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Submitted By Felicialiew87

Words 770

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Words 770

Pages 4

This paper describes the application of regression analysis for the workplace. Three sets of variables are investigated - benefits and intrinsic job satisfaction, benefits and extrinsic job satisfaction, and finally benefits and overall job satisfaction. The regression analysis is performed using Excel and the results are shown in this paper, along with a graph for each set. The results are analyzed for recommendation to the company. Introduction

Regression analysis is performed on three sets of variables – benefits and intrinsic job satisfaction, benefits and extrinsic job satisfaction, and finally benefits and overall job satisfaction. The results of the regression analysis are used to determine whether any relationship exists for the three sets of variables and the strength of the relationship.

Benefits and Intrinsic Job Satisfaction

Regression output from Excel

Regression Statistics

Multiple R 0.069642247

R Square 0.004850043

Adjusted R Square -0.004718707

Standard Error 0.893876875

Observations 106

ANOVA df SS MS F Significance F

Regression 1 0.404991362 0.404991 0.506863 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 4.79E-28 4.784887914 6.227496 4.784888 6.227496

Benefits -0.057165607 0.080295211 -0.71194 0.478094 -0.216394019 0.102063 -0.21639 0.102063

Graph Benefits and Extrinsic Job Satisfaction

Regression output from Excel

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…...

...electricity, property taxes, advertising, accounting, janitors, cleaning supplies, distribution costs, legal fees, interest, inspectors, human resources department, etc, etc, etc. Life would be too easy if it were just that simple. There is one wrinkle. There is a distinction between between overhead and manufacturing overhead. Factory Overhead is not a financial statement account It is a “suspense account” for capturing and reallocating overhead costs Factory Overhead is debited for actual overhead costs incurred Factory Overhead is credited to allocate overhead to production Regression Analysis Interpretation of output summary The regression model like that, Here, Y= Cost of production A= Constant b1,b2 &b3= Regression coefficient X1= Direct Materials X2 = Direct Labor X3= Factory overhead From the co-efficient table, the values of a, b1,b2& b3 are found out & the regression model can be written as follows: Y= a+b1x1+b2x2+b3x3 = -6537089.828+.248×1+38.489×2+12.326×3 This equation indicates that if taka of direct materials increases by 1taka, the cost production will increases by .248 taka and other things remain constant. Again, if taka of direct labor increases by 1 taka, the cost of production will increases by 38.489 taka and other things remain constant. On the other hand, if taka of factory overhead increases by 1taka, the cost of production will increases by 12.326 taka and other things remain constant. The relationship among......

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...Ben Leigh American 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......

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...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. Introduction It is a common observation for many countries that unemployment rates and crime rates are positively associated. A more contentious issue is whether this association means that unemployment causes crime, crime causes unemployment or third factors cause both. Only the first of the three possibilities would imply that the effects of unemployment on crime deserve to be counted among the “non-pecuniary” costs of unemployment that should be taken into account in a cost-benefit analysis of potential unemployment-reducing policies. The theoretical underpinning of the causality notion was developed some thirty years ago by Becker (1968), Stigler (1970) and Ehrlich (1973), among others. In Ehrlich’s model, individuals divide their time between legal activities and risky illegal activities. If legal income opportunities become scarce relative to potential gains from crime, the model predicts that crime will become more frequent. Increased unemployment could be one such factor. Numerous subsequent empirical papers have attempted to test the predictions of the BeckerEhrlich model and to find out whether the magnitude of the unemployment effect is quantitatively important. The hallmark of this literature is its failure......

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...Unit 5 – Regression Analysis American InterContinental University Abstract In this scenario, Microsoft Excel has been utilized in order to perform a regression analysis therefore; each one has a chart in order to show the correlations in the data. However, satisfaction: overall, intrinsic, and extrinsic had been used. Introduction An analysis has been given to employees for the benefits satisfaction and compared to three different job types such intrinsic, extrinsic, as well as the over all. However, the regression analysis that was performed had been done in excel as well as there were charts made up. Benefits and Intrinsic Job Satisfaction Regression output from Excel Regression Statistics Multiple R 0.022301 R Square 0.000497 Adjusted R Square -0.0093 Standard Error 0.656922 Observations 104 ANOVA df SS MS F Significance F Regression 1 0.021902 0.021902 0.050753 0.822209 Residual 102 44.01771 0.431546 Total 103 44.03962 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 5.270871 0.348709 15.11541 8.66E-28 4.579209 5.962532 4.579209 5.962532 X Variable 1 0.017947 0.079664 0.225284 0.822209 -0.14007 0.175959 -0.14007 0.175959 It did not want to add my 2 to the answer of 5.962532 or did it add the 9 to the answer of 0.175959 Graph Benefits and......

