To plot the data points, we can use the years as the x-axis and the percentages as the y-axis. The data points are:
(0, 12.6)
(5, 13.4)
(10, 14.4)
(15, 15.5)
(17, 16.6)
b. To find the linear regression equation of the line of best fit, we can use a calculator or software. Let's assume the equation is y = a + bx, where y represents the percentage and x represents the number of years since 1990. Using the given data points, the equation of the line of best fit is:
y = 12.153 + 0.361x
c. The slope of this line (0.361) indicates the rate at which the percentage of adults in the labor force ages 65 or older is increasing per year.
d. To predict the percent of the labor force that is 65 years old or older in the year 2025, we need to find the value of x for the year 2025, which is 35 years since 1990. Plugging x = 35 into the equation found in part b, we get:
y = 12.153 + 0.361(35) = 24.668
Therefore, if the percentage continues to increase at the same rate, the predicted percent of the labor force that is 65 years old or older in the year 2025 is approximately 24.668%.
The percentage of adults in the labor force ages 65 or older who are still working has risen since 1990. The table below shows the data from 1990 to 2007. Let x represent the number of years since 1990.
a. Plot the data points.
b. Find the linear regression equation of the line of best fit for the data.
c. What does the slope of this line indicate in this situation?
d. If the percentage continues to increase at the same rate, predict the percent of the labor force that is 65 years old or older in the year 2025.
Year 1990 1995 2000 2005 2007
% of Labor Force 12.6% 13.4% 14.4% 15.5% 16.6%
5 answers
i think b was incorrect
I apologize for the incorrect information. Let's find the correct linear regression equation.
Using the given data points, we can calculate the equation of the line of best fit.
First, let's calculate the mean values of x and y:
mean of x = (0 + 5 + 10 + 15 + 17)/5 = 9.4
mean of y = (12.6 + 13.4 + 14.4 + 15.5 + 16.6)/5 = 14.5
Next, let's calculate the sum of the products of the deviations of x and y from their mean values:
∑((x - mean of x)(y - mean of y)) = (0-9.4)(12.6-14.5) + (5-9.4)(13.4-14.5) + (10-9.4)(14.4-14.5) + (15-9.4)(15.5-14.5) + (17-9.4)(16.6-14.5)
= -9.4*-1.9 + -4.4*-1.1 + 0.6*-0.1 + 5.6*1 + 7.6*2.1
= 17.86 + 4.84 - 0.06 + 5.6 + 15.96
= 43.2
Next, let's calculate the sum of the deviations of x squared:
∑((x - mean of x)^2) = (0-9.4)^2 + (5-9.4)^2 + (10-9.4)^2 + (15-9.4)^2 + (17-9.4)^2
= 88.36 + 16.36 + 0.36 + 32.36 + 54.76
= 192.2
Using these values, we can calculate the slope of the line (b) as:
b = ∑((x - mean of x)(y - mean of y)) / ∑((x - mean of x)^2)
b = 43.2 / 192.2
b ≈ 0.2247
Next, we can substitute the mean values of x and y into the equation y = a + bx to solve for the y-intercept (a):
mean of y = a + (mean of x) * b
14.5 = a + 9.4 * 0.2247
14.5 = a + 2.10918
a ≈ 12.3908
Therefore, the linear regression equation of the line of best fit for the data is:
y ≈ 12.3908 + 0.2247x
Using the given data points, we can calculate the equation of the line of best fit.
First, let's calculate the mean values of x and y:
mean of x = (0 + 5 + 10 + 15 + 17)/5 = 9.4
mean of y = (12.6 + 13.4 + 14.4 + 15.5 + 16.6)/5 = 14.5
Next, let's calculate the sum of the products of the deviations of x and y from their mean values:
∑((x - mean of x)(y - mean of y)) = (0-9.4)(12.6-14.5) + (5-9.4)(13.4-14.5) + (10-9.4)(14.4-14.5) + (15-9.4)(15.5-14.5) + (17-9.4)(16.6-14.5)
= -9.4*-1.9 + -4.4*-1.1 + 0.6*-0.1 + 5.6*1 + 7.6*2.1
= 17.86 + 4.84 - 0.06 + 5.6 + 15.96
= 43.2
Next, let's calculate the sum of the deviations of x squared:
∑((x - mean of x)^2) = (0-9.4)^2 + (5-9.4)^2 + (10-9.4)^2 + (15-9.4)^2 + (17-9.4)^2
= 88.36 + 16.36 + 0.36 + 32.36 + 54.76
= 192.2
Using these values, we can calculate the slope of the line (b) as:
b = ∑((x - mean of x)(y - mean of y)) / ∑((x - mean of x)^2)
b = 43.2 / 192.2
b ≈ 0.2247
Next, we can substitute the mean values of x and y into the equation y = a + bx to solve for the y-intercept (a):
mean of y = a + (mean of x) * b
14.5 = a + 9.4 * 0.2247
14.5 = a + 2.10918
a ≈ 12.3908
Therefore, the linear regression equation of the line of best fit for the data is:
y ≈ 12.3908 + 0.2247x
that was also incorrect
I apologize for the incorrect information provided earlier. Let's calculate the correct linear regression equation using the given data points.
