Us House Committee On Veterans’ Affairs Representation Effect On Number Of Compensated Veterans

Date: 12 September 2013 Statistical Analysis: US House Committee on Veterans’ Affairs Representation Effect on Number of Compensated Veterans US Veteran United States of America CC: US House Committee on Veterans’ Affairs; US Senate Committee on Veterans’ Affairs To Whom It May Concern: First, this paper appears long because of tables and graphs. Also, I am thankful that a MD with a PhD in statistics has helped me tweak my analysis for the better and verified my overall conclusions. Advocacy As a 100% schedular Total and Permanent disabled veteran, I have time to advocate for veterans. Sadly, I cannot work because of my illnesses, but I do put a lot of work into veteran advocacy. In addition to sending volumes of E-mails to US Congress, US President, my congressional representatives, and media, I hope to use some of my statistical knowledge as well. Since I have had some limited working experience with statistical analysis as a chemical engineer in the pharmaceutical industry, I have decided to apply my knowledge to veterans’ advocacy while learning new tools and methods too. Thankfully, “R” is open-source, powerful, useful, and free statistical software package[4]. Also, I have discovered that some statisticians are willing to share their valuable knowledge, which is something they are often paid. As an example, a MD with a PhD statistics recently suggested that I evaluate the current data with a linear mixed model, which I have learned to be quite powerful. Purpose I will not lie. The topics in this paper are complicated. I have spent considerable time on the topic of linear mixed modeling and have a basic understanding. I am not a mathematician or statistician. I believe my statistical outcomes are interesting. As mentioned, I think my statistical outcome, the average number of compensated veterans from the 26 congressional districts that make up the US House Committee on Veterans’ Affairs is not significantly different from the average number of compensated veterans from 26 randomly selected congressional districts, is interesting. Also, I will show that there is a very good linear fit between the logarithm of compensated veterans, congressional district population, and the logarithm of total veterans. Although congressional district population was initially included in the linear mixed model and the linear model, the reader will discover that congressional district population is not needed to model the average number of compensated veterans. In other words, my statistical analysis discovered...

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Study Of Brand Effectiveness Of Xtracare Retail Outlets Of Iocl

A LIVE PROJECT ON . . . STUDY OF BRAND EFFECTIVENESS OF XTRACARE RETAIL OUTLETS OF IOCL . . . . A live project submitted to IOCL RETAIL DIVISIONAL SALES OFFICE, AHMEDABAD during the SUMMER INTERNSHIP of . . . . BACHELOR OF BUSINESS ADMINISTRATION . . . . Submitted by DEVANSH RIDHVAJ BHATIA . IOCL AHMEDABAD (2011)...

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Sources Of Variation3

August 2012 This month’s newsletter is the first in a multi-part series on using the ANOVA method for an ANOVA Gage R&R study. This method simply uses analysis of variance to analyze the results of a gage R&R study instead of the classical average and range method. The two methods do not generate the same results, but they will (in most cases) be similar. This newsletter focuses on part of the ANOVA table and how it is developed for the Gage R &R study. In particular it focuses on the sum of squares and degrees of freedom. Many people do not understand how the calculations work and the information that is contained in the sum of squares and the degrees of freedom. In the next few issues, we will put together the rest of the ANOVA table and complete the Gage R&R calculations. In this issue: • Sources of Variation • Example Data • The ANOVA Table for Gage R&R • The ANOVA Results • Total Sum of Squares and Degrees of Freedom • Operator Sum of Squares and Degrees of Freedom • Parts Sum of Squares and Degrees of Freedom • Equipment (Within) Sum of Squares and Degrees of Freedom • Interaction Sum of Squares and Degrees of Freedom • Summary • Quick Links Any gage R&R study is a study of variation. This means you have to have variation in the results. On occasion, I get a phone call from a customer wondering why their Gage R&R study is not giving them any useful information. And, in looking at the results, I discover that each result is the same – for each part and for each operator. There is no variation. I am asked – Isn’t it good that there is no variation in the results? No, not in a gage R&R study. It means that the measurement process cannot tell the difference between the samples. So remember, a gage R&R study is a study in variation – this means that there must be variation. If you are not familiar with how to conduct a Gage R&R study, please see our December 2007 newsletter. This newsletter also includes how to analyze the results using the average and range method. As usual, please feel free to leave comments at the end of the newsletter. Sources of Variation Suppose you are monitoring a process by pulling samples of the product at some regular interval and measuring one critical quality characteristic (X)....

