150-300-word paragraph explaining the market research

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Business Finance

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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.

Refer to the Excel spreadsheet, “Quality Control Analytics at Toyota," to complete the "Case: Quality Management Toyota," at the end of Chapter 13 in the textbook. Answer Questions 1-8.

Refer to the Excel spreadsheet, "Computing Trend and Seasonal Factor From a Linear Regression Line Obtained "to complete the "Example 18.4: Computing Trend and Seasonal Factor From a Linear Regression Line Obtained With Excel," located in Chapter 18 of the textbook.

After working through the example, reflect write a 150-300-word paragraph explaining the market research, panel consensus, historical analogy, and Delphi method qualitative forecasting techniques.

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.


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Forecasting Data Period 2011 Q1 2011 Q2 2011 Q3 2011 Q4 2012 Q1 2012 Q2 2012 Q3 2012 Q4 Simple linear regression Demand (y) Intercept Slope Forecasts and Error Analysis Forecast Error #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Total #DIV/0! Average #DIV/0! Bias Period(x) #DIV/0! #DIV/0! Forecast #DIV/0! Using Linear Regression Method 9 Trend from Actual Amount forecast Quarter 2011 Ration of Actual/Trend 1 2 3 4 0 0 0 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 1 2 3 4 0 0 0 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 2012 Forecast Including Trends FITSt I-2013 FITS9 I-2013 FITS10 I-2013 FITS11 I-2013 FITS12 Intercept=176.1, Slope=52.3 = 9 10 11 12 FIT X Seasonal #DIV/0! #DIV/0! #DIV/0! #DIV/0! 1 2 3 4 nd Error Analysis Absolute #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! MAD SE Correlation Coefficient of determination Seasonal Factor(Av. Of Same Qtr for 2011 and 2012) #DIV/0! #DIV/0! #DIV/0! #DIV/0! Squared #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! MSE #DIV/0! #DIV/0! #DIV/0! Abs Pct Err #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! MAPE Tracking Signal Cum error Cum Abs ErrMad #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Track Signal (Cum error/MAD) #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Regression 1.5 1 0.5 0 0 0.2 0.4 Series1 0.6 0.8 Linear (Series1) 1 1.2 Quality Control Number of samples Sample size p chart 10 Data # Defects Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8 Sample 9 Sample 10 Graph information Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8 Sample 9 Sample 10 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! ## ## ## ## ## ## ## ## ## ## % Defects #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Results Total Sample Size Total Defects Percentage defects Std dev of p-bar z value Upper Control Limit Center Line Lower Control Limit 0 0 #DIV/0! #DIV/0! 3 #DIV/0! #DIV/0! #DIV/0! p-chart 1 0.9 0.8 Mean 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 4 5 6 Sample 7 8 9 10 326 section 2 MANUFACTURING AND SERVICE PROCESS For a step-by-step walkthrough of this example, visit www.mhhe.com/ jacobs14e sbs cht. EXAMPLE 13.2: Process Control Chart Design An insurance company wants to design a control chart lo monitor whether insurance claim forms are being completed correctly. The company intends to use the chart to see if improve- ments in the design of the form are effective. To start the process, the company collected dala on the number of incorrectly completed claim forms over the past 10 days. The insurance company processes thousands of these forms cach day, and due to the high cost of inspecting cach 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 centerline for the control chari. Total number of defective units from all samples 91 = .03033 Number of samples X Sample size 3.000 Next calculate the sample standard deviation: Analytics Xcel For the Excel Impiale, vil www.mhhe.com actite .03033(1 - .03033) 00990 300 Finally, calculate the upper and lower process control limits. Az-value of 3 gives 99.7 percent confidence that the process is within these limits. UCI. = p + 38, – 03033 + 34.00990) = XXXO3 LCI. =P - 35,= _03033 - 30.00990) = .00063 The calculations in Exhibit 13.6, including the control chart, are included in the spreadsheet "SPC.xls." exhibit 13.6 0.06500 0.06000 0.05500 0.05000 Insurance Company Claim Form NUMBER OF FORMS NUMBER COMPLETED FRACTION SAMPLE INTED INCIRRICTLY DETIVE 1 300 10 0.03333 2 300 8 0.02067 3 300 9 0.03000 4 300 13 0.04333 5 300 7 0.02333 6 300 7 0.02333 7 300 6 0.02000 8 300 11 0.03667 9 300 12 0.04000 10 300 8 0.02867 Totals 3,000 91 0.03033 Sample standard deviation 0.00990 0.04500 0.04000 0.03500 0.03000 0.02500 0.02000 0.01500 0.01000 0.00500 Sample Lipper controllimit Lower controllimit mo 1 2 3 4 5 7 8 9 1
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Excellent resource! Really helped me get the gist of things.

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