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Sagot :
Alright, let's break down the solution based on the given data and regression equation step by step.
### (a) Mean Relationship
First, we need to find the mean values of current stock prices ([tex]\( y \)[/tex]) and earnings per share ([tex]\( x \)[/tex]) based on the given data.
The mean current stock price, [tex]\( \bar{y} \)[/tex], is calculated using the sum of all current stock prices divided by the number of data points.
Similarly, the mean earnings per share, [tex]\( \bar{x} \)[/tex], is calculated using the sum of all earnings per share divided by the number of data points.
The calculated mean values are:
- Mean current stock price, [tex]\( \bar{y} = 1.44875 \)[/tex] dollars
- Mean earnings per share, [tex]\( \bar{x} = 36.98875 \)[/tex] dollars
Based on these values, we can state the relationship:
- Current stock prices that are greater than the mean current stock price ([tex]\(1.44875\)[/tex] dollars) tend to be paired with values for earnings per share that are greater than the mean value for earnings per share ([tex]\(36.98875\)[/tex] dollars).
This conclusion derives from the assumption that if [tex]\(y\)[/tex] is greater than its mean, using the positive trend suggested by the regression line, [tex]\(x\)[/tex] will generally also be above its mean.
### (b) Slope Interpretation
The regression equation given is:
[tex]\[ \hat{y} = -0.236 + 0.046x \][/tex]
In a regression equation of the form [tex]\( \hat{y} = a + bx \)[/tex]:
- [tex]\( a \)[/tex] is the intercept.
- [tex]\( b \)[/tex] is the slope.
The slope [tex]\( b = 0.046 \)[/tex] indicates the amount by which [tex]\( y \)[/tex] (the current stock price) changes for a one-dollar change in [tex]\( x \)[/tex] (earnings per share).
Thus, according to the regression equation:
- For an increase of one dollar in earnings per share, there is a corresponding increase of [tex]\( 0.046 \)[/tex] dollars in the current stock price.
### Conclusion
(a) Current stock prices greater than the mean of the current stock prices tend to be paired with values for earnings per share that are greater than the mean of the values for earnings per share.
(b) According to the regression equation, for an increase of one dollar in earnings per share, there is a corresponding increase of 0.046 dollars in the current stock price.
### (a) Mean Relationship
First, we need to find the mean values of current stock prices ([tex]\( y \)[/tex]) and earnings per share ([tex]\( x \)[/tex]) based on the given data.
The mean current stock price, [tex]\( \bar{y} \)[/tex], is calculated using the sum of all current stock prices divided by the number of data points.
Similarly, the mean earnings per share, [tex]\( \bar{x} \)[/tex], is calculated using the sum of all earnings per share divided by the number of data points.
The calculated mean values are:
- Mean current stock price, [tex]\( \bar{y} = 1.44875 \)[/tex] dollars
- Mean earnings per share, [tex]\( \bar{x} = 36.98875 \)[/tex] dollars
Based on these values, we can state the relationship:
- Current stock prices that are greater than the mean current stock price ([tex]\(1.44875\)[/tex] dollars) tend to be paired with values for earnings per share that are greater than the mean value for earnings per share ([tex]\(36.98875\)[/tex] dollars).
This conclusion derives from the assumption that if [tex]\(y\)[/tex] is greater than its mean, using the positive trend suggested by the regression line, [tex]\(x\)[/tex] will generally also be above its mean.
### (b) Slope Interpretation
The regression equation given is:
[tex]\[ \hat{y} = -0.236 + 0.046x \][/tex]
In a regression equation of the form [tex]\( \hat{y} = a + bx \)[/tex]:
- [tex]\( a \)[/tex] is the intercept.
- [tex]\( b \)[/tex] is the slope.
The slope [tex]\( b = 0.046 \)[/tex] indicates the amount by which [tex]\( y \)[/tex] (the current stock price) changes for a one-dollar change in [tex]\( x \)[/tex] (earnings per share).
Thus, according to the regression equation:
- For an increase of one dollar in earnings per share, there is a corresponding increase of [tex]\( 0.046 \)[/tex] dollars in the current stock price.
### Conclusion
(a) Current stock prices greater than the mean of the current stock prices tend to be paired with values for earnings per share that are greater than the mean of the values for earnings per share.
(b) According to the regression equation, for an increase of one dollar in earnings per share, there is a corresponding increase of 0.046 dollars in the current stock price.
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