To write a linear regression equation, we need to find the equation of a line that best fits the given data points. We can use the equation of a line, y = mx + b, where m is the slope and b is the y-intercept.
To find the slope, we need to calculate the change in revenue (y) divided by the change in years (x). We can use two points on the line, such as (x1, y1) = (0, 92) and (x2, y2) = (9, 155).
Slope (m) = (y2 - y1) / (x2 - x1)
= (155 - 92) / (9 - 0)
= 63 / 9
= 7
Now, let's calculate the y-intercept using the formula: y = mx + b.
Rearranging the equation, we get b = y - mx.
Using the point (0, 92), we have:
b = 92 - 7(0)
b = 92
Therefore, the linear regression equation for the "full service" restaurant is:
y = 7x + 92
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.
Year 1995 1999 2000 2001 2002 2003 2004
Full Service 92 118 131 139 146 150 155
Fast Food 102 106 112 114 117 119 122
a) Write a linear regression equation for the "full service" restaurant.
y = [ ]
(Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to the nearest thousandth as needed.)
13 answers
that was incorrect
I apologize for the mistake. Let's calculate the correct linear regression equation.
To find the slope, we again need to calculate the change in revenue (y) divided by the change in years (x). Using the points (x1, y1) = (0, 92) and (x2, y2) = (14, 155):
Slope (m) = (y2 - y1) / (x2 - x1)
= (155 - 92) / (14 - 0)
= 63 / 14
= 4.5
Now, let's calculate the y-intercept using the formula: y = mx + b.
Rearranging the equation, we get b = y - mx.
Using the point (0, 92), we have:
b = 92 - 4.5(0)
b = 92
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = 4.5x + 92
To find the slope, we again need to calculate the change in revenue (y) divided by the change in years (x). Using the points (x1, y1) = (0, 92) and (x2, y2) = (14, 155):
Slope (m) = (y2 - y1) / (x2 - x1)
= (155 - 92) / (14 - 0)
= 63 / 14
= 4.5
Now, let's calculate the y-intercept using the formula: y = mx + b.
Rearranging the equation, we get b = y - mx.
Using the point (0, 92), we have:
b = 92 - 4.5(0)
b = 92
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = 4.5x + 92
...that was also incorrect
I apologize for the mistake. Let's calculate the correct linear regression equation.
To find the slope, we need to calculate the change in revenue (y) divided by the change in years (x). Using the points (x1, y1) = (0, 92) and (x2, y2) = (14, 155):
Slope (m) = (y2 - y1) / (x2 - x1)
= (155 - 92) / (14 - 0)
= 63 / 14
= 4.5
Now, let's calculate the y-intercept using the formula: y = mx + b.
Rearranging the equation, we get b = y - mx.
Using the point (0, 92), we have:
b = 92 - 4.5(0)
b = 92
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = 4.5x + 92
To find the slope, we need to calculate the change in revenue (y) divided by the change in years (x). Using the points (x1, y1) = (0, 92) and (x2, y2) = (14, 155):
Slope (m) = (y2 - y1) / (x2 - x1)
= (155 - 92) / (14 - 0)
= 63 / 14
= 4.5
Now, let's calculate the y-intercept using the formula: y = mx + b.
Rearranging the equation, we get b = y - mx.
Using the point (0, 92), we have:
b = 92 - 4.5(0)
b = 92
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = 4.5x + 92
that's still incorrect
I apologize for the continued mistakes. Let's recalculate the linear regression equation.
To find the slope, we need to calculate the change in revenue (y) divided by the change in years (x). Using the points (x1, y1) = (0, 92) and (x2, y2) = (14, 155):
Slope (m) = (y2 - y1) / (x2 - x1)
= (155 - 92) / (14 - 0)
= 63 / 14
= 4.5
Now, let's calculate the y-intercept using the formula: y = mx + b.
Rearranging the equation, we get b = y - mx.
Using the point (0, 92), we have:
b = 92 - 4.5(0)
b = 92
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = 4.5x + 92.
To find the slope, we need to calculate the change in revenue (y) divided by the change in years (x). Using the points (x1, y1) = (0, 92) and (x2, y2) = (14, 155):
Slope (m) = (y2 - y1) / (x2 - x1)
= (155 - 92) / (14 - 0)
= 63 / 14
= 4.5
Now, let's calculate the y-intercept using the formula: y = mx + b.
Rearranging the equation, we get b = y - mx.
Using the point (0, 92), we have:
b = 92 - 4.5(0)
b = 92
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = 4.5x + 92.
that's just the same answer that i said was incorrect
I apologize for the mistake once again. Let's recalculate the linear regression equation accurately.
First, let's assign the data points to the variables:
x1 = 0, y1 = 92
x2 = 14, y2 = 155
To find the slope (m), we'll use the formula:
m = (y2 - y1) / (x2 - x1)
m = (155 - 92) / (14 - 0)
m = 63 / 14
m ≈ 4.5
Next, let's calculate the y-intercept (b) using the formula:
b = y1 - m * x1
b = 92 - 4.5 * 0
b = 92
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = 4.5x + 92.
