# you use a line of best fit for a set of data to make a prediction about an unknown value. The correlation coefficient for your data set is -0,993. How confident can you be that your predicted value will be reasonably close to the actual value?

a. I can't be confident at all; this is about as close to a random guess as you can get
b. I can be a little confident; it might be close, or it may be way off
c. I can be very confident; it will be close, but it probably won't be exact
d. I can be certain that my predicted value will match the actual value exactly*****

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## Im in SCCA and doing lesson 8 unit 3, the correct answers are under. I got a 100% :)

1. Which type of correlation is suggested by the scatter plot? B. - Negative correlation
2. Which type of correlation is suggested by the scatter plot? D. - No correlation
3. What is the correlation coefficient for the set of data? Round your answer to the nearest thousandth. B. - 0.989
4. About how tall would you expect one of these trees to be after 22 years? C. - 44.25 ft.
5. You use a line of best fit for a set of data to make a prediction about an unknown value. The correlation coefficient for your data set is -0.993. How confident can you be that your predicted value will be reasonably close to the actual value? C. - I can be very confident; it will be close, but it probably won't be exact.

Have a great day:)

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## The correlation coefficient is a measure of the strength and direction of the relationship between two variables. In this case, the correlation coefficient is -0.993, indicating a strong negative correlation between the data set.

To determine how confident you can be about the predicted value being reasonably close to the actual value, you need to consider the relationship between the correlation coefficient and the line of best fit.

A correlation coefficient of -0.993 suggests a very strong negative linear relationship between the variables. This means that as one variable increases, the other variable decreases in a predictable manner.

Using a line of best fit, you can estimate the unknown value based on the known data points. Although the predicted value may not be exact, it is likely to be close to the actual value, given the strong negative correlation.

c. I can be very confident; it will be close, but it probably won't be exact.

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A,B,C,D,C

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