Answer:
The answer to this question can be defined as follows:
Step-by-step explanation:
Given:
Standard deviations of the four activities of the critical path are 1,2,4,2.
Standard deviation of this critical path = Sum of square root of variance of this corresponding critical path
Standard deviation of critical path
Now we need to find the probability that the project will completed in 38 weeks given that its expected completion time is 40 weeks.
That is, we need to find P(X<38) :
Probability
Thus the probability that the project will be completed in 38 weeks is 0.34.
It depends on how b approaches 0
If b is positive and gets closer to zero, then we say b is approaching 0 from the right, or from the positive side. Let's say a = 1. The equation a/b turns into 1/b. Looking at a table of values, 1/b will steadily increase without bound as positive b values get closer to 0.
On the other side, if b is negative and gets closer to zero, then 1/b will be negative and those negative values will decrease without bound. So 1/b approaches negative infinity if we approach 0 on the left (or negative) side.
The graph of y = 1/x shows this. See the diagram below. Note the vertical asymptote at x = 0. The portion to the right of it has the curve go upward to positive infinity as x approaches 0. The curve to the left goes down to negative infinity as x approaches 0.
Answer:
y=4.8710 is the missing value
Step-by-step explanation:
The first step in approaching this question is determining the exponential equation that models the set of data. This can easily be done in Ms.Excel application. We first enter the data into any two adjacent columns of an excel workbook. The next step is to highlight the data, click on the insert tab and select the x,y scatter-plot feature. This creates a scatter-plot for the data.
The next step is to click the Add chart element feature and insert an exponential trend line to the scatter plot ensuring the display equation on chart is checked.
The exponential regression equation for the data set is given as;
To find the missing y value, we simply substitute x with 2 in the regression equation obtained;
