Put a comments in those two post, the Professor asked us to comments to our classroom post on blackboard 1 I used the Random Number Generator in Excel to create 100 random numbers. It was found that the average of the 100 numbers was 7.5. This means that he will need to see on average 7.5 customers a day to cover their expenses. I created a histogram to see what the numbers would look like, and it created a good bell curve. Probability distribution is the list of all of the possible outcomes of a random variable and its corresponding probability values. A discrete variable is a variable whose value is obtained by counting. A continuous variable is one that continues as if it can assume an infinite number of real values within a given interval. 2 A poisson distribution according to Evans is a discrete distribution (whole numbers that can be counted) used to model the number of occurrences in some unit of measure - ie; the number of customer consulted each day. This type of distribution assumes no limit on the number of occurrences and can be any nonnegative integer. I listed 1-100 for the samples in column C and navigated to the Data Analysis tool, Random Number Generation as explained on page 456 (Evans, 2020) following example 12.2: -Number of variables = 1 -Number of random numbers = 100 -Distribution = Poisson -Lambda = 7(mean) -Output range - Cell $D$6 I competed the average of cells D6 - D105 = 7.32. If the consultant needs to have at least a 5 customer daily average to cover his costs than this Random Number Generation shows with his average of 7.32, he will be successful. Probability distributions are used when uncertain inputs are being modeled. Certain points of data may not be available therefore using probably distribution models will help determine unknown variables. Discrete random variables can be counted and continuous random variables are over one or more continuous intervals of real numbers (Evans