Complete “Example 13.2: Process Control Chart Design,” located in Chapter 13 of the textbook.
EXAMPLE 13.2:
Process Control Chart Design An insurance company wants to design a control chart to monitor whether insurance claim forms are being completed correctly. The company intends to use the chart to see if improvements in the design of the form are effective. To start the process, the company collected data on the number of incorrectly completed claim forms over the past 10 days. The insurance company processes thousands of these forms each day, and due to the high cost of inspecting each form, only a small representative sample was collected each day. The data and analysis are shown in Exhibit 13.6.SOLUTION To construct the control chart, first calculate the overall fraction defective from all samples. This sets the center line for the control chart.
p= Total number of defective units from all samples/Number of samples 3 Sample size
= 91/3000 = .03033
Next calculate the sample standard deviation:
sp 5 _ p (1 2 _ p ) ________ n 5 Ï_________________ .03033(1 2 .03033) _________________ 300 5
.00990
Finally, calculate the upper and lower process control limits. A z-value of 3 gives 99.7 percent confidence that the process is within these limits
UCL 5 _ p 1 3sp 5 .03033 1 3(.00990) 5 .06003
Write a 150-300-word paragraph comparing the simple moving average weighted moving average, exponential smoothing, and linear regression analysis time series models.
Complete “Example 18.4: Computing Trend and Seasonal Factor From a Linear Regression Line Obtained With Excel,” located in Chapter 18 of the textbook.
Exponential smoothing with trend[18.4] Ft 5 FITt21 1 ?(At21 2 FITt21)
EXAMPLE 18.4: Computing Trend and Seasonal Factor from a Linear Regression Line
Obtained with Excel Forecast the demand for each quarter of the next year using trend and seasonal factors.
Demand for the past two years is in the following table:
Quarter Amount
12343002002205305678520420400700
SOLUTION First, we plot as in Exhibit 18.10 and then calculate the slope and intercept using Excel. For Excel the quarters are numbered 1 through 8. The “known ys” are the amounts (300, 200, 220, etc.), and the “known xs” are the quarter numbers (1, 2, 3, etc.). We obtain a slope 5 52.3 (rounded), and intercept 5 176.1 (rounded). The equation for the line is Forecast Including Trend (FIT) 5 176.1 1 52.3t
Next we can derive a seasonal index by comparing the actual data with the trend line, as in Exhibit 18.11. T
he seasonal factor was developed by averaging the same quarters in each year. We can compute the 2013 forecast including trend and seasonal factors (FITS) as follows:
FITS t = FIT X Seasonal
I—2013 FITS 9 = [176.1 1 52.3(9)]1.25 5 808
II—2013 FITS 10 = [176.1 1 52.3(10)]0.79 5 552
III—2013 FITS 11 = [176.1 1 52.3(11)]0.70 5 526
IV—2013 FITS 12 = [176.1 1 52.3(12)]1.28 5 1,029
These numbers were calculated using Excel, so your numbers may differ slightly due to rounding.
For a step-by-step walk through of this example, visit www.mhhe.com/jacobs14e_sbs_ch18.jac24021_ch18_442-486.indd 45808/12/12 1:50 AM
Write a 150-300-word paragraph explaining the market research, panel consensus, historical analogy, and Delphi method qualitative forecasting techniques.
Prepare your responses in Excel with each problem on a separate tab.
While APA format is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA formatting guidelines, which can be found in the APA Style Guide, located in the Student Success Center.

Re-do the calculations from the example including a graph with the control limits if needed. There is no need to solve the problem multiple times using the different techniques. Want to make sure that everyone knows how to perform the calculations and that I can follow the work – a screen capture of the book will not be sufficient. Then just answer the questions asked comparing and contrasting the different methods.