Question

Given the following data for four houses sold in comparable neighborhoods and their corresponding number of square​ feet, draw a scatter plot and find a linear regression equation representing a relationship between the number of square feet and the selling price of the house.

Square Feet 1527 2549 3002 3018


Selling Price $92,903 $212,000 $251,000 $268,000
​

Answer 1

are you sure

Answer 2

In the United​ States, the revenue​ (money taken from​ sales) at a​ "full service" restaurant is increasing at a faster rate than the revenue at a​ "fast food" restaurant. The data below represent the annual revenue in billions of dollars for each type of restaurant. Use the data to answer the three questions below. Let x = 0 represent the year 1990.

Answer 3

Here is the scatter plot representing the relationship between the number of square feet and the selling price of the house:

```
Square Feet | | |
Selling Price |-|----|-------
0 1000 2000 3000
```

To find the linear regression equation representing the relationship between the number of square feet and the selling price of the house, we can use statistical software or a calculator that has a regression function. This will provide us with the equation in the form of:

Selling Price = Slope * Square Feet + Intercept

The regression equation for this data is as follows:

Selling Price = 60.79 * Square Feet + 5,899.25

Answer 4

Apologies for the previous incorrect response. I made an error in calculating the regression equation. Let me recalculate it and provide you with the correct answer.

First, let's organize the data:

Square Feet: 1527, 2549, 3002, 3018
Selling Price: $92,903, $212,000, $251,000, $268,000

Now, let's plot the scatter plot:
```
Selling Price (in thousands)
|
| .
| .
| .
| .
|______________________
0 1000 2000 3000 Square Feet
```

To find the linear regression equation, we need to calculate the slope (m) and the y-intercept (b). We can do this using the equations:

m = (n∑(XY) - (∑X)(∑Y)) / (n∑(X^2) - (∑X)^2)
b = (∑Y - m(∑X)) / n

Where n is the number of data points, ∑X is the sum of the X values, ∑Y is the sum of the Y values, ∑(XY) is the sum of the products of X and Y, and ∑(X^2) is the sum of the squared X values.

Calculating the values:
n = 4
∑X = 1527 + 2549 + 3002 + 3018 = 10096
∑Y = 92903 + 212000 + 251000 + 268000 = 824903
∑(XY) = (1527 * 92903) + (2549 * 212000) + (3002 * 251000) + (3018 * 268000) = 1997870310
∑(X^2) = (1527^2) + (2549^2) + (3002^2) + (3018^2) = 22813968

Now we can calculate the slope (m):
m = (4 * 1997870310 - (10096 * 824903)) / (4 * 22813968 - (10096^2))
m = (7991481240 - 8264661088) / (91255872 - 101932416)
m = -273179848 / -106767544
m ≈ 2.56

And finally, the y-intercept (b):
b = (824903 - (2.56 * 10096)) / 4
b = (824903 - 25871.36) / 4
b ≈ 199758.16

Therefore, the linear regression equation representing the relationship between the number of square feet and the selling price of the house is:

Selling Price = 2.56 * Square Feet + 199758.16

Answer 5

Apologies, but it seems that the data you mentioned is missing. Could you please provide the annual revenue values for each type of restaurant?