...Grading Two Variable Proposal Grading Two Variable Proposal The topic that is current and interest me the most is the growing shift towards personal health and weight control. I choose this topic because I think that there are two very large variables at play when it comes to reaching the outcome of losing weight and living a healthier lifestyle. There is always a new trendy workouts to try like P90X, or T25. Or a person could subscribe to their local gym. After they determine the goal of what they want to look like and weigh, the first variable presents itself. To me the first variable to reaching the end result of achieving a healthier lifestyle and losing weight is what is the most effective method of physical activity that will promote weight loss? As mentioned before there are a lot of different types of things that a person could do in order to achieve their personal goal therefore they would have to identify which method will work best for their lifestyle. The second variable that plays an even larger role than the first variable is what type of diet to go on. Similar to the first variable, there are hundreds of different types of diets that claim that they can produce the results that a person is looking for. This variable is important in helping achieve a healthier lifestyle because proper nutrition gives the body the fuel that will promote healthier living. I believe these two variables have a relationship because you need to both parts in order to achieve...
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...SPSS .................................................................................. 7 Use syntax from start to finish .................................................................................................... 7 Maintain accurate codebooks for raw data sets .......................................................................... 7 Use summary data sets for analysis ............................................................................................ 7 Preserve the original raw data sets .............................................................................................. 8 Create data dictionaries for summary data sets........................................................................... 8 Naming variables ........................................................................................................................ 8 Use...
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...Derek Taylor 24 July 2015 Homework Assignment & Research Assignment 1.) What is the difference between Application Software and System Software? (Give an example of both) System software is an integral part that aids in the computer’s functioning. It manages the computer resources in ways that they can operate in tandem. System software provides a platform for the execution of application software. Examples of system software are BIOS and firmware. Application software is created for users. They manage their specific tasks to suit their needs like a media player of word processors. Examples of application software are CRM software, ERP software, accounting, graphics, and media software. 2.) What does it mean when we say that an interpreter both “Translates and Executes” Instructions? In the case of the interpreter, it translates and executes each line of codes one line at a time. Thus if the program has syntax errors (violation of program rule) lower down in the code, you never know until the interpreter reaches to that statement. 3.) Explain the “fectch-decode-execute” cycle. In the first step, fetch instruction, the processor fetches the instruction from the memory. The instruction is transferred from memory to instruction register. The processor is ready to fetch instruction. The instruction pointer contains the address 0100 contains the instruction MOV AX, 0. The memory places the instruction on the data bus. The processor then copies...
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...Description Also see Options Syntax One-to-one merge on specified key variables merge 1:1 varlist using filename , options Many-to-one merge on specified key variables merge m:1 varlist using filename , options One-to-many merge on specified key variables merge 1:m varlist using filename , options Many-to-many merge on specified key variables merge m:m varlist using filename , options One-to-one merge by observation merge 1:1 n using filename options , options Description Options keepusing(varlist) generate(newvar) nogenerate nolabel nonotes update replace noreport force variables to keep from using data; default is all name of new variable to mark merge results; default is merge do not create merge variable do not copy value-label definitions from using do not copy notes from using update missing values of same-named variables in master with values from using replace all values of same-named variables in master with nonmissing values from using (requires update) do not display match result summary table allow string/numeric variable type mismatch without error Results assert(results) keep(results) specify required match results specify which match results to keep sorted do not sort; dataset already sorted sorted does not appear in the dialog box. 1 2 merge — Merge datasets Menu Data > Combine datasets > Merge two datasets Description merge joins corresponding observations from...
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...A Handbook of Statistical Analyses using SAS SECOND EDITION Geoff Der Statistician MRC Social and Public Health Sciences Unit University of Glasgow Glasgow, Scotland and Brian S. Everitt Professor of Statistics in Behavioural Science Institute of Psychiatry University of London London, U.K. CHAPMAN & HALL/CRC Boca Raton London New York Washington, D.C. Library of Congress Cataloging-in-Publication Data Catalog record is available from the Library of Congress This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431. Trademark Notice:...
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...different variables. The 5 different variables are broken down by location, income, household size, years in current location, and credit balance. I plan on creating different graphs and tables to make things easier to study to see the difference in groups. 1st individual variable The pie chart above clearly shows that most/max of the customers to buy from AJ Davis live in the urban location. Even though the rural location is the min of the three, rural and suburban is very close to each other that they both make-up for the rest of the sales. 2nd individual variable The histogram above shows how many people fall into the different income ranges. It seems that the middle class has the lowest amount of people in that category where the lower and upper class mirror each other in the chart above. 3rd individual variable The dotplot above shows how many families fall into the category of how many people are in the household size. It looks as if the families with two people is the largest group out of everyone by a long shot, and households with one, five, six leaving there are equal. 1st pairing of variables The boxplot above shows the connection between the three different locations and the credit balance with AJ Davis. The Rural location seems to be the group that doesn’t hold the highest credit balance which could tie into the fact rural has the lowest income level. Rural max is very close to urban and suburban min. 2nd pairing variable ...