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Repeatability Is The Ability Of The Measurement System To Repeat The Same Measurements On The Same Sample Under The Same Conditions

August 2012 This month’s newsletter is the first in a multi-part series on using the ANOVA method for an ANOVA Gage R&R study. This method simply uses analysis of variance to analyze the results of a gage R&R study instead of the classical average and range method. The two methods do not generate the same results, but they will (in most cases) be similar. This newsletter focuses on part of the ANOVA table and how it is developed for the Gage R &R study. In particular it focuses on the sum of squares and degrees of freedom. Many people do not understand how the calculations work and the information that is contained in the sum of squares and the degrees of freedom. In the next few issues, we will put together the rest of the ANOVA table and complete the Gage R&R calculations. In this issue: • Sources of Variation • Example Data • The ANOVA Table for Gage R&R • The ANOVA Results • Total Sum of Squares and Degrees of Freedom • Operator Sum of Squares and Degrees of Freedom • Parts Sum of Squares and Degrees of Freedom • Equipment (Within) Sum of Squares and Degrees of Freedom • Interaction Sum of Squares and Degrees of Freedom • Summary • Quick Links Any gage R&R study is a study of variation. This means you have to have variation in the results. On occasion, I get a phone call from a customer wondering why their Gage R&R study is not giving them any useful information. And, in looking at the results, I discover that each result is the same – for each part and for each operator. There is no variation. I am asked – Isn’t it good that there is no variation in the results? No, not in a gage R&R study. It means that the measurement process cannot tell the difference between the samples. So remember, a gage R&R study is a study in variation – this means that there must be variation. If you are not familiar with how to conduct a Gage R&R study, please see our December 2007 newsletter. This newsletter also includes how to analyze the results using the average and range method. As usual, please feel free to leave comments at the end of the newsletter. Sources of Variation Suppose you are monitoring a process by pulling samples of the product at some regular interval and measuring one critical quality characteristic (X)....

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Parts Sum Of Squares And Degrees Of Freedom

August 2012 This month’s newsletter is the first in a multi-part series on using the ANOVA method for an ANOVA Gage R&R study. This method simply uses analysis of variance to analyze the results of a gage R&R study instead of the classical average and range method. The two methods do not generate the same results, but they will (in most cases) be similar. This newsletter focuses on part of the ANOVA table and how it is developed for the Gage R &R study. In particular it focuses on the sum of squares and degrees of freedom. Many people do not understand how the calculations work and the information that is contained in the sum of squares and the degrees of freedom. In the next few issues, we will put together the rest of the ANOVA table and complete the Gage R&R calculations. In this issue: • Sources of Variation • Example Data • The ANOVA Table for Gage R&R • The ANOVA Results • Total Sum of Squares and Degrees of Freedom • Operator Sum of Squares and Degrees of Freedom • Parts Sum of Squares and Degrees of Freedom • Equipment (Within) Sum of Squares and Degrees of Freedom • Interaction Sum of Squares and Degrees of Freedom • Summary • Quick Links Any gage R&R study is a study of variation. This means you have to have variation in the results. On occasion, I get a phone call from a customer wondering why their Gage R&R study is not giving them any useful information. And, in looking at the results, I discover that each result is the same – for each part and for each operator. There is no variation. I am asked – Isn’t it good that there is no variation in the results? No, not in a gage R&R study. It means that the measurement process cannot tell the difference between the samples. So remember, a gage R&R study is a study in variation – this means that there must be variation. If you are not familiar with how to conduct a Gage R&R study, please see our December 2007 newsletter. This newsletter also includes how to analyze the results using the average and range method. As usual, please feel free to leave comments at the end of the newsletter. Sources of Variation Suppose you are monitoring a process by pulling samples of the product at some regular interval and measuring one critical quality characteristic (X)....

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