...Benckiser 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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...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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...Statistical Project Assignment | Statistics for Business & Economics | | DATASET 1: SIMPLE REGRESSION ANALYSIS Variable Definition Xi = Weight of car (pounds) Yi = Price of car ($) 1. (a) Regression Model using X to predict Y Weight and Price of Car Sales | | | | | | | | | | | | | Regression Statistics | | | | | | Multiple R | 0.212585295 | | | | | | R Square | 0.045192508 | | | | | | Adjusted R Square | 0.038951936 | | | | | | Standard Error | 7883.368653 | | | | | | Observations | 155 | | | | | | | | | | | | | ANOVA | | | | | | | | df | SS | MS | F | Significance F | | Regression | 1 | 450055137.6 | 450055137.6 | 7.241725381 | 0.007915154 | | Residual | 153 | 9508567701 | 62147501.31 | | | | Total | 154 | 9958622839 | | | | | | | | | | | | | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Intercept | 9854.041192 | 2894.819474 | 3.404026151 | 0.000847889 | 4135.063875 | 15573.01851 | Weight | 2.843766419 | 1.056751555 | 2.691045407 | 0.007915154 | 0.756058281 | 4.931474557 | Table 1 – Simple Linear Regression Model (Y and X) Simple linear regression equation Ŷi=b0+b1Xi From Table 1, we can see that b0 = 9854.0412 and b1 = 2.8438 Ŷi=9854.0412+2.8438Xi Figure 1 – Scatter Plot – Weight of Car vs Price of Car (b) Interpret the slope b1 measures the estimated change in the average value of Y as a result of...
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...reason for my asking. Everyone was willing to participate, as I kept their personal information confidential. I took the survey results and computed with 49 surveys. After analyzing the data I noticed that there were many relationships amongst the variables. Based on the research I concluded that the higher the age the more that they are willing to invest for the current year. There was also a strong relationship between age and income. The older the person, the more money they made. The dependent variable was age. Older people make more money, are more familiar with investments, and are more willing to make larger investments currently. Also, age related to home value in that an increase in age was related to an increase in home value. Analysis Investments | Income | Education | Kids | Home Value | Additional Investments | Age | 72000 | 198000 | 4 | 4 | 480000 | 25000 | 38 | 225000 | 300000 | 4 | 3 | 790000 | 50000 | 43 | 4000 | 30000 | 2 | 0 | 0 | 5000 | 23 | 5000 | 15000 | 2 | 0 | 0 | 2000 | 24 | 20000 | 30000 | 4 | 2 | 228000 | 6000 | 32 | 12000 | 75000 | 1 | 0 | 280000 | 7500 | 31 | 20000 | 45000 | 0 | 2 | 330000 | 10000 | 34 | 125000 | 250000 | 8 | 4 | 875000 | 50000 | 44 | 8000 | 24000 | 2 | 0 | 0...
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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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...Introduction to Multiple Regression Dale E. Berger Claremont Graduate University http://wise.cgu.edu Overview Multiple regression is a flexible method of data analysis that may be appropriate whenever a quantitative variable (the dependent or criterion variable) is to be examined in relationship to any other factors (expressed as independent or predictor variables). Relationships may be nonlinear, independent variables may be quantitative or qualitative, and one can examine the effects of a single variable or multiple variables with or without the effects of other variables taken into account (Cohen, Cohen, West, & Aiken, 2003). Multiple Regression Models and Significance Tests Many practical questions involve the relationship between a dependent or criterion variable of interest (call it Y) and a set of k independent variables or potential predictor variables (call them X1, X2, X3,..., Xk), where the scores on all variables are measured for N cases. For example, you might be interested in predicting performance on a job (Y) using information on years of experience (X1), performance in a training program (X2), and performance on an aptitude test (X3). A multiple regression equation for predicting Y can be expressed a follows: (1) [pic] To apply the equation, each Xj score for an individual case is multiplied by the corresponding Bj value, the products are added together, and the constant A is added to the...
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...BEHAVIOR OF MOBILE COMMUNICATION DEVICES USERS’ STATISFACTION LEVEL A Research Developed in Fulfilling the Course Completion of Managerial Data Analysis by Daniel Vincent Hadikrisno December 2011 . STATEMENT BY THE AUTHOR I hereby declare that this submission is my own work and, to the best of my knowledge, contains no material previously published or written by another person. Jakarta, 18th of December 2011 ( Daniel Vincent Hadikrisno ) ABSTRACTION The main purpose of this research is to study the behavior of mobile communication device customers; what affects their decision in deciding to purchase and use a particular mobile communication device over the other. This is done by analyzing their statisfaction level towards their current mobile communication device based on factors that the writer have determined beforehand. The data are gathered using questionnaires that are distributed to young adults and adults who are currently using at least one of the three most popular mobile communication devices in Jakarta: Blackbery, iPhone, and Android. The data are then gathered and transformed into three different regression model, where test of significances can be taken towards them. The result is that the three mobile communication devices are each fulfilling a different specific need of the customers. These differences are actually their competitive advantage over their direct competitors. Therefore, should a new product would like to be released, make...
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...Probability, Statistics, and Forecasting OPRE 433 Fall 2013 Regression Report Xie Gehui (gxx24@case.edu) Dec 2, 2013 I. Introduction The data set given contains more than one independent variable, so the target of our regression analysis is to build an appropriate multiple regression model. To realize this target, we have to build a multiple linear regression model to test the regression assumptions: model appropriateness, constant variance, independence, and normality. Certainly we need to modify the data set or the model itself to satisfy these assumptions, and at last get the model acceptable. In the original data set that we are going to deal with in this report, there are 20,640 observations of 8 explanatory variables labeled X1, X2, X3, X4, X5, X6, X7, X8 and 1 dependent variable labeled Y. All of the 9 variables are continuous. II. Method of analysis To check the model appropriateness assumption, we need to make sure the functional form is correct. The residual plot will show the pattern suggