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Remember?Remember? An Illustration of An Illustration of Total Risk (Discrete Distribution)Total Risk (Discrete Distribution)Remember?Remember? An Illustration of An Illustration of Total Risk (Discrete Distribution)Total Risk (Discrete Distribution)
The standard deviation standard deviation = SQRT (14,400,000)= $3,795$3,795
The expected cash flow expected cash flow = $5,000$5,000
Coefficient of Variation (CV)Coefficient of Variation (CV) = $3,795 / $5,000 = $3,795 / $5,000= = 0.7590.759
CV is a measure of CV is a measure of relativerelative risk and is the ratio of risk and is the ratio of standard deviation to the mean of the distribution.standard deviation to the mean of the distribution.
Remember? Remember? An Illustration of An Illustration of Total Risk (Discrete Distribution)Total Risk (Discrete Distribution)Remember? Remember? An Illustration of An Illustration of Total Risk (Discrete Distribution)Total Risk (Discrete Distribution)
Here we have assumed a mean price of $35 per unit and a standard deviation of $5. In step 2 we have pulled a price of $37.14 from the distribution which is 0.43 standard
deviations to the right of the mean.
Let us create a simple simulation exercise using price:
Step 1: Describe the distribution. We will assume a standard normal for pricesExpected Price 35.00$ Variation (stand dev) 5.00$
Step 2: We will now grab a price for our product from a continuous distribution that has a mean value of $35 and a standard deviation of $5.
Value: 37.14$ Note how far away this value is from 500.
Step 3: We can calculate a z-score and determine the probability under the curvez-score 0.43 <--- (C11-C4)/C5
Simulation Exercise!Simulation Exercise!Now let us use more than one observation and ‘simulate’ the distribution. Let us use 500
data observation points and look at the frequency distribution.
Step 4: What if we wanted to expand this to a LARGER sample size to generatea better distribution than a sample size of 1? Why don't we createlots of observations for our data - say 500 points.[ See cells F2:O51 ]
Step 5: Use FREQUENCY to create a frequency distribution for our 500 data pts.Bin Frequency15 0 =FREQUENCY(F2:O51,B22:B30)20 025 1130 7535 15740 18145 6650 955 1
>55 0Observations: 500
Mean: 34.95Standard Dev: 5.02
Create the beginning of the table that is yellow. In the cell that is purple enter the formula. The portion that is F2:O51
is the 500 randomly generated data points to the right. Theportion of the formula that is B22:B30 represents the
"bins" that we want to use to determine our distribution. After entering the formula, press the 'F2' button and then press "CNTRL-SHIFT-ENTER" all at the same time. This will
Simulation Exercise!Simulation Exercise!We can graph the distribution and we notice how the graph is beginning to look like a standard normal continuous graph. If we were to add more bins and additional data
observations are graph would approximate the standard normal distribution.