Question:

EXAMPLE 13.2: Process Control Chart DesignAn 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

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

Quarter	Amount
1	300
2	200
3	220
4	530
5	520
6	420
7	400
	700

I have the tables attached for both problems. Part 2 of the problem is below, which is what I need assistance with:

Complete "Example 13.2: Process Control Chart Design," located in Chapter 13 of the textbook.

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.

Write a 150-300-word paragraph explaining the market research, panel consensus, historical analogy, and Delphi method qualitative forecasting techniques.

p sp 0.03033 0.00990 UCL LCL 0.06004 0.00063 Intercept Slope 176.1 52.3 Period Year 1 2 3 4 5 6 7 8 9 10 11 12 Quarter 2011 I 2011 II 2011 III 2011 IV 2012 I 2012 II 2012 III 2012 IV 2013 I 2013 II 2013 III 2013 IV Amount FITt 300 200 220 530 520 420 400 700 228.3 280.6 332.9 385.1 437.4 489.6 541.9 594.2 646.4 698.7 751.0 803.2 Actual Trend Seasonal Factor FITSt 1.314 1.251 0.713 0.785 0.661 0.700 1.376 1.277 1.189 1.251 0.858 0.785 0.738 0.700 1.178 1.277 1.251 0.785 0.700 1.277 Period 285.7322 220.3428 232.8471 491.8583 547.3306 384.5015 379.0844 758.8454 808.9289 548.6602 525.3217 1025.8325 1 2 3 4 5 6 7 8 9 10 11 12 Year Amount 2011 2011 2011 2011 2012 2012 2012 2012 2013 2013 2013 2013 Sum Average Average of Same Quarter 600 1550 1500 1500 2400 3100 2600 2900 3800 4500 4000 4900 33350 2779.2 Seasonal Factor 2266.7 3050.0 2700.0 3100.0 0.82 1.10 0.97 1.12 0.82 1.10 0.97 1.12 0.82 1.10 0.97 1.12 Intercept Slope Deseasonalized Demand 735.662 1412.363 1543.981 1344.758 2942.647 2824.727 2676.235 2599.866 4659.191 4100.410 4117.284 4392.876 555.0 342.2