).
\(\sum_{i = 1}^{n}x_i=0 + 1+2 + 3+4 + 5=15\)
\(\sum_{i = 1}^{n}y_i=135 + 160+157 + 192+192+227 = 1063\)
\(\sum_{i = 1}^{n}x_i^2=0^2 + 1^2+2^2 + 3^2+4^2 + 5^2=0 + 1+4 + 9+16 + 25 = 55\)
\(\sum_{i = 1}^{n}x_iy_i=0\times135+1\times160 + 2\times157+3\times192+4\times192+5\times227=0 + 160+314+576+768+1135 = 2953\)

Step2: Calculate slope \(m\)

The formula for the slope \(m\) of the regression line \(y=mx + b\) is \(m=\frac{n\sum_{i = 1}^{n}x_iy_i-\sum_{i = 1}^{n}x_i\sum_{i = 1}^{n}y_i}{n\sum_{i = 1}^{n}x_i^2-(\sum_{i = 1}^{n}x_i)^2}\)
\[

$$\begin{align*} m&=\frac{6\times2953-15\times1063}{6\times55 - 15^2}\\ &=\frac{17718-15945}{330 - 225}\\ &=\frac{1773}{105}\\ &\approx16.89 \end{align*}$$

\]

Step3: Calculate y - intercept \(b\)

The formula for the y - intercept \(b\) is \(b=\frac{\sum_{i = 1}^{n}y_i-m\sum_{i = 1}^{n}x_i}{n}\)
\[

$$\begin{align*} b&=\frac{1063-16.89\times15}{6}\\ &=\frac{1063 - 253.35}{6}\\ &=\frac{809.65}{6}\\ &\approx134.94 \end{align*}$$

\]

The regression equation is \(y = 16.89x+134.94\)

Step4: Find \(x\) for 2010

Since \(x\) represents the number of years since 2002, for 2010, \(x = 2010 - 2002=8\)

Step5: Calculate projected profit

Substitute \(x = 8\) into the regression equation \(y=16.89\times8 + 134.94\)
\[

$$\begin{align*} y&=135.12+134.94\\ &=270.06\approx270 \end{align*}$$

\]

Answer:

Regression Equation: \(y = 16.89x+134.94\)
Final Answer: 270