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...Discriminant analysis The discriminant analysis model involves linear combinations of the following form D=b0+b1X1+b2X2+…………………+bkXk The weights b’s are estimated so that the groups differ as much as possible on the values of the discriminant function. Xi is the predictor or independent variable. The data we have used is bankloan.sav. Since the dependent variable is “previously defaulted” which has nominal values as 0 for ‘NO’ and 1 for ‘YES’, we need to use discriminant analysis. The independent variables or the predictors that is used are “Age in years”, “Level of education”, Years with current employees”, House hold incomes”, “Debt to income ratio”, “Credit card debt”, “Years at current address” and “other debts”. The total number of data available for us is 800. The tables shown below is the discriminant analysis that is done on this data Analysis Case Processing Summary | Unweighted Cases | N | Percent | Valid | 700 | 82.4 | Excluded | Missing or out-of-range group codes | 150 | 17.6 | | At least one missing discriminating variable | 0 | .0 | | Both missing or out-of-range group codes and at least one missing discriminating variable | 0 | .0 | | Total | 150 | 17.6 | Total | 850 | 100.0 | Group Statistics | Previously defaulted | Mean | Std. Deviation | Valid N (listwise) | | | | Unweighted | Weighted | No | Age in years | 35.5145 | 7.70774 | 517 | 517.000 | | Years with current employer | 9.5087 | 6.66374 | 517 | 517.000...
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...Dave’s Crash Course in Statistics using SPSS 1.0 Classifying the different types of data There are four types of variables: nominal, ordinal, interval and ratio. Distinguishing between these types of variables is important, as several statistical tools may only be used for certain types of data. Nominal variables: where values are assigned to categories in no particular order. This assignment of values is arbitrary and holds no particular meaning or order to them. For example, “sex” where 1=male 2=female “marital status” where 1=never married 2= married 3=defacto “yes/no type questions” where 1=yes 2=no. Ordinal variables: where values are assigned to categories that are related to each other in some logical order – such as ascending or descending order. For example, “age group” 1=under 21yrs 2=21-35yrs 3=35-49yrs 4=50 yrs and over “education” where 1=high school completed 2=tertiary studies completed 3=post-graduate studies completed. The higher the value assigned, the higher the category (ie. higher age group or education level). Interval variables: where the values assigned are ordered in the same way as ordinal variables, however, the intervals or distances between the categories are equally spaced. For example, “please rate the importance of the following attributes…” according to the scale 1----------2----------3----------4----------5 where 1=strongly...
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...years. There is no place in the world free with this type of products. The modernization and the globalization make the counterfeiting process more difficult to control, affecting not only the countries economy, but also its safety and the citizens’ general integrity. Knowing the importance of this problem, different organizations around the world are making a big effort trying to create a variety of countermeasures in order to stop the development of this sector and of course the negative consequences. In order to create effective measures, it is necessary to completely understand the counterfeiting process and the specific reasons why people acquire counterfeit products. The objective of this study is to describe what are the different variables, and their direct influence on the purchaser’s behavior at the moment of buying counterfeit merchandise. The data collection method is based on focus groups, interviews and questionnaires. The results obtained from this study can help to understand the complete counterfeit process and facilitate future studies in order to created adequate countermeasures against this global issue. Why do people buy counterfeit products? 3 PURPOSE STATEMENT Counterfeit is a serious issue that affects individuals, cities and countries. The world as a whole is being touched by this powerful industry. The free trading of these products is...
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...and faculty were asked to complete a survey to help determine if there is enough demand and how cost points may affect that demand. The demand and forecast for pizza will determine if it is justified to move forward with the proposed joint venture. When completing a demand analysis it is important to test and review the proper demographic and independent variables. In the proposed University-Domino’s joint venture the surveys collected many data points. The demographic variables included students age, number of people in the household, and approximate household income. The independent variables included number of fast food restaurants passed, distance of commute, whether the student is coming to class straight from work, number of times fast food is eaten weekly, amount spent when eating fast food, and if a drink was purchased. The demand function, also known as the curve will specify the relationship between the various price points of pizza and quantity demanded. It is also important justify the reasons certain variables are chosen. The first demographic variable is student’s age. This is an important variable because it will...
