In: Statistics and Probability
The production department of Celltronics International wants to explore the relationship between the number of employees who assemble a subassembly and the number produced. As an experiment, 3 employees were assigned to assemble the subassemblies. They produced 12 during a one-hour period. Then 5 employees assembled them. They produced 20 during a one-hour period. The complete set of paired observations follows. |
Number of Assemblers |
One-Hour Production (units) |
3 | 12 |
5 | 20 |
2 | 6 |
6 | 25 |
4 | 17 |
The dependent variable is production; that is, it is assumed that different levels of production result from a different number of employees. |
Click here for the Excel Data File
b. |
A scatter diagram is provided below. Based on it, does there appear to be any relationship between the number of assemblers and production? |
(Click to select)NoYes , as the number of assemblers (Click to select)decreasesincreases, so does the production. |
c. |
Compute the correlation coefficient. (Negative amounts should be indicated by a minus sign. Round sx, sy and r to 3 decimal places.) |
X | Y | ( )2 | ( )2 | ( )( ) | ||
3 | 12 | -4 | 16 | |||
5 | 20 | 1 | 1 | 4 | ||
2 | 6 | -10 | 100 | |||
6 | 25 | 2 | 4 | 18 | ||
4 | 17 | 1 | 0 | 0 | ||
= | = | sx | = |
sy | = | r | = |
X | Y | X-xbar | Y-ybar | (X-xbar )2 | ( Y-ybar)2 | ( X-xbar)(Y-ybar ) | ||
3 | 12 | -1 | -4 | 1 | 16 | 4 | ||
5 | 20 | 1 | 4 | 1 | 16 | 4 | ||
2 | 6 | -2 | -10 | 4 | 100 | 20 | ||
6 | 25 | 2 | 9 | 4 | 81 | 18 | ||
4 | 17 | 0 | 1 | 0 | 1 | 0 | ||
xbar=sum(X)/n | 4 | |||||||
ybar=Sum(Y)/n | 16 | |||||||
s^2y=(sum(Y-ybar)^2)/(n-1) | 53.5 | sy=sqrt(s^2y) | 7.3144 | |||||
s^2x=(sum(X-xbar)^2)/(n-1) | 2.5 | sx=sqrt(s^2x) | 1.5811 | =Sxy/(Sx*Sy) | =0.9944 | |||
Sxy=(sum((X-xbar)(Y-ybar)))/(n-1) | 11.5 | |||||||
From the scatter plot there is positive relationship between the number of assemblers and production.
means as number of assemblers increases the production is also increases . (Yes and Increases)