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Team

RES/342 Research and Evaluation

Teacher

Date

The Hypothesis

Team C’s hypothesis is that the more years of education one receives the more a person can potentially earn in salary. The team will use the process of linear regression analysis to explain how the information is used and conduct a five-step test to see if the hypothesis proves true or false.

Linear Regression Analysis Team C’s purpose of this research paper is to use a linear regression analysis test to determine if a significant linear relationship exists between an independent variable which is X, level or years of education, and a dependent variable Y, salaries earned or potentially earned. “It is used to determine the extent to which there is a linear relationship between a dependent variable and one or more independent variables,” (Statistically Significant Consulting, 2010, para. 1). Learning Team C will use the salary and education levels from the Wages and Wage Earners Data Set collected through access to the e-source link of University of Phoenix. For this test the dependent variable, Y, will represent the salary of the 100 participants and the independent variable, X, will represent the education of the 100 participants.

How the Information is used

This information will be used in a linear regression test to see if there is enough evidence to reject the null hypothesis that a higher education does not equal a difference in salary. This test will research and analyze the earnings of workers based on the years of education they have received to see if the slope equals zero. If the slope equals zero, then Team C will not be able to reject the null hypothesis. This week, Learning Team C will use linear regression to determine if a significant linear relationship between the two variables actually…...

...Running head: REGRESSION Regression Names RES/342 - Research and Evaluation II Date Professor Table of contents Executive Summary 3 Dataset 3 Data Observations 3 Statistical Analysis 4 Conclusion 4 Dataset for the 2004 season 5 Regression Analysis taking LOG (Y) 6 Regression Analysis 8 Executive Summary This report is to determine whether total team payroll for major league baseball teams directly varies with each team’s home attendance. This is an important statistical analysis because if we can prove that there is a relationship between salary and attendance then we can see that more fans in the stands will give a team more buying power when it comes to signing players. Dataset The independent variable is team payroll and the dependent variable is team home attendance. Each team plays 81 home games. The dataset consists of 30 Major League Baseball teams from the 2004 season. Data Observations For the independent variable: The arithmetic mean for home attendance is: 30,453.67 The median for total home attendance is: 31,499.50 The standard deviation for total home attendance is: 8,132.28139 The minimum for total home attendance is: 14,052 The maximum for total home attendance is: 50,499 For the dependent variable: The arithmetic mean for total payroll is: $73,052,363.27 The......

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...good can be something unimpressive, ordinary, prosaic, boring, and even worse for your teacher. In order to avoid any problems with your work, you can have the best professionals in the field perform the task for you. In order to get this help, just submit an order as it described in the scheme below and the team of essaysReasy.com writers will write a truly good essay for you. Usually, your ideas are what the teachers value the most. Your opinion concerning the topic is what makes a great essay . There is no such a thing as bad opinion. Opinion might be wrong, although if you support your every word with historical/critical/theoretical evidence from any other source, your theory, even the craziest one, will have the right to exist on paper because otherwise it will be a fantasy, not an essay. If you decided to follow the easiest and the shortest path – go to the library and grab every book you see (not literary! Use your book list your teacher was supposed to give you at the beginning of the semester, or particularly for this assignment, - most likely he/she wants you to be guided by them, or you can follow advices of librarians if some books are not available, or you are desperate to find fresh ideas in those sources). When you are sitting and reading everything you could find, start forming your opinion, take notes and write down possible quotations. It is better if you can come up with an opinion on a primary source, not just rewriting somebody's ideas from a critic......

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...Regression Models Student Name Grantham University BA/520 – Quantitative Analysis Instructor Name April 6, 2013 Abstract This paper will refer to regression models and the benefits that variables provide when developing and examining such models. Also, it will discuss the reason why scatter diagrams are used and will describe the simple linear regression model and will refer to multiple regression analysis as well as the potential uses for this type of model. Regression Models Regression models are a statistical measure that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables). Regression models provide the scientist with a powerful tool, allowing predictions about past, present, or future events to be made with information about past or present events. Inference based on such models is known as regression analysis. The main purpose of regression analysis is to predict the value of a dependent or response variable based on values of the independent or explanatory variables. According to Render, Stair, and Hanna (2011) they are two reasons for which regression analyses are used: one is to understand the relation between various variables and the second is to predict the variable's value based on the value of the other. Variables provide many advantages when creating models. One of the......

