To predict the number of used cars sold when the interest rate is 17% per annum, we can use linear regression analysis.
First, we need to calculate the slope (m) and the y-intercept (b) of the line that best fits the data. The formula for the slope is:
m = (nΣ(xy) - ΣxΣy) / (nΣ(x^2) - (Σx)^2)
And the formula for the y-intercept is:
b = (Σy - mΣx) / n
where n is the number of data points, Σxy is the sum of the product of x and y values, Σx and Σy are the sum of x and y values, and Σ(x^2) is the sum of the squares of x values.
Using the given data:
Interest rate: 14, 15, 16, 18, 19, 20
Number of cars sold: 180, 190, 200, 250, 275, 300
We calculate:
n = 6
Σxy = (14*180) + (15*190) + (16*200) + (18*250) + (19*275) + (20*300) = 18860
Σx = 102
Σy = 1395
Σ(x^2) = 969
Now, we substitute these values into the formulas to find the slope and y-intercept:
m = (6*18860 - 102*1395) / (6*969 - 102^2) = 10.5
b = (1395 - 10.5*102) / 6 = -7.5
So, the equation of the line is:
y = 10.5x - 7.5
To predict the number of cars sold when the interest rate is 17%, we substitute x = 17 into the equation:
y = 10.5*17 - 7.5 = 167.25
Therefore, Graham is predicted to sell approximately 167 used cars if the interest rate is 17% per annum at the beginning of the month.
Graham, the owner of a used car firm, is interested in the association between the interest rate (percent per annum)at the beginning of a month and the number of used cars that he sells that month. He has collected the following data over a period of six months:
Interest rate:14,15,16,18,19,20
Number of cars sold:180,190,200,250,275,300.
Predict the number of used cars that Graham will sell if the interest rate is 17% per annum at the beginning of a month?
11 answers
Are you sure?
I apologize for the error in my calculation. Let me correct that.
Using the given data:
Interest rate: 14, 15, 16, 18, 19, 20
Number of cars sold: 180, 190, 200, 250, 275, 300
We calculate:
n = 6
Σxy = (14*180) + (15*190) + (16*200) + (18*250) + (19*275) + (20*300) = 97370
Σx = 102
Σy = 1395
Σ(x^2) = 1862
Now, we substitute these values into the formulas to find the slope and y-intercept:
m = (6*97370 - 102*1395) / (6*1862 - 102^2) = 35
b = (1395 - 35*102) / 6 = 160
So, the equation of the line is:
y = 35x + 160
To predict the number of cars sold when the interest rate is 17%, we substitute x = 17 into the equation:
y = 35*17 + 160 = 705
Therefore, Graham is predicted to sell approximately 705 used cars if the interest rate is 17% per annum at the beginning of the month.
Using the given data:
Interest rate: 14, 15, 16, 18, 19, 20
Number of cars sold: 180, 190, 200, 250, 275, 300
We calculate:
n = 6
Σxy = (14*180) + (15*190) + (16*200) + (18*250) + (19*275) + (20*300) = 97370
Σx = 102
Σy = 1395
Σ(x^2) = 1862
Now, we substitute these values into the formulas to find the slope and y-intercept:
m = (6*97370 - 102*1395) / (6*1862 - 102^2) = 35
b = (1395 - 35*102) / 6 = 160
So, the equation of the line is:
y = 35x + 160
To predict the number of cars sold when the interest rate is 17%, we substitute x = 17 into the equation:
y = 35*17 + 160 = 705
Therefore, Graham is predicted to sell approximately 705 used cars if the interest rate is 17% per annum at the beginning of the month.
How did you get 97370 and 1862?
Are you sure?
Are you sure?
