DOB: 05/1999

Part 2:

• Show your work and explain your process for determining the solution for each of these problems on a word document with the solution given below the problem.
• If Excel was used, please indicate that as well on the word document.
• A word document and/or the Excel Workbook (if used) should be submitted to the Dropbox with labels on the worksheets to indicate which problem is being evaluated.
• All answers should be clearly indicated.
• Written explanation, reasoning, and rationale should use complete sentences.

A venture capitalist has just given you several million dollars to develop your dream product! Explain in detail what this product is and why people would buy it. (Think Steve Jobs and the iPhone – did people really think we needed “smartphones” back in 2007?)

Now your dream product has gone into production and the manager is asking you, as the statistical expert, to use statistical methods to ensure quality control. See example and CA Starter video in this module’s Livebinder.

1. Construct a quality control chart and compute upper and lower control limit bounds.
   1. You will generate a random dataset of N samples of defective proportions by completing the following steps:
      1. You will start with a random number by combining the last 2 digits of the year in which you were born plus the day of the month in which you were born. For example, if you were born October 3, 1990, your number would be 90 + 3 = 93. (If your number exceeds 100, subtract 100 from the total.) Call this X and it will seed your random number generation.
      2. Choose a number of samples, N. (N should be between 5 and 10 samples.)
      3. In Excel, type =RAND()*X in a cell. Repeat N times. This will generate the proportion of defective products (out of 100) for your N samples.
   2. Use Excel to create a p-chart for a sample size, 100, and the number of samples, N. See video in Livebinder for creating the p-chart.
   3. What is your Lower Control Limit (LCL) and Upper Control Limit (UCL).
2. Achieve goals through planning and prioritization.
   1. Is the product in control? If not in control, what sample(s) was outside of the limits, ie below LCL or above UCL?
   2. What measures could be taken now to address the data points that out of control?
   3. What recommendations would you suggest to optimize quality in future production?