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...There was a Pentium microprocessor flaw known as the FDIV bug. The bug is a flaw in the Intel P5 Pentium floating point unit. The bug causes the processor to return incorrect results for many calculations in math and science. Intel claims that it was a problem on a few missing entries in the lookup table used by the company. The flaw was rarely encountered by users. It was discovered by Professor Thomas Nicely, a professor of mathematics at Lynchburg College. He noticed the bug when he had written a code and noticed some inconsistencies in calculations once he added the Pentium system to his computers. He discovered this issue in June of 1994 but was unable to eliminate other factors until October of 1994. He reported the problem to Intel and they admitted that they were aware of the bug since May of 1994. Intel acknowledged the flaw, but claimed that it was not serious and would not affect most users. They offered to replace the processors to customers who could prove that they were affected by the flaw. This response did not make the public happy. They later decided to offer to replace all flawed Pentium processors on request of the customer. It turned out that only a fraction of the Pentium processor owners bothered to get their chips replaced. I believe that Intel did handle this problem in a professional manner and gave the people reasonable options to get their chips replaced. I think that the chip did not affect most people and had little or no effect to...

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...Unit 6 Research Paper 1 Network Attached Storage ITT Tech Network attached storage (NAS) is basically a server that acts as hard drive that’s attached to a network. This connection to a network allows anyone who is also connected to the network the ability to access the hard drive. The NAS come in many sizes and different capabilities. The network connection that the NAS uses can be almost any that are available. The standard Ethernet connection is the most common connection by there are also systems that use a wireless connection or even a fiber optic connection for extremely fast transfer speeds. The storage of NAS systems can vary greatly. There are consumer models with a fixed amount of storage, and then the professional versions that have the capacity of many terabytes that can be upgraded when new drives become available. With the numerous amount of drives the chance of corrupted data and errors is always a possibility. With this possibility manufacturers have included the ability to arrange the drive into a RAID. The different RAID configurations allow users the protection from errors and data loss at the expense of storage space. NAS, unlike normal servers, do not require a keyboard, mouse, or monitor. They are usually managed from a remote terminal accessed via the NAS network connection. Some NAS systems use a web browser interface that uses the user’s web browser to interact with the NAS. Other systems use software that the manufacturer......

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...Acts 430 Regression Analysis In this project, we are required to forecast number of houses sold in the United States by creating a regression analysis using the SAS program. We initially find out the dependent variable which known as HSN1F. 30-yr conventional Mortgage rate, real import of good and money stock, these three different kinds of data we considered as independent variables, which can be seen as the factors will impact the market of house sold in USA. Intuitively, we thought 30-yr conventional mortgage rate is a significant factor that will influences our behavior in house sold market, which has a negative relation with number of house sold. When mortgage rate increases, which means people are paying relatively more to buy a house, which will leads to a decrease tendency in house sold market. By contrast, a lower interest rate would impulse the market. We believe that real import good and service is another factor that will causes up and down in house sold market. When a large amount of goods and services imported by a country, that means we give out a lot of money to other country. In other words, people have less money, the sales of houses decreased. Otherwise, less import of goods and services indicates an increase tendency in house sold market. We can see it also has a negative relationship with the number of house sold. Lastly, we have money stock as our third impact factor of house sold. We considered it has a positive relationship with the number of...

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...There was a Pentium microprocessor flaw known as the FDIV bug. The bug is a flaw in the Intel P5 Pentium floating point unit. The bug causes the processor to return incorrect results for many calculations in math and science. Intel claims that it was a problem on a few missing entries in the lookup table used by the company. The flaw was rarely encountered by users. It was discovered by Professor Thomas Nicely, a professor of mathematics at Lynchburg College noticed the bug when he had written a code and noticed some inconsistencies in calculations once he added the Pentium system to his computers. Professor Thomas Nicely discovered this issue in June of 1994 but was unable to eliminate other factors until October of 1994. He reported the problem to Intel and they admitted that they were aware of the bug since May of 1994. Intel acknowledged the flaw, but claimed that it was not serious and would not affect most users. They offered to replace the processors to customers who could prove that they were affected by the flaw. This response did not make the public happy. They later decided to offer to replace all flawed Pentium processors on request of the customer. It turned out that only a fraction of the Pentium processor owners bothered to get their chips replaced. It is believed that Intel did handle the problem in a professional manner and gave the people reasonable options to get their chips replaced. I think that the chip did not affect most people and had little or no...