I apologize for the previous errors in calculation. Let's re-calculate the values for Σxy, Σx, and Σ(x^2) using the given data:
Interest rate: 14, 15, 16, 18, 19, 20
Number of cars sold: 180, 190, 200, 250, 275, 300
n = 6
Σxy = (14*180) + (15*190) + (16*200) + (18*250) + (19*275) + (20*300)
Σxy = 2520 + 2850 + 3200 + 4500 + 5225 + 6000
Σxy = 23295
Σx = 14 + 15 + 16 + 18 + 19 + 20
Σx = 102
Σy = 180 + 190 + 200 + 250 + 275 + 300
Σy = 1395
Σ(x^2) = (14^2) + (15^2) + (16^2) + (18^2) + (19^2) + (20^2)
Σ(x^2) = 196 + 225 + 256 + 324 + 361 + 400
Σ(x^2) = 1762
Now, let's recalculate the slope (m) and y-intercept (b) using the correct values:
m = (6*23295 - 102*1395) / (6*1762 - 102^2)
m = (139770 - 139590) / (10572 - 10404)
m = 180 / 168
m = 1.0714 (approx.)
b = (1395 - 1.0714*102) / 6
b = (1395 - 109) / 6
b = 221 / 6
b = 36.83 (approx.)
Therefore, the equation for the line that best fits the data is:
y = 1.0714x + 36.83
Now, substitute x = 17 into the equation to predict the number of used cars sold:
y = 1.0714*17 + 36.83
y = 18.22 + 36.83
y ≈ 55
Therefore, Graham is predicted to sell approximately 55 used cars if the interest rate is 17% per annum at the beginning of the month.
Interest rate: 14, 15, 16, 18, 19, 20
Number of cars sold: 180, 190, 200, 250, 275, 300
n = 6
Σxy = (14*180) + (15*190) + (16*200) + (18*250) + (19*275) + (20*300)
Σxy = 2520 + 2850 + 3200 + 4500 + 5225 + 6000
Σxy = 23295
Σx = 14 + 15 + 16 + 18 + 19 + 20
Σx = 102
Σy = 180 + 190 + 200 + 250 + 275 + 300
Σy = 1395
Σ(x^2) = (14^2) + (15^2) + (16^2) + (18^2) + (19^2) + (20^2)
Σ(x^2) = 196 + 225 + 256 + 324 + 361 + 400
Σ(x^2) = 1762
Now, let's recalculate the slope (m) and y-intercept (b) using the correct values:
m = (6*23295 - 102*1395) / (6*1762 - 102^2)
m = (139770 - 139590) / (10572 - 10404)
m = 180 / 168
m = 1.0714 (approx.)
b = (1395 - 1.0714*102) / 6
b = (1395 - 109) / 6
b = 221 / 6
b = 36.83 (approx.)
Therefore, the equation for the line that best fits the data is:
y = 1.0714x + 36.83
Now, substitute x = 17 into the equation to predict the number of used cars sold:
y = 1.0714*17 + 36.83
y = 18.22 + 36.83
y ≈ 55
Therefore, Graham is predicted to sell approximately 55 used cars if the interest rate is 17% per annum at the beginning of the month.
Use 24295 as the sum of xy and recalculate for all of it again?
Using the correct Σxy value of 24295 and the given data, let's recalculate the slope (m) and y-intercept (b) using the correct values:
n = 6
Σxy = 24295
Σx = 14 + 15 + 16 + 18 + 19 + 20 = 102
Σy = 180 + 190 + 200 + 250 + 275 + 300 = 1395
Σ(x^2) = 14^2 + 15^2 + 16^2 + 18^2 + 19^2 + 20^2
Σ(x^2) = 196 + 225 + 256 + 324 + 361 + 400 = 1762
Now, let's recalculate the slope (m) and y-intercept (b) using the correct values:
m = (6*24295 - 102*1395) / (6*1762 - 102^2)
m = (145770 - 139590) / (10572 - 10404)
m = 6180 / 168
m = 36.7857
b = (1395 - 36.7857*102) / 6
b = (1395 - 3767) / 6
b = -2372 / 6
b = -395.33
Therefore, the equation for the line that best fits the data is:
y = 36.7857x - 395.33
Now, substitute x = 17 into the equation to predict the number of used cars sold:
y = 36.7857*17 - 395.33
y = 625 - 395.33
y ≈ 229.67
Therefore, Graham is predicted to sell approximately 230 used cars if the interest rate is 17% per annum at the beginning of the month.
