...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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...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 the variables...
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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 competition...
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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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...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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...Introduction Every four years, the United States presidential election becomes one of the country’s biggest news stories for a large chunk of that year. During this time there are a great many words both spoken and written about how people are going to vote and what factors supposedly lead them to vote a certain way. It seems that much of the time the analysis of these issues is of a qualitative nature, and with that in mind this paper will attempt to approach the problem from a statistical point of view. That is not to say there is a lack of quantitative research on how specific demographics tend to vote; indeed, some of the good data and information out there will be used to inform this paper’s hypotheses and econometric model. This study will focus on the 2012 U.S. presidential election Barack Obama and Mitt Romney (there were several other candidates on the ballot, but none received a significant portion of the votes). Pundits threw around seemingly countless factors in an attempt to analyze and predict how people would vote in this election; this paper will focus on a select group of those factors, in hopes of drawing some firm and well-grounded conclusions as to whether they actually played a statistically significant role. Countywide data will be utilized. The recent widespread availability of election results on a county-by-county level, combined with countywide information from the U.S. Census Bureau, allows for a very large number of observations (at least in...
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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 Benefits...
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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 independent...
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...Assignment 2 Mid City Regression Analysis November 6, 2012 Question 1 – Do buyers pay a premium for a brick house, all else being equal? According to Model 1 above, a premium indeed is paid for a brick house if no other factor is considered. The reference being of a non-brick house shows an average price of $121,958 for house of all sizes, all locations and any number of rooms and bedrooms. A brick house, for all of the same criteria shows a premium of $25,810 being paid. Question 2 – Is there a premium for a house in neighborhood 3, all else being equal? Going by Model 2 above, a premium can indeed be observed for houses purchased in Neighborhood 3. Setting neighborhood 3 as the reference point, we observe an average house price of $159,294 with houses in the neighborhoods 1 & 2 showing average prices being $49,140 & $34,063 lower, respectively. Question 3 – Is there an extra premium for a brick house in neighborhood 3, in addition to the usual premium for a brick house? According to model 3, a premium can indeed be observed for brick houses in neighborhood 3 as opposed to brick houses in all other neighborhoods. We test this by adding an interaction variable of brick houses within neighborhood 3 and testing this against the reference which is a brick house and a house within neighborhood 3. With these reference parameters set, we get an average price for the reference group of $148,230 & a premium for the houses meeting the interaction variable criteria...
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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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... A Report on “Multiple Regression Analysis of Determinants of Dividend Payout Ratio of Reckitt Benckiser” Acknowledgement It is a great honor for us to submit this report to our respected teacher. At first we want to convey our thanks and gratitude to her for assigning us to prepare report entitled, “Reckitt Benckiser”. It would not have been possible for us to complete the report, but for his help. All of the efforts ended at a desired point for the cooperation and hard work, Sincerity and seriousness of our group members. So, all of them as well as our group members are worth of pure compliment. Letter of Transmittal February 14, 2015 Dear Sir, Subject: Submitting the report on “Determinants of dividend payout ratio of Reckitt Benckiser”. We are submitting a well-structured and comprehensive report on Reckitt Benckiser”. Despite many constraints like scope and access to information, we have tried to create something satisfactory. We have tried to follow your guideline in every aspects of preparing this report. We have concentrated on the most relevant and logical areas to make our report coherent as well as practical. We hope this report will entice your kind appreciation. Sincerely, ________________ Executive Summery Reckitt Benckiser is a global leader in household, health and personal care sectors and one of the fast growing multinationals. In our report we mainly deal with Multiple regression analysis of determinants of dividend...
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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 circumstances...
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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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...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 Stat P-value Intercept 3.866348351 0.385522375...
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