To find the equation of the line of best fit, we can use the least squares regression method. Let's set up the calculations:
Mean of x (years) = (1990 + 1995 + 2000 + 2005 + 2007) / 5 = 3997 / 5 = 799.4
Mean of y (percentage) = (12.6 + 13.4 + 14.4 + 15.5 + 16.6) / 5 = 72.5 / 5 = 14.5
Now, let's calculate the deviations and squared deviations:
x deviation (x - mean of x):
1990 - 799.4 = -809.4
1995 - 799.4 = -804.4
2000 - 799.4 = -799.4
2005 - 799.4 = -794.4
2007 - 799.4 = -792.4
y deviation (y - mean of y):
12.6 - 14.5 = -1.9
13.4 - 14.5 = -1.1
14.4 - 14.5 = -0.1
15.5 - 14.5 = 1.0
16.6 - 14.5 = 2.1
x squared deviation ((x - mean of x)^2):
(-809.4)^2 = 655,084.36
(-804.4)^2 = 647,363.36
(-799.4)^2 = 639,681.16
(-794.4)^2 = 632,038.56
(-792.4)^2 = 628,225.76
xy product (x deviation * y deviation):
-809.4 * -1.9 = 1,538.86
-804.4 * -1.1 = 884.84
-799.4 * -0.1 = 79.94
-794.4 * 1.0 = -794.4
-792.4 * 2.1 = -1,662.04
Now, let's calculate the sums of these values:
Sum of x = -3,999.6
Sum of y = 72.5
Sum of x squared deviation = 3,202,392.6
Sum of xy product = -1285.7
Now, we can calculate the slope of the line (b) using the formula:
b = Sum of xy product / Sum of x squared deviation
b = -1285.7 / 3,202,392.6
b ≈ -0.00040144
Next, we can calculate the y-intercept (a) using the formula:
a = mean of y - (b * mean of x)
a = 14.5 - (-0.00040144 * 799.4)
a ≈ 14.8117
Therefore, the linear regression equation of the line of best fit for the data is:
y ≈ 14.8117 - 0.00040144x
To find the equation of the line of best fit, we can use the least squares regression method. Let's set up the calculations:
Mean of x (years) = (1990 + 1995 + 2000 + 2005 + 2007) / 5 = 3997 / 5 = 799.4
Mean of y (percentage) = (12.6 + 13.4 + 14.4 + 15.5 + 16.6) / 5 = 72.5 / 5 = 14.5
Now, let's calculate the deviations and squared deviations:
x deviation (x - mean of x):
1990 - 799.4 = -809.4
1995 - 799.4 = -804.4
2000 - 799.4 = -799.4
2005 - 799.4 = -794.4
2007 - 799.4 = -792.4
y deviation (y - mean of y):
12.6 - 14.5 = -1.9
13.4 - 14.5 = -1.1
14.4 - 14.5 = -0.1
15.5 - 14.5 = 1.0
16.6 - 14.5 = 2.1
x squared deviation ((x - mean of x)^2):
(-809.4)^2 = 655,084.36
(-804.4)^2 = 647,363.36
(-799.4)^2 = 639,681.16
(-794.4)^2 = 632,038.56
(-792.4)^2 = 628,225.76
xy product (x deviation * y deviation):
-809.4 * -1.9 = 1,538.86
-804.4 * -1.1 = 884.84
-799.4 * -0.1 = 79.94
-794.4 * 1.0 = -794.4
-792.4 * 2.1 = -1,662.04
Now, let's calculate the sums of these values:
Sum of x = -3,999.6
Sum of y = 72.5
Sum of x squared deviation = 3,202,392.6
Sum of xy product = -1285.7
Now, we can calculate the slope of the line (b) using the formula:
b = Sum of xy product / Sum of x squared deviation
b = -1285.7 / 3,202,392.6
b ≈ -0.00040144
Next, we can calculate the y-intercept (a) using the formula:
a = mean of y - (b * mean of x)
a = 14.5 - (-0.00040144 * 799.4)
a ≈ 14.8117
Therefore, the linear regression equation of the line of best fit for the data is:
y ≈ 14.8117 - 0.00040144x