1. Section 1.2 Discrimination in the Workplace: Inference through Simulation

2.  Inference: a statistical procedure that involves deciding whether an event can reasonably attributed to chance OR if you should look for another explanation.  Simulation: Setting up a model to simulate the actual process and repeating it to see what happens. This is then compared to what actually occurred.

3.  Summary Statistics: a single number that condenses and summarizes the data.  Average or Mean is a summary statistic  Sum of the data values / # of data values (n)

4.  Simulate selecting 3 employees out of 10 to lay off. Like round 2 of the lay offs from 1.1.  How would you do that with simple materials?  Refer to page 13 for an example simulation.  Follow these steps.  The ages to use are written on the board.  Repeat the process 10 times.

5.  Using a TI-83 or higher:  Assign each employee a number 1 – 10.  Use the “randInt” function to randomly select a value from 1-10.  MATH key  PRB  randInt(1,10,n) (start, end, n selections)  What if you select the same number twice? ▪ randInt(1,10,6): take the first 3 non-duplicated values.

6.  Create a classroom Dot Plot of your averages for each repetition.  Look at the Dot Plot: How many times did we get a result of 58 or higher?  Based on our simulation, what is the probability that you would randomly get an average age of 58 or higher?  Probability: proportion of successes out of total trials in the long run.  If Westvaco was truly unbiased by age would you expect that they chose the people they did? Explain.

7.  If we decided that the probability was high enough that there was reasonable possibility that Westvaco could have chosen those employees without bias, then they may be off the hook.  However, if the probability was very low, we can say that it is very unlikely that they chose those employees unbiased of age.  They may still have valid reasoning, but now the need for an explanation is on them.

8.  P5 on page 17  E11 on page 19