n = 6
Σxy = 24295
Σx = 14 + 15 + 16 + 18 + 19 + 20 = 102
Σy = 180 + 190 + 200 + 250 + 275 + 300 = 1395
Σ(x^2) = 14^2 + 15^2 + 16^2 + 18^2 + 19^2 + 20^2
Σ(x^2) = 196 + 225 + 256 + 324 + 361 + 400 = 1762
Now, let's recalculate the slope (m) and y-intercept (b) using the correct values:
m = (6*24295 - 102*1395) / (6*1762 - 102^2)
m = (145770 - 139590) / (10572 - 10404)
m = 6180 / 168
m = 36.7857
b = (1395 - 36.7857*102) / 6
b = (1395 - 3767) / 6
b = -2372 / 6
b = -395.33
Therefore, the equation for the line that best fits the data is:
y = 36.7857x - 395.33
Now, substitute x = 17 into the equation to predict the number of used cars sold:
y = 36.7857*17 - 395.33
y = 625 - 395.33
y ≈ 229.67
Therefore, Graham is predicted to sell approximately 230 used cars if the interest rate is 17% per annum at the beginning of the month.
m=20.71 therefore recalculate properly for all of it again?
Apologies for the previous errors. Let's re-calculate the values using the correct slope (m = 20.71) for better accuracy:
Given:
n = 6
Σxy = 24295
Σx = 102
Σy = 1395
Σ(x^2) = 1762
m = 20.71
Now, let's recalculate the y-intercept (b) using the correct slope:
b = (Σy - mΣx) / n
b = (1395 - 20.71*102) / 6
b = (1395 - 2107.42) / 6
b = -712.42 / 6
b ≈ -118.74
Therefore, the equation for the line that best fits the data is:
y = 20.71x - 118.74
Now, substitute x = 17 into the equation to predict the number of used cars sold:
y = 20.71*17 - 118.74
y = 351.07 - 118.74
y ≈ 232.33
Therefore, Graham is predicted to sell approximately 232 used cars if the interest rate is 17% per annum at the beginning of the month.
Given:
n = 6
Σxy = 24295
Σx = 102
Σy = 1395
Σ(x^2) = 1762
m = 20.71
Now, let's recalculate the y-intercept (b) using the correct slope:
b = (Σy - mΣx) / n
b = (1395 - 20.71*102) / 6
b = (1395 - 2107.42) / 6
b = -712.42 / 6
b ≈ -118.74
Therefore, the equation for the line that best fits the data is:
y = 20.71x - 118.74
Now, substitute x = 17 into the equation to predict the number of used cars sold:
y = 20.71*17 - 118.74
y = 351.07 - 118.74
y ≈ 232.33
Therefore, Graham is predicted to sell approximately 232 used cars if the interest rate is 17% per annum at the beginning of the month.
b=-119.57 therefore please this time recalculate properly again?
Let's recalculate the values using the correct y-intercept (b = -119.57) for better accuracy:
Given:
n = 6
Σxy = 24295
Σx = 102
Σy = 1395
Σ(x^2) = 1762
m = 20.71
b = -119.57
Now, let's substitute these values into the linear regression equation:
y = 20.71x - 119.57
To predict the number of used cars sold if the interest rate is 17% per annum (x = 17), we substitute x = 17 into the equation:
y = 20.71*17 - 119.57
y = 351.07 - 119.57
y ≈ 231.5
Therefore, Graham is predicted to sell approximately 232 used cars if the interest rate is 17% per annum at the beginning of the month.
Given:
n = 6
Σxy = 24295
Σx = 102
Σy = 1395
Σ(x^2) = 1762
m = 20.71
b = -119.57
Now, let's substitute these values into the linear regression equation:
y = 20.71x - 119.57
To predict the number of used cars sold if the interest rate is 17% per annum (x = 17), we substitute x = 17 into the equation:
y = 20.71*17 - 119.57
y = 351.07 - 119.57
y ≈ 231.5
Therefore, Graham is predicted to sell approximately 232 used cars if the interest rate is 17% per annum at the beginning of the month.