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...Intercontinental University Unit 5 Individual Project BUSN311-1301B-10: Quantitative Methods and Analysis Instructor Leonidas Murembya April 23, 2013, Abstract This paper will be discussing regression analysis using AIU’s survey responses from the AIU data set in order to complete a regression analysis for benefits & intrinsic, benefits & extrinsic and benefit and overall job satisfaction. Plus giving an overview of these regressions along with what it would mean to a manager (AIU Online). Introduction Regression analysis can help us predict how the needs of a company are changing and where the greatest need will be. That allows companies to hire employees they need before they are needed so they are not caught in a lurch. Our regression analysis looks at comparing two factors only, an independent variable and dependent variable (Murembya, 2013). Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.018314784 R Square 0.000335431 The portion of the relations explained Adjusted R Square -0.009865228 by the line 0.00033% of relation is Standard Error 1.197079687 Linear. Observations 100 ANOVA df SS MS F Significance F Regression 1 0.04712176 0.047122 0.032883 0.856477174 Residual 98 140.4339782 1.433 Total 99 140.4811 Coefficients Standard Error t Stat P-value Lower......

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...Regression Analysis: Basic Concepts Allin Cottrell∗ 1 The simple linear model Suppose we reckon that some variable of interest, y, is ‘driven by’ some other variable x. We then call y the dependent variable and x the independent variable. In addition, suppose that the relationship between y and x is basically linear, but is inexact: besides its determination by x, y has a random component, u, which we call the ‘disturbance’ or ‘error’. Let i index the observations on the data pairs (x, y). The simple linear model formalizes the ideas just stated: yi = β0 + β1 xi + ui The parameters β0 and β1 represent the y-intercept and the slope of the relationship, respectively. In order to work with this model we need to make some assumptions about the behavior of the error term. For now we’ll assume three things: E(ui ) = 0 2 2 E(ui ) = σu E(ui u j ) = 0, i = j u has a mean of zero for all i it has the same variance for all i no correlation across observations We’ll see later how to check whether these assumptions are met, and also what resources we have for dealing with a situation where they’re not met. We have just made a bunch of assumptions about what is ‘really going on’ between y and x, but we’d like to put numbers on the parameters βo and β1 . Well, suppose we’re able to gather a sample of data on x and y. The task ˆ of estimation is then to come up with coefﬁcients—numbers that we can calculate from the data, call them β0 and ˆ1 —which serve as estimates of the unknown......

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...Unemployment and crime: New evidence for an old question Kerry L. Papps Victoria University of Wellington Rainer Winkelmann* IZA and Centre for Economic Policy Research, London December 1999 This paper uses panel data techniques to examine the relationship between unemployment and a range of categories of crime in New Zealand. The data cover sixteen regions over the period 1984 to 1996. Random and fixed effects models are estimated to investigate the possibility of a causal relationship between unemployment and crime. Hypothesis tests show that two-way fixed effects models should be used. The main result of the paper is that there is some evidence of significant effects of unemployment on crime, both for total crime and for some subcategories of crime. We are grateful to Rachel Bambery, New Zealand Police National Headquarters, for her assistance in obtaining crime and population statistics. The staff of the University of Canterbury Library also gave invaluable help in unraveling the complexities of New Zealand unemployment and income data. The paper has benefited from useful comments by two anonymous referees, Simon Kemp, Jacques Poot and participants of the CEPR conference on “Metropolitan Economic Performance”, Lisbon, October 1998. *Corresponding author: IZA, P.O. Box 7240, 53072 Bonn, Germany; winkelmann@iza.org. “I know only of three ways of living in society: one must be a beggar, a thief, or a wage earner.” HONORÉ de MIRABEAU (1749-1791) 1.......

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...Q1: All the regressions were performed. Output can be made available if needed. See outputs for Q2 in appendix. Q2: Select the model you are going to keep for each brand and explain WHY. Report the corresponding output in an appendix attached to your report (hence, 1 output per brand) We use Adjusted R Squared to compare the Linear or Semilog Regression. R^2 is a statistic that will give some information about the goodness of fit of a model. In regression, the Adjusted R^2 coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R2 of 1 indicates that the regression line perfectly fits the data. Brand1: Linear Regression R^2 | 0.594 | SemiLog Regression R^2 | 0.563 | We use the Linear Regression Model since R-squared is higher. Brand 2: Linear Regression R^2 | 0.758 | SemiLog Regression R^2 | 0.588 | We use the Linear Regression Model since R-squared is higher Brand 3: Linear Regression R^2 | 0.352 | SemiLog Regression R^2 | 0.571 | We use the Semilog Regression Model since R-squared is higher Brand 4: Linear Regression R^2 | 0.864 | SemiLog Regression R^2 | 0.603 | We use the Linear Regression Model since R-squared is higher Q3: Here we compute the cross-price elasticity. Depending on whether we use linear or semi-log model, Linear Model Linear Model Semi-Log Model Semi-Log Model ` ...

