You are helping your boss, the owner of a coffee shop, set prices. She has gathered some data by counting the number of cups sold per day at various prices. Your job is to see if there is a relationship between price and sales for one of the two most popular drinks.

Coffee sales:
Price: 2.40 1.50 1.10 1.60 2.50 1.30 1.00 1.70 2.20 2.00
Sales: 64 85 94 83 63 91 96 79 70 73
Price: 1.50 2.20 2.70 2.50 2.90 2.00 1.60 2.10 3.00 1.80
Sales: 96 79 70 73 64 85 94 83 63 91
1. Which drink did you select? Circle one.

CoffeeTea

Find a line of best fit.
2. Enter the data into your calculator and perform a linear regression. Round a and b to the nearest tenth. (2 points: 1 point for slope and 1 point for y-value of y-intercept)
What is your linear regression equation?






3. What is the meaning of the slope? (2 points



4. Complete the table. (10 points: 1 point for each row)
Identify the actual number of sales at each price.
Use your line of best fit to calculate the predicted sales at each price. Round the predicted sales to the nearest tenth.
Calculate the residuals at each price by subtracting predicted sales from actual sales.
Coffee:
Price Actual sales Predicted sales Residual
1.5
1.6
1.8
2.0
2.1
2.2
2.5
2.7
2.9
3.0

Tea:
Price Actual sales Predicted sales Residual
1.0
1.1
1.3
1.5
1.6
1.7
2.0
2.2
2.4
2.5

5. Create a residual plot by plotting the price on the x-axis and the residual on the y-axis. (4 points)


6. Analyze the residual plot. Based on your plot, does your model fit the data well? (2 points)