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Hypothesis Testing on Data Set 2

6-1 Data Set Homework: Hypothesis Testing on Data Set 2

Data set 2 presents a sample of the number of defective flash drives produced by a small manufacturing company over the last 30 weeks. The company’s operations manager believes that the number of defects produced by the process is less than seven defective flash drives per week. Use this online calculator (or any statistical package that you are comfortable with) to construct a hypothesis test to verify the operations manager’s claim. Your hypothesis test should include null and alternative hypotheses, a t test statistic value, a p value, a decision, and a conclusion. Submit a Word file that includes the hypothesis test.  ATTACHMENT PREVIEW

 
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Final Project Case Addendum Vice-president Arun Mittra speculates:

QSO 510 Final Project Case Addendum Vice-president Arun Mittra speculates: We have always estimated how many transformers will be needed to meet demand. The usual method is to look at the sales figures of the last two to three months and also the sales figures of the last two years in the same month. Next make a guess as to how many transformers will be needed. Either we have too many transformers in stock, or there are times when there are not enough to meet our normal production levels. It is a classic case of both understocking and overstocking. Ratnaparkhi, operations head, has been given two charges by Mittra. First, to develop an analysis of the data and present a report with recommendations. Second, “to come up with a report that even a lower grade clerk in stores should be able to fathom and follow.” In an effort to develop a report that is understood by all, Ratnaparkhi decides to provide incremental amounts of information to his operations manager, who is assigned the task of developing the complete analyses. A-Cat Corporation is committed to the pursuit of a robust statistical process control (quality control) program to monitor the quality of its transformers. Ratnaparkhi, aware that the construction of quality control charts depends on means and ranges, provides the following descriptive statistics for 2006 (from Exhibit 1). 2006 Mean 801.1667 Standard Error 24.18766 Median 793 Mode 708 Standard Deviation 83.78851 Sample Variance 7020.515 Kurtosis -1.62662 Skewness 0.122258 Range 221 Minimum 695 Maximum 916 Sum 9614 Count 12 The operations manager is assigned the task of developing descriptive statistics for the remaining years, 2007–2010, that are to be submitted to the quality control department. A-Cat’s president asks Mittra, his vice-president of operations, to provide the sales department with an estimate of the mean number of transformers that are required to produce voltage regulators. Mittra, recalling the product data from 2006, which was the last year he supervised the production line, speculates that the mean number of transformers that are needed is less than 745 transformers. His analysis reveals the following: t = 2.32 p = .9798 This suggests that the mean number of transformers needed is not less than 745 but at least 745 transformers. Given that Mittra uses older (2006) data, his operations manager knows that he substantially underestimates current transformers requirements. She believes that the mean number of transformers required exceeds 1000 transformers and decides to test this using the most recent (2010) data. Initially, the operations manager possessed only data for years 2006 to 2008. However, she strongly believes that the mean number of transformers needed to produce voltage regulators has increased over the three-year period. She performs a one-way analysis of variance (ANOVA) analysis that follows: 2006 2007 2008 779 845 857 802 739 881 818 871 937 888 927 1159 898 1133 1072 902 1124 1246 916 1056 1198 708 889 922 695 857 798 708 772 879 716 751 945 784 820 990 Anova: Single Factor SUMMARY Groups Count Sum Average Variance 2006 12 9614 801.1667 7020.515 2007 12 10784 898.6667 18750.06 2008 12 11884 990.3333 21117.88 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 214772.2 2 107386.1 6.870739 0.003202 3.284918 Within Groups 515773 33 15629.48 Total 730545.2 35 The results (F = 6.871 and p = 0.003202) suggest that indeed the mean number of transformers has changed over the period 2006–2008. Mittra has now provided her with the remaining two years of data (2009 and 2010) and would like to know if the mean number of transformers required has changed over the period 2006–2010. Finally, the operations manager is tasked with developing a model for forecasting transformer requirements based on sales of refrigerators. The table below summarizes sales of refrigerators and transformer requirements by quarter for the period 2006–2010, which are extracted from Exhibits 2 and 1 respectively. Sales of Refrigerators Transformer Requirements 3832 2399 5032 2688 3947 2319 3291 2208 4007 2455 5903 3184 4274 2802 3692 2343 4826 2675 6492 3477 4765 2918 4972 2814 5411 2874 7678 3774 5774 3247 6007 3107 6290 2776 8332 3571 6107 3354 6729 3513

 
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A-Cat Corporation

Introduction:

Transformer requirements will be analyzed at A-Cat Corporation, a refrigerator production company.  The operations team in charge of the production facility has been asked to determine how many transformers will be needed to meet demands accurately.  In the past, they have had issues correctly forecasting this number.  The amounts calculated have either exceeded the amount necessary or have fallen short.  Miscalculating has led to negative results coming from over expenditures or missed sales (ND, QSO 510).