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...Term Paper Requirements 1. There is no need for references unless you choose to use the text as a guide for interpretation of the data. 2. Paper must: • Have a cover page that is within APA guidelines • Have a header with student’s last name, page numbers, and be right justified • Be a minimum of 4 pages • Be single-spaced • Use 12-point font (Arial works best) • Have 1” margins • Detailed analysis of the data (see data specifics) • Use section headings for each part of the analysis (see suggested section headings) • Contain output tables 3. Options: • • Graphs or charts, if desired Running head 4. Data Specifics: • Describe the data o What is it that you are analyzing? o What do you intended to produce in the analysis? o What is your hypothesis statement? Includes Confidence Level and what it means Identify the dependent and independent variables o Explain why your choice is the dependent variable o Explain why the others are independent variables Analyze the data o What are the results of the first analysis? o Which variable, or variables, are statistically significant? Why? o Which variable, or variables, are not statistically significant? Why? o Is there a need to perform an additional analysis? Why? • • o What are the results of the second analysis? o Which variable, or variables, are statistically significant? Why? o Which variable, or variables, are not statistically significant? Why? o Is there a need to perform an additional analysis? Why? 5. Analysis......

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...ABSTRACT To reduce the effort, testing cycle time & % of human errors that can easily creep in while comparing the results of Regression Test Suite, a thought process was put into designing & implementing an Automation Framework for the purpose. A lot of work and research has already being done for the Execution phase of Regression Testing wherein two parallel sides – Test & Prod are setup & Test Cases executed by firing the same one after the another & results stored. A large number of Regression Automation Tools are available in market like, QTP, Selenium, WATIR etc, to cover this up. Contrary to this very less work is available & very less has been thought about the Comparison phase wherein Test Results thus generated have to be compared to produce a summary report for QA Testers to analyze which they can further categorize into Expected & Unexpected Breaks & then reach out to Development for investigation & thus complete the end-to-end life cycle of Regression Testing. With advent of IT and shift of focus toward Financial Banks & Institutions, a need is felt to have some faster & feasible way to compare records with high volume. That is the starting point for this paper under which an Automation Framework for Comparison Phase of Regression Testing is built in Perl, that could easily cover records of any volume. Use of Industry Compliant Methodology, named Best Match, made the framework even more flexible for scenarios having duplicate records on either of the two......

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...relationships between the variables. The relationships can either be negative or positive. This is told by whether the graph increases or decreases. Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.069642247 R Square 0.004850043 Adjusted R Square -0.00471871 Standard Error 0.893876875 Observations 106 ANOVA df SS MS F Significance F Regression 1 0.404991362 0.404991 0.50686 0.478094147 Residual 104 83.09765015 0.799016 Total 105 83.50264151 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 5.506191723 0.363736853 15.13784 4.8E-28 4.784887893 6.2274956 4.7848879 6.22749555 Benefits -0.05716561 0.080295211 -0.711943 0.47809 -0.21639402 0.1020628 -0.216394 0.10206281 Y=5.5062+-0.0572x Graph Benefits and Extrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.161906 R Square 0.026214 Adjusted R Square 0.01685 Standard Error 1.001305 Observations 106 ANOVA df SS MS F Significance F Regression 1 2.806919 2.806919 2.799606 0.097293 Residual 104 104.2717 1.002612 Total 105 107.0786 Coefficients Standard Error t Stat P-value Lower 95% Upper......

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...STATISTICS FOR ENGINEERS (EQT 373) TUTORIAL CHAPTER 3 – INTRODUCTORY LINEAR REGRESSION 1) Given 5 observations for two variables, x and y. | 3 | 12 | 6 | 20 | 14 | | 55 | 40 | 55 | 10 | 15 | a. Develop a scatter diagram for these data. b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? c. Develop the estimated regression equation by computing the values and. d. Use the estimated regression equation to predict the value of y when x=10. e. Compute the coefficient of determination. Comment on the goodness of fit. f. Compute the sample correlation coefficient (r) and explain the result. 2) The Tenaga Elektik MN Company is studying the relationship between kilowatt-hours (thousands) used and the number of room in a private single-family residence. A random sample of 10 homes yielded the following. Number of rooms | Kilowatt-Hours (thousands) | 12 9 14 6 10 8 10 10 5 7 | 9 7 10 5 8 6 8 10 4 7 | a. Identify the independent and dependent variable. b. Compute the coefficient of correlation and explain. c. Compute the coefficient of determination and explain. d. Test whether there is a positive correlation between both variables. Use α=0.05. e. Determine the regression equation (used Least Square method) f. Determine the value of kilowatt-hours used if number of rooms is 11. g. Can you use the model in (f.) to predict the kilowatt-hours if number of......

