...Unit 5 – Regression Analysis Lakeia White American InterContinental University Abstract According to NLREG, “the goal of regression analysis is to determine the values of parameters for a function that cause the function to best fit a set of data observations that you provide.” (NLREG) As one continues to read one will find several different regression test that has been processed from AIU data set to assist them with their study on job satisfaction around the world. Introduction The following report contains the required data needed to find the regression analysis, there is three different test that has been processed regression analysis with benefits & intrinsic, regression analysis with benefits & extrinsic, and regression analysis with benefits & overall job satisfaction. As one continues to read one will find the ending results in each equation and how the results will benefit AIU. Benefits and Intrinsic Job Satisfaction Test #1: Regression Analysis-Benefits & Intrinsic: The line equation for the least square regression line is: y = 0.1697x + 4.4278 X = The independent variable which is Benefits’ Y = The corresponding dependent variable which is Intrinsic’ The slope (m) = 0.1697, and the intercept (b) = 4.4278 Therefore the Correlation Coefficient, r = 0.4061 and the Coefficient Determination, [pic] = 0.1649 [pic]. Benefits and Extrinsic Job Satisfaction Test #2: Regression Analysis-Benefits & Extrinsic: The equation for the Least...
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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 Stat...
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...Introduction Regression analysis was developed by Francis Galton in 1886 to determine the weight of mother/daughter sweet peas. Regression analysis is a parametric test used for the inference from a sample to a population. The goal of regression analysis is to investigate how effective one or more variables are in predicting the value of a dependent variable. In the following we conduct three simple regression analyses. Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.616038 R Square 0.379503 Adjusted R Square 0.371338 Standard Error 0.773609 Observations 78 ANOVA df SS MS F Significance F Regression 1 27.81836 27.81836 46.48237 1.93E-09 Residual 76 45.48382 0.598471 Total 77 73.30218 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2.897327 0.310671 9.326021 3.18E-14 2.278571 3.516082 2.278571 3.516082 X Variable 1 0.42507 0.062347 6.817798 1.93E-09 0.300895 0.549245 0.300895 0.549245 Graph Benefits and Extrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.516369 R Square 0.266637 Adjusted R Square 0.256987 Standard Error 0.35314 Observations 78 ANOVA ...
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...complete the following requirements in the form of a 3-page report: TEST #1: Regression Analysis- Benefits & Intrinsic Perform the following Regression Analysis, using a .05 significance level Run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the INTRINSIC job satisfaction column of all data points in the AIU data set as the dependent variable. Copy and paste the results of the output to your report in Microsoft Word. Create a graph with the trendline displayed the regression. Copy and paste the results of the output to your report in Microsoft Word. TEST #2: Regression Analysis- Benefits & Extrinsic Perform the following Regression Analysis, using a .05 significance level Run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the EXTRINSIC job satisfaction column of all data points in the AIU data set as the dependent variable. Copy and paste the results of the output to your report in Microsoft Word. Create a graph with the trendline displayed for the regression. Copy and paste the results of the output to your report in Microsoft Word. TEST #3: Regression Analysis- Benefits & Overall Job Satisfaction Perform the following Regression Analysis, using a .05 significance level Run a regression analysis using the BENEFITS column of all data points in the AIU data set as the independent variable and the...
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...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 ...
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...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 Stat P-value Intercept 3.866348351 0.385522375 10.02885592 3.15E-12 Benefits 0.254462373...
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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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...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 Extrinsic...
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...ANALYSIS OF REGRESSION Jessica Cain American InterContinental University Abstract The world today uses statistics in many different ways to understand numbers and possible outcomes. One way that this is by using regression analysis. The regression analysis which is based on a correlation between two variables can help us to better understand the relationship between the two variables. The process which is a valuable one has helped researchers, and businesses to grow based on information obtained from a regression analysis that contains a linear regression. Introduction The purpose of a regression analysis is to help show a linear regression of certain variables. This helps to understand the correlation of the variables being tested. Correlation does give reason to suspect that the relationship between two variables is not die to chance or other hidden variables (Editorial Board, [EB], 2012). This is done by utilizing excel to show how the variables match up, and if one is causing the other or if there are outliers that are affecting the outcome. This is important as it will allow for a company to see and eliminate these unnecessary variables and continue their growth. Benefits and Intrinsic Job Satisfaction Regression output from Excel |SUMMARY OUTPUT | | | | | |Intrinsic |-0.08484 |4.844477 |Y=4...
