Answer:
Residual = -2
The negative residual value indicates that the data point lies below the regression line.
Step-by-step explanation:
We are given a linear regression model that relates daily high temperature, in degrees Fahrenheit and number of lemonade cups sold.

Where y is the number of cups sold and x is the daily temperature in Fahrenheit.
Residual value:
A residual value basically shows the position of a data point with respect to the regression line.
A residual value of 0 is desired which means that the regression line best fits the data.
The Residual value is calculated by
Residual = Observed value - Predicted value
The predicted value of number of lemonade cups is obtained as

So the predicted value of number of lemonade cups is 23 and the observed value is 21 so the residual value is
Residual = Observed value - Predicted value
Residual = 21 - 23
Residual = -2
The negative residual value indicates that the data point lies below the regression line.
Answer:
i hope this helps
Step-by-step explanation:
The answer would be A, or "A box-and-whisker plot. The number line goes from 0 to 12, and the box ranges from 4 to 10. A line divides the box at 7.
Step-by-step explanation:
If you plot the data (2, 4, 6, 8, and 12) it looks something like the poor box-and-whisker plot below
⊕← | Ф | →⊕
|---|---|---|---|---|---|---|---|---|---|---|---|
0 1 2 3 4 5 6 7 8 9 10 11 12
Description A describes that exact box-and-whisker plot.
It is given in the question that,
A flower garden has 12 sunflowers for every 45 irises.
And we have to find the unit rate for the number of irises per sunflower.
And for that we have to divide number of Irises by number of Sunflower. That is

And that's the required unit rate .
Answer:
12
Step-by-step explanation:
You just find the total weight and divide it by the number of weights