The president of the company has delegated the vice president of operations to provide the sales team with the mean number of transformers needed to produce the voltage regulators to meet demand requirements. The VP has asked the head of operations to create a report that analyzes the necessary data.  From this report, recommendations will be provided.  A simplified report will be issued to employees to understand the data better. (ND, QSO 510).

Some quantifiable factors may affect operational performance. The price of the regulators could be changing the operational process. A-cat is concerned that various ordering patterns may be resulting in suppliers changing prices on them. Also, the number of transformers required for voltage regulators per a period could influence performance. Transformer requirements are measured in units and refrigerator sales. Data from past years are being used to forecast current demand of voltage regulators (ND, QSO 510). The fact that old data is being used is affecting operational process control. Because processes and items manufactured overtime change, old data is less valuable to forecasting present day trends.  The value of the equipment is another factor to be considered.  Products could be designed and made in a manner that results in either shorter or longer lifespan.

Problem statement.

 The operations manager needs to evaluate the quality of transformers in use as well as forecast future transformer needs based on some refrigerators built to control inventory better. The who is the operations manager; the what is transformer QC and forecasting and the why is to manage inventory (Chris, 2015) better. In the past stock has been too high or too low and this has caused the A-Cat Corporation to lose profits. The goal now is to analyze past quality and demand data to forecast today’s needs better.

Data handling and analyzation is a significant and recurring issue at A Cat Corporation. 

The key part of the A cat organization operational front is to get the precise estimate the demand of voltage regulators (ND, QSO,510). If the voltage regulators expect very high or too low than the original/required number of units, then the corporation is going to pay a hefty price regarding inventory costs for failing to meet production levels/opportunity cost. 

Many things need to be considered to obtain accurate data on supply and demand of the regulators. 

It is important to keep an up to date log of sales numbers, regulators, transformers, quality of the products.  This information should be updated regularly as new information is provided.  This report will allow management to see how the company is doing and where the holes are. 

Data from past periods are being used to forecast current demand of voltage regulators (ND, QSO 510). The fact that old data is being used is affecting operational process control. Because processes and items manufactured overtime change, old data is less valuable to forecasting present day trends.

Here the major problem in the cat A corporation is using the old data for estimating the demand of voltage regulators. Next, the critical information is not sharing among the various departments. Quality control department is not updated by the latest descriptive statistics.

There are number gaps in the operational front in the cat A corporation. The functional team has to provide timely estimates and share with the relevant departments like sales team and production team.  The descriptive statistics/ quality parameters should be communicated on the timely basis to quality/production control team.

A corporation should update data so that it can be validated.  Tracking is nearly impossible if accurate information isn’t provided. 

The following need to be remedied to keep data consistent.

  • Data not being updated in real time.
  • Utilization of old information
  • Failure to catch the trend
  • Critical data not being shared with all vested parties
  • Not identifying the key factors for estimates and understanding the relationship among them
  • Analyzing statistically and implementation of new/updated strategies.

The operational manager needs to forecast the demand of transformers based on the sales of the refrigerators.

Since we forecast the demand using sales of the refrigerators, plot the sales of the refrigerators on the x-axis and transform requirements on the y-axis.

Chris. (2009, March 18). How to Write a Problem Statement. Ceptara. Retrieved from www.ceptara.com/blog/how-to-write-problem-statement

Kalla, Siddharth. (ND). Statistical Variance. Explorable. Retrieved from https://explorable.com/statistical-variance

 
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the strengths and weaknesses using the Organizational Change Readiness assessment

What are the strengths and weaknesses using the Organizational Change Readiness assessment?

 
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