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...Regression Paper Introduction The purpose of regression analysis is to find out the values of parameters for a purpose that cause the purpose to best fit a set of selected data observations. The description of this linear regression test will be explained and analyzed in the paper. The data collected for various teams will help comparing the numbers with the anticipation of getting a reliable and comparable hypothesis test answer. Having enough data will give the test a fare chance to show the results needed for a positive outcome. Conclusion In finishing the regression analyses, team D can conclude that there seems to be a linear relationship between the salary affect of the performance players based on the win and losses. Team D formulated both verbal and numerical hypothesis statements on the salaries of Major League Baseball players. In addition to using the regression hypothesis test linear, the Team also studies the data given by the Major League Baseball player’s data. This analysis confirms that the mean salary for Major League Baseball players is considerably correlated to the team wins. The sample mean of team salaries and the sample mean of wins of our data set are prime variables. Using regression analysis played an important role in helping us answer the research question of if the salary of Major League Baseball players is connected to the wins of Major League Baseball teams. Reference Doane, D., & Seward, L. (2007). Applied Statistics in...

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...A) Estimated regression equation – First Order: y = β0 + β1x1 + β2x2 + ε Output of 1st Model | | | | | | | | | | | | | | Regression Statistics | | | | | | Multiple R | 0.763064634 | | | | | | R Square | 0.582267636 | SSR/SST | | ̂̂̂ | | | Adjusted R Square | 0.512645575 | | | | | | Standard Error | 547.737482 | | | | | | Observations | 15 | | | | | | | | | | | | | ANOVA | | | | | | | | df | SS | MS | F | Significance F | | Regression | 2 | 5018231.543 | 2509115.772 | 8.363263464 | 0.005313599 | | Residual | 12 | 3600196.19 | 300016.3492 | | | | Total | 14 | 8618427.733 | | | | | | | | | | | | | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Intercept | -20.35201243 | 652.7453202 | -0.031179101 | 0.975639286 | -1442.561891 | 1401.857866 | Age (x1) | 13.35044655 | 7.671676501 | 1.740225432 | 0.107375657 | -3.364700634 | 30.06559374 | Hours (x2) | 243.7144645 | 63.51173661 | 3.837313819 | 0.002363965 | 105.334278 | 382.0946511 | B) equation | ŷ= -20.3520124320994 + 13.3504465516772 x̂1 + 243.714464532425 x̂2 | C) Interpretation of β β̂1 = 13.35044655, If number of hours worked (x2) held fixed, we can estimate that every one-year increase in age (x1) the mean of annual earnings will increase by 13.35044655. β̂2 = 243.7144645, If age (X1) held fixed, we can estimate that every one hour (x2) of work increase, the mean of......

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...2013 Recent data highlight that competitive skiers face a high risk of injuries especially during off-balance jump landing maneuvers in downhill skiing. The purpose of the present study was to develop a musculo-skeletal modeling and simulation approach to investigate the cause-andeffect relationship between a perturbed landing position, i.e., joint angles and trunk orientation, and the peak force in the anterior cruciate ligament (ACL) during jump landing. A two-dimensional musculo-skeletal model was developed and a baseline simulation was obtained reproducing measurement data of a reference landing movement. Based on the baseline simulation, a series of perturbed landing simulations (n = 1000) was generated. Multiple linear regression was performed to determine a relationship between peak ACL force and the perturbed landing posture. Increased backward lean, hip ﬂexion, knee extension, and ankle dorsiﬂexion as well as an asymmetric position were related to higher peak ACL forces during jump landing. The orientation of the trunk of the skier was identiﬁed as the most important predictor accounting for 60% of the variance of the peak ACL force in the simulations. Teaching of tactical decisions and the inclusion of exercise regimens in ACL injury prevention programs to improve trunk control during landing motions in downhill skiing was concluded. In the last few years, injury data reported through the International Ski Federation Injury Surveillance......

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...A Term Paper On BUSINESS STATISTICS 1 Submitted To Dr. Md. Abul Kalam Azad Associate Professor Department of Marketing University of Dhaka Submitted By Group Name: “ORACLES” Section: B Department of Marketing (17th Batch) University of Dhaka Date of Submission: 12- 04-2012 Group profile “ORACLES” | Roll No. |NAME | |42 | Imran Hosen | | | | |74 |Zerin Momtaz Chowdhury | | | | |106 |Toufiqul Islam | | | ...

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