Respuesta :
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.
[tex]y = -34+ \frac{3}{5} x[/tex]
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
[tex]y = -34+ \frac{3}{5} (95)\\\\y = -34+ 3 (19)\\\\y = -34+ 57\\\\y = 23[/tex]
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:
During the summer, Krista noted both the number of customers who came to her lemonade stand each day and how much the temperature rose during the day while her stand was open. Based on the data, she concluded that there is a positive correlation between the number of customers and the increase in temperature. Identify which of the two data tables represents a positive correlation. Then, plot the set of data points from that table.
Table1
Increase in Temperature (°F) Number of Customers
10 10
8 9
11 12
15 17
15 16
16 18
11 11
16 13
14 15
13 14
Table2
Increase in Temperature (°F) Number of Customers
3 10
2 14
4 12
5 13
3 10
3 15
7 8
10 5
11 6
12 8
Step-by-step explanation:
