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Words 540

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For almost two decades, managers have been learning to play by a new set of rules. Companies must be flexible to respond rapidly to competitive and market changes. They must benchmark continuously to achieve best practice. They must outsource aggressively to gain efficiencies. And they must nurture a few core competencies in race to stay ahead of rivals.

Positioning—once the heart of strategy—is rejected as too static for today’s dynamic markets and changing technologies. According to the new dogma, rivals can quickly copy any market position, and competitive advantage is, at best, temporary.

But those beliefs are dangerous half-truths, and they are leading more and more companies down the path of mutually destructive competition. True, some barriers to competition are falling as regulation eases and markets become global. True, companies have properly invested energy in becoming leaner and more nimble. In many industries, however, what some call hypercompetition is a self-inflicted wound, not the inevitable outcome of a changing paradigm of competition.

The root of the problem is the failure to distinguish between operational effectiveness and strategy. The quest for productivity, quality, and speed has spawned a remarkable number of management tools and techniques: total quality management, benchmarking, time-based competition, outsourcing, partnering, reengineering, change management. Although the resulting operational improvements have often been dramatic, many companies have been frustrated by their inability to translate those gains into sustainable profitability. And bit by bit, almost imperceptibly, management tools have taken the place of strategy. As managers push to improve on all fronts, they move farther away from viable competitive positions.

Operational Effectiveness: Necessary but Not Sufficient…...

...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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...Regression Analysis Definition: Regression is used to examine the relationship between one dependent and one independent variable. After performing an analysis, the regression statistics can be used to predict the dependent variable when the independent variable is known. Regression goes beyond correlation by adding prediction capabilities. Types Of Regression Analysis: Most widely used two types of regression analysis are- I [pic] Linear Regression Analysis: When the regression is conducted by two variables or factors then is called linear regression analysis. Multiple regression analysis: Multiple regression analysis is a technique for explanation of occurrence and calculation of future actions. A coefficient of correlation among variables X also Y is a quantitative index of connection involving these two variables. In squared type, while a coefficient of purpose specifies the quantity of difference in the principle variable Y that is accounted for through the deviation in the analyst variable X. [pic][pic][pic][pic]Examples for Linear Regression Analysis: ABC a manufacturing co. where the production cost depends on their raw materials cost. Now, For the given set of x(tk in million) and y ( tk in thousand per unit) values, determine the Linear Regression and also find the slope and intercept and use this in a regression equation. |X |Y | |50 |4.2 ...

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...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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...Abstract 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 ......

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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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...Multiple Regression Project: Forecasting Sales for Proposed New Sites of Pam and Susan’s Stores I. Introduction Pam and Susan’s is a discount department store that currently has 250 stores, most of which are located throughout the southern United States. As the company has grown, it has become increasingly more important to identify profitable locations. Using census and existing store data, a multiple regression equation will be used to forecast potential sales, and therefore which proposed new site location will be more profitable. II. Data The data set has 37 independent variables. This includes 7 categorical variables for competitive type and 30 numerical categories. There are 250 stores, meaning the sample size is 250. As the sales are given in $1,000’s of dollars it is best to remember that a unit change in x will correspond to that coefficient of x multiplied by 1,000. III. Results and Discussion Building a multiple regression model requires a step-by-step approach. Failure to follow such methodology could ultimately lead to incorrect and inaccurate forecasting for the dependent variable of interest. Below I will outline the process and findings used to obtain a multiple regression equation to forecast potential sales at newly proposed site of Pam and Susan’s discount department stores. The initial step in building a multiple regression model is to look for outliers and non-linear relationships between your dependent (predicated sales) and......

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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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...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 ......

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...Significance of Regression Analysis In statistics, regression analysis 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 changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are held fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted......

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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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