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You need to show how you arrived at the simulated values in column D. Run the RAND() function 500 times to meet this requirement. Then you want to use the VLOOKUP function to associate a cumulative probability to the number of defects. After you complete the exercise, you want to go a bit further into your description of the differences between the probability table and the simulation table. What do differences between the two outcomes tell us? What should we do about the differences (possible additional actions)? Why do we need to run the simulation in the first place? The central limit theorem in it's shortest form states that the sampling distribution of the sampling means approaches a normal distribution as the sample size gets larger, regardless of the shape of the population distribution.

Could you please have the writer answer the following questions: For your output, you have a mean of 1.50 and a standard deviation of 1.162, not 0.056. Based on these values, the distribution is positively skewed and will not follow the assumption of the central limit theorem. Even with a standard deviation of 0.056, we would expect 99.974% of the values to fall between 0 and 1.668. Based on the probability table given, the cumulative probability of 2 defects is only 54.9%. The data set is positively skewed because there are additional data point outside of the mean plus three standard deviations. Thoughts? You used an interesting technique to produce the 500 simulated values (data analysis / random number generation). As a part of the tip portion of this assignment, can you share with other students the values you used to produce the simulated numbers (variables, distribution, pvalue, etc.)?