## Kiley gathered the data in the table. She found the approximate line of best fit to be y = 1.6x – 4. A 2-column table with 5 rows. The fir

Question

Kiley gathered the data in the table. She found the approximate line of best fit to be y = 1.6x – 4. A 2-column table with 5 rows. The first column is labeled x with entries 0, 2, 3, 5, 6. The second column is labeled y with entries negative 3, negative 1, negative 1, 5, 6. What is the residual value when x = 3? –1.8 –0.2 0.2 1.8

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Math
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2021-09-08T22:15:22+00:00
2021-09-08T22:15:22+00:00 2 Answers
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## Answers ( )

Answer:–1.8

Step-by-step explanation:Given the following table:

x y

0 -3

2 -1

3 -1

5 5

6 6

We are also given y = 1.6x – 4

Therefore, when x = 3, we have

Predicted y = 1.6(3) – 4 = 0.8

Since the observed y = – 1 when x = 3 on the table, the residual value can be estimated as follows:

Residual value = Observed value of y – Predicted value of y = -1 – 0.8 = – 1.8.

Therefore, the residual value when x = 3 is –1.8.

Answer:The residual value is -1.8 when x = 3

Step-by-step explanation:We are given the following table

x | y

0 | -3

2 | -1

3 | -1

5 | 5

6 | 6

Residual value:A residual value basically shows the position of a data point with respect to the line of best fit.

The residual value is calculated as,

Residual value = Observed value – Predicted value

Where observed values are already given in the question and the predicted values are calculated by using the equation of line of best fit.

When we substitute x = 3 in the above equation then we would get the predicted value.

So the predicted value is 0.8

From the given table, the observed value corresponding to x = 3 is -1

So the residual value is,

Residual value = Observed value – Predicted value

Residual value = -1 – 0.8

Residual value =

-1.8Therefore, the residual value is -1.8 when x = 3

Note:A residual value closer to 0 is desired which means that the regression line best fits the data.