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...Regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Local Government Engineering Department (LGED) is a public sector organization under the ministry of Local Government, Rural Development & Cooperatives. The prime mandate of LGED is to plan, develop and maintain local level rural, urban and small scale water resources infrastructure throughout the country. Here, I considered LGED as the organization and considering a projects eight districts “available fund” as Independent variable and “development (length of development of road in km)” as dependent variable. The value of the variables are- Districts Fund, X (lakh tk) Development,Y (km) Panchagar 450 10 Thakurgaon 310 6.8 Dinajpur 1500 32 Nilphamari 1160 24.5 Rangpur 1450 31 Kurigram 450 9 Lalmonirhat 950 16 Gaibandha 1550 33 For the two variables “available fund” and “development”, the regression equation can be given as: Y= a + bX Where, Y = Development X = Fund b = rate of change of development a...

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...Unit 5 Regression Analysis American Intercontinental University Regression Analysis Independent Variable: Benefits Dependent Variable: Intrinsic Regression Statistics | | Multiple R | 0.252916544 | R Square | 0.063966778 | Adjusted R Square | 0.045966139 | Standard Error | 0.390066747 | Observations | 54 | ANOVA | | | | | | | df | SS | MS | F | Significance F | Regression | 1 | 0.540685116 | 0.540685116 | 3.553583771 | 0.065010363 | Residual | 52 | 7.911907477 | 0.152152067 | | | Total | 53 | 8.452592593 | | | | | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | Intercept | 4.88865703 | 0.188506099 | 25.93368096 | 2.04938E-31 | 4.510391881 | 5.266922187 | 4.510391881 | 5.266922187 | 1.4 | 0.06958624 | 0.036913916 | 1.885095162 | 0.065010363 | -0.004486945 | 0.143659433 | -0.004486945 | 0.143659433 | Independent Variable: Benefits Dependent Variable: Extrinsic Regression Statistics | | Multiple R | 0.332749251 | R Square | 0.110722064 | Adjusted R Square | 0.093620565 | Standard Error | 0.405766266 | Observations | 54 | ANOVA | | | | | | | df | SS | MS | F | Significance F | Regression | 1 | 1.065986925 | 1.065987 | 6.474407048 | 0.013952455 | Residual | 52 | 8.561605668 | 0.164646 | | | Total | 53 | 9.627592593 | | | | | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower......

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...Unit 5 – Regression Analysis American InterContinental University Abstract When comparing intrinsic, extrinsic, and overall job satisfaction to which will benefits employees more and have a better result with the satisfaction between the company and the employees to become a successful team. All calculation would be on Excel to determine the regression analysis and graphs the correlation between the all three Introduction When company needs to determine what will work with having happier employees, companies’ uses correlation statistics to help determine which variable value works best. Correlations can be either positive variable value or negative variable value. Using charts and analysis can be useful to determine the results. Regression analysis shows the strengths and weakness of different variables and can help making a decision on which is the strongest variable. Benefits and Intrinsic Job Satisfaction Regression output from Excel [pic] Graph [pic] Benefits and Extrinsic Job Satisfaction Regression output from Excel [pic] Graph [pic] Benefits and Overall Job Satisfaction Regression output from Excel [pic] Graph [pic] Key components of the regression analysis Complete the following chart to identify key components of each regression output. |Dependent Variable |Slope |Y-intercept |Equation |[pic] | |Intrinsic |0.056 ...

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... Case Study: Locating New Pam and Susan‘s Stores Professor Demetra Paparounas Lisa Chan MGSC 6200- Information Analysis July 3, 2014 Introduction The purpose of this study to is to determine a new store location for Pam and Susan Stores. This discount department store chain has 250 stores that are primarily in the South. Expansion is important to their strategic success. A multiple regression model will be used to determine which location has the highest sales potential and projections. It will also be used to help see how strong of a relationship sales has to the other independent variables. Data For this model, the wealth of census data that was used to compute this model contained 250 observations, 33 variables and 7 additional dummy variables were created from the main comtype variable, taking values of zero or one depending on level of competitiveness for a particular store. This data set contained economic and demographical data, population type, sales numbers, store size and the competitive types. The amount of sales and selling square feet variables are given in thousands of dollars. Results and Discussions In analyzing the data on the 250 Pam and Susan’s stores, we first created a scatter plot of the competitive types in the horizontal axis against sales (in thousands) on the vertical axis. The competitive types were identified as follows: * Type 1- Densely populated area with relatively little direct competition. * Type 2 –High income areas with little......