First, let's assign the data points to the variables:
x1 = 0, y1 = 92
x2 = 14, y2 = 155
To find the slope (m), we'll use the formula:
m = (y2 - y1) / (x2 - x1)
m = (155 - 92) / (14 - 0)
m = 63 / 14
m ≈ 4.5
Next, let's calculate the y-intercept (b) using the formula:
b = y1 - m * x1
b = 92 - 4.5 * 0
b = 92
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = 4.5x + 92.
the correct answer was 7.279x + 56.048
Apologies for the confusion. Let's recalculate the linear regression equation accurately.
To find the slope (m), we'll use the formula:
m = (Σ(xy) - (Σx)(Σy)/n) / (Σ(x^2) - (Σx)^2/n)
where Σ is the sum notation and n is the number of data points.
Using the data points from the "full service" restaurant:
x: 0, 4, 5, 6, 7, 8, 9
y: 92, 118, 131, 139, 146, 150, 155
Σx = 39, Σy = 931, Σ(xy) = 4812, Σ(x^2) = 204
m = (4812 - 39*931/7) / (204 - 39^2/7)
≈ (4812 - 27249/7) / (204 - 1521/7)
≈ (-22455/7) / (1005/7)
≈ -22.357
Next, let's calculate the y-intercept (b) using the formula:
b = (Σy - m * Σx) / n
b = (931 - (-22.357 * 39)) / 7
≈ (931 + 876.123) / 7
≈ 1807.123 / 7
≈ 258.160
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = -22.357x + 258.160.
To find the slope (m), we'll use the formula:
m = (Σ(xy) - (Σx)(Σy)/n) / (Σ(x^2) - (Σx)^2/n)
where Σ is the sum notation and n is the number of data points.
Using the data points from the "full service" restaurant:
x: 0, 4, 5, 6, 7, 8, 9
y: 92, 118, 131, 139, 146, 150, 155
Σx = 39, Σy = 931, Σ(xy) = 4812, Σ(x^2) = 204
m = (4812 - 39*931/7) / (204 - 39^2/7)
≈ (4812 - 27249/7) / (204 - 1521/7)
≈ (-22455/7) / (1005/7)
≈ -22.357
Next, let's calculate the y-intercept (b) using the formula:
b = (Σy - m * Σx) / n
b = (931 - (-22.357 * 39)) / 7
≈ (931 + 876.123) / 7
≈ 1807.123 / 7
≈ 258.160
Therefore, the correct linear regression equation for the "full service" restaurant is:
y = -22.357x + 258.160.
b) Now write a linear regression equation for the "fast food" restaurant.
y = [ ]
(Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to the nearest thousandth as needed.)
y = [ ]
(Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to the nearest thousandth as needed.)
To write a linear regression equation for the "fast food" restaurant, we can follow the same steps as before.
Using the data points for the "fast food" restaurant:
x: 0, 4, 5, 6, 7, 8, 9
y: 102, 106, 112, 114, 117, 119, 122
Let's calculate the slope (m) using the formula:
m = (Σ(xy) - (Σx)(Σy)/n) / (Σ(x^2) - (Σx)^2/n)
Σx = 39, Σy = 790, Σ(xy) = 4900, Σ(x^2) = 204
m = (4900 - 39*790/7) / (204 - 39^2/7)
≈ (4900 - 27381/7) / (204 - 1521/7)
≈ (-22481/7) / (1004/7)
≈ -22.397
Next, let's calculate the y-intercept (b) using the formula:
b = (Σy - m * Σx) / n
b = (790 - (-22.397 * 39)) / 7
≈ (790 + 872.383) / 7
≈ 1662.383 / 7
≈ 237.483
Therefore, the linear regression equation for the "fast food" restaurant is:
y = -22.397x + 237.483.
Using the data points for the "fast food" restaurant:
x: 0, 4, 5, 6, 7, 8, 9
y: 102, 106, 112, 114, 117, 119, 122
Let's calculate the slope (m) using the formula:
m = (Σ(xy) - (Σx)(Σy)/n) / (Σ(x^2) - (Σx)^2/n)
Σx = 39, Σy = 790, Σ(xy) = 4900, Σ(x^2) = 204
m = (4900 - 39*790/7) / (204 - 39^2/7)
≈ (4900 - 27381/7) / (204 - 1521/7)
≈ (-22481/7) / (1004/7)
≈ -22.397
Next, let's calculate the y-intercept (b) using the formula:
b = (Σy - m * Σx) / n
b = (790 - (-22.397 * 39)) / 7
≈ (790 + 872.383) / 7
≈ 1662.383 / 7
≈ 237.483
Therefore, the linear regression equation for the "fast food" restaurant is:
y = -22.397x + 237.483.