Age of Employee (X)
% (y)
(x*y)
(x*x)
(20 - 30) 20
10
20 * 10 = 200
20 * 20 = 400
(30 - 40) 15
7.5
15 * 7.5 = 112.5
15 * 15 = 225
(40 - 50) 10
5
10 * 5 = 50
10 * 10 = 100
(50 - 60) 3
1.5
3 * 1.5 = 4.5
3 * 3 = 9
(60 - 65) 2
1
2 * 1 = 2
2 * 2 = 4
Σx = 50
Σy = 25
Σxy = 369
Σx2 = 738
(b) = (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2)
(b) = ((5)*(75)-(50)*(25))/((5)*(369)-(50)2)
(b)= (375 - 1250) / (1845 - 2500)
(b) = 875/655
(b) = 1.4
(a) = (ΣY - b(ΣX)) / N
(a) = (25-1.4(50))/5
(a) = (25 - 40.5)/5
(a) = -3.1
Chat with our AI personalities
There are numerous ways to do this. I think the easiest is to put the data in excel and have excel show the trend line, equation, andcorrelation coefficient. Excel gives you several options to choose for the trend line analysis. The other way is if it is a linear relationship, you can do the linear regression analysis following the steps listed in the related link. If you are not familiar with regression analysis, it may not be easy for you to follow.
on the lineGiven a linear regression equation of = 20 - 1.5x, where will the point (3, 15) fall with respect to the regression line?Below the line
Finding the line of best fit is called linear regression.
The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive. The regression coefficient is the slope of the line of the regression equation.
A correlation coefficient is a value between -1 and 1 that shows how close of a good fit the regression line is. For example a regular line has a correlation coefficient of 1. A regression is a best fit and therefore has a correlation coefficient close to one. the closer to one the more accurate the line is to a non regression line.