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...NHL Regression Analysis Write-Up 1. I chose the topic because I am a big hockey fan, namely the Los Angeles Kings, who I have been following for quite a while. I am trying to find out whether the amount of goals scored, the number of minutes penalized and a Canadian nationality correlates to a player's salary amount. 2. I think my model will predict estimates of the conditional expectation of my dependent variable (annual salary), given the independent variables (goals, PIM & Canadian nationality). I expect the b for x1 (goals) to be positive as the more goals one scores, it warrants higher skill, thus higher pay. I expect the b value for x2 (PIM) to be negative as an increase of penalty minutes translates into less playing time which can result in lesser opportunities to score/assist on plays. Also, players that accumulate more penalty minutes are more likely to play the role of "enforcer" than "goal scorer", thus are historically paid less than most players. I expect the b value of x3 (Canadian Nationality) to be positive as most players in the National Hockey League are of Canadian nationality. 3. The coefficient parameter for annual salary, goals, penalty minutes and nationality are as respectively follows: 39058.28, -10044.7929 and -354824.588. B0: This intercept literally means if goals and PIM were 0 while nationality was not considered at all, that annual salary is 39058.28. That would not be sufficient. We are relatively more interested in the change of the salary...
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...how many credits are needed to graduate. Display should include the student name and the number of credits left to graduate. This should be based off a 90 credit program, where some courses are half credits. Step 1: This program is most easily solved using just a few variables. Identify potential problems with the following variables declared in the pseudocode. Assume that the college has the ability to offer half credits. (Reference: Variable Names, page 39-40). |Variable Name |Problem (Yes or No) |If Yes, what’s wrong? | |Declare Real creditsTaken |n | | |Declare Int creditsLeft |y | | |Declare Real studentName |y | | |Constant Real credits Needed = 90 |y | | Step 2: What is wrong with the following calculation? (Reference: Variable Assignment and Calculations, page 43). Set creditsLeft = creditsTaken –...
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...1Z0-007, 200 ♦ CompTIA: 220-601 ♦ SUN: 310-014, 310-044 ♦ Citrix: 1Y0-A01, 1Y0-256 ♦ CIW: 1D0-420 ♦ Novell: 50-686 ♦ Adobe: 9A0-029 ♦ Apple: 9L0-005, 9L0-505 ♦ Avaya: 132-S-100 ♦ Cognos: COG-105 ♦ CWNP: PW0-100 ♦ EMC: E20-001 ♦ Hyperion: 4H0-002 ♦ HP: HP0-771, HP0-J24 ♦ IBM: 000-253, 000-700 ♦ Juniper: JN0-100, JN0-201 ♦ Lotus: LOT-739 ♦ Nortel: 920-803 ♦ SAS: A00-201 ♦ SNIA: S10-100 ♦ Sybase: 510-015 ♦ Symantec: 250-101 ♦ TeraData: NR0-011 For pricing and placing order, please visit http://certificationking.com/order.html We accept all major credit cards through www.paypal.com For other payment options and any further query, feel free to mail us at info@certificationking.com Exam A QUESTION 1 The SAS data set SASUSER.HOUSES contains a variable PRICE which has been assigned a permanent label of "Asking Price". Which SAS program temporarily replaces the label "Asking Price" with the label "Sale Price" in the output? A. proc print data = sasuser.houses; label price = "Sale Price"; run; B. proc print data = sasuser.houses label; label price "Sale Price"; run; C. proc print data =...
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...Day, Year QUALITIES AND SELECTION OF VARIABLES - The research variables are the important aspect which is considered for a research proposal. With the help of the research variables, the hypothesis is been prepared effectively from the variables. For preparing a good research variables there are different variables which match the research to make it effective in the process. There are attributes and values of the variables which can be changed accordingly. The flexibility is one of the qualities which a variable research consists and that is the most important consent while setting the research hypothesis. Two or more variables are needed to complete the research proposal. The variables have mutually exclusive attributes which occur at different time and are not selective for instance a person can select one alternative from the given options according to the study (Denise & Beck, 2004). This is helpful to prepare a research proposal as it may not take long time to conclude the research proposal. The variables are the factors which can be measured and they vary as per the name suggests. The variables improve the research proposal because they are the main base for the research which has to be taken for a particular business or a company. Without the help of variables, the research cannot be possible as the hypothesis and the methods are base upon the variables (Burns & Susan, 2007). There are different kinds of variables which are to be selected according to the...
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...quantitative variables. Give an example of qualitative and quantitative variables. Qualitative variables are nonnumeric and categorical. A quantitative variable is the information is reported numerically. Quantitative variable when the variable studied indicates how many and how much. Example of qualitative is a type of car and for the quantitative variables is the number of runner in the marathon. 6. Explain the difference between a sample and a population Population is the entire of individuals or objects of interest or the measurements obtained from all individuals or objects of interest. A sample taken from population, so that’s mean the sample is a proportion. 7. Explain the difference between a discrete and a continuous. Give an example of each not included in the text. Discrete variables can assume only certain values, and there are gap between values. There is no decimal in the discrete variables. Example of discrete variables is the number of students who obtained grade A in English. Continuous variables can be in decimal point. An example of continuous variables is the amount of currency exchange between two countries. a. the name of their cell phone provider ( AT&T, Verizon and so on)- the level of scale for this data is NOMINAL because it is categorically divide, there is no particular order to the name of their cell phone provider. b. the numbers of minutes used last month (200, 400, for example)- the level of measurement for this variables is RATIO...
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