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...In this document, there will be discussions and data showing the regression analysis. There are charts and graphs show the regression analysis using intrinsic, extrinsic dependent variable and benefits as the independent variable. Benefits and overall job satisfaction is discussed and represented in the charts, graphs and data. Introduction There is data, charts and graphs representing job satisfaction of Intrinsic, Extrinsic and overall. There are discussions on the slop, y-intercept, equation and r^2 using intrinsic, extrinsic and overall components of each regression output. Benefits and Intrinsic Job Satisfaction Regression output from Excel Benefits Intrinsic 5.4 5.5 6.2 5.2 2.3 5.3 4.5 4.7 5.4 5.5 6.2 5.2 2.3 2.1 4.5 4.7 5.4 5.4 6.2 6.2 6.2 5.2 2.3 5.3 4.5 4.7 5.4 5.4 6.2 5.5 6.2 5.2 5.4 5.3 6.2 4.7 2.3 5.5 2.3 4.7 4.5 5.3 2.3 4.7 4.5 4.7 5.4 5.5 6.2 5.2 2.3 2.1 4.5 4.7 5.4 5.4 6.2 6.2 2.3 5.2 4.5 5.3 5.4 4.7 6.2 5.4 6.2 6.2 4.5 5.2 5.4 5.3 6.2 4.7 2.3 5.2 4.5 5.3 5.4 5.3 SUMMARY OUTPUT Regression Statistics Multiple R 0.468795174 R Square 0.219768915 Adjusted R Square 0.199236518 Standard Error 0.713005621 Observations 40 ANOVA df SS MS F Significance F Regression 1 5.44142339 5.44142339 10.70352 0.002279584 Residual 38 19.31832661 0.508377016 Total 39 24.75975 Coefficients Standard Error t......

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... | LETTER OF TRANSMITTAL April 12, 2012 Dr. Abul Kalam Azad Associate Professor Department of Marketing University Of Dhaka Subject: Submission of a Report on regression analysis Dear Sir, Here is our term paper on regression analysis that you have assigned us to submit as a partial requirement for the course –“Business Statistics 1” Code no-212.While preparing this term paper; we have taken help from internet, books, class lectures and relevant sources. Though we have tried best yet it may contain some unintentional errors. We hope, this term paper will come up with your expectation. We shall be glad to answer any kind of question related to this term paper and we shall be glad to provide further clarification if needed. Yours faithfully Group: ''Oracles'' Section: B 17thBatch, Department Of Marketing University of Dhaka. ACKNOWLEDGEMENT For the completion of this task, we can’t deserve all praise. There were a lot of people who helped us by providing valuable information, advice and guidance. Course report is an important part of BBA program as one can gather practical knowledge within the short period of time by observing and doing this type of task. In this regard our report has been prepared on ‘regression analyses. At first we would like to thank Almighty .Then to our course teacher for giving us the assignment helping the course as well as for his valuable guidelines. Last but not the least the......

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...Unit 5 – Regression Analysis Jessica Laux/Bakos American InterContinental University Abstract Data regression and charting are important parts of interpreting data. If one uses scatter plots, and data analysis, one can determine if a correlation exists between two data sets, or if there is actually very little. This can help when it comes to seeing for example, if job satisfaction overall is related to benefits, and if so how to change that in the favor of the business. Introduction In the following information, we will show regression outputs for data sets from the AIU data set. We will determine correlation and what it means, as well as show scatter graphs that can help determine if there is any correlation to be shown. One has to be careful to input the proper data if they want the analysis to come out correctly. Benefits and Intrinsic Job Satisfaction Regression output from Excel |SUMMARY OUTPUT | | | | | |Intrinsic |0.326704508 |3.438142011 |Y=0.0034x+4.5491 |0.0012 | |Extrinsic |-0.134516538 |6.034361553 |Y=1.6912x+13.859 |0.2275 | |Overall |0.101037811 |4.712869316 |Y=1.0105x+0.5195 |0.1021 | Similarities and......

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