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...Abstract This paper will include results regarding the following analysis: Benefits versus Intrinsic, Benefits versus Extrinsic, and Benefits versus Overall Job Satisfaction. Charts and graphs have been included into this report. Introduction The information that is provided in the charts and graphs shows the statistical numbers based on the number of employees that were included in the company survey. You will see the differences and similarities between the three categories provided. Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.057961607 R Square 0.003359548 Adjusted R Square -0.012460142 Standard Error 1.08903837 Observations 65 ANOVA df SS MS F Significance F Regression 1 0.251866 0.251866 0.212365 0.646507 Residual 63 74.71829 1.186005 Total 64 74.97015 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 4.8423386 0.424293 11.41273 5.62E-17 3.994457 5.69022 3.994457 5.69022 Benefits 0.03929547 0.085271 0.460831 0.646507 -0.1311 0.209696 -0.1311 0.209696 Graph Benefits and Extrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.324324066 R Square 0.1051861 Adjusted R Square 0.090982705 Standard Error 0.570612506...
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...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 95% Lower 95.0% Upper 95.0% Intercept 4.448334 0.407452 10.91745 6.02E-19 3.640342 5.256326 3.640342 5.256326 Benefits 0.150497 0.089945 1...
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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 95...
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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 Differences ...
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...Exploring the effect of ethical leadership in the relationship of reward system and Job performance Masters of Business Administration (Human Resource Management) BY Ayesha Amjad MBA3Y02121027 Sadia Mazhar MBA3Y02121010 Supervisor Mr. Muhammad Waqas LAHORE BUSINESS SCHOOL THE UNIVERSITY OF LAHORE ACKNOWLEDGEMENTS In the name of Allah, the Most Gracious and the Most Merciful Alhamdulillah, all praises to Allah for the strengths and His blessing in completing this research paper. Special appreciation goes to our supervisor, Sir Muhammad Waqas, for his supervision and constant support. Her invaluable help of constructive comments and suggestions throughout the research paper work have contributed to the success of this research. I would like to express my gratitude to the Dean, Lahore School of Business Dr. Naheed Sultana and also to the Head of Department, Lahore School of Business, Dr. Atif Mahmood for their support and help towards our postgraduate affairs. Sincere thanks to all our friends Amaima Yawar, Anum Sarwar, and Numan Irfan. Thanks for the friendship and memories. Last but not least, our deepest gratitude goes to beloved parents of Ayesha Amjad; Muhammad Amjad Fraooq and Mrs. Shazia Amjad and also to my brother Usman Amjad and sister Fiza Amjad for their endless love, prayers and encouragement. We also pay gratitude to deceased parents of Saadia Mazhar, Hafiz Muhammad Mazhar and Arjumand Mazhar and her sisters Ayesha Omer, Farkhanda Nouman...
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...Staples Equity Valuation and Analysis David Lecky Chad Loudermilk Bennett Matkins Kara Reynolds Amanda Rhodes David.Lecky@ttu.edu Chad.loudermilk@ttu.edu Bennett.Matkins@ttu.edu Karereyddd@yahoo.com Amanda.b.Rhodes@ttu.edu Table of Contents Executive Summary……………………………………………………….. 2 Overview of Staples and the Industry………………………………... 7 Five Forces Model……………………………………………………………………….. 9 Rivalry among Existing Firms……………………………………………………….. 9 Threat of New Entrants……………………………………………………………….. 15 Threat of Substitute Products………………………………………………………. 17 Bargaining Power of Buyers………………………………………………………... 17 Bargaining Power of Suppliers…………………………………………………..... 18 Classifying the Industry………………………………………………………………. 18 Key Success Factors……………………………………………………………………. 19 Competitive Advantage Analysis………………………………………………….. 19 Accounting Analysis………………………………………………………. 25 Key Accounting Policies………………………………………………………………. 25 Accounting Flexibility………………………………………………………………….. 26 Evaluation of Actual Accounting Strategy……………………………………… 29 Quality of Disclosure…………………………………………………………………… 30 Screening Ratio Analysis….…………………………………………………………. 33 Revenue Diagnostics………………………………………………………………….. 34 Expense Diagnostics…………………………………………………………………… 37 Potential “Red Flags”………………………………………………………………….. 39 Undo Accounting Distortions……………………………………………………….. 41 Ratio Analysis………………………………………………………………. 44 Liquidity Ratio……………………………………………………………………………. 44 Profitability Ratio……………………………………………………………………….. 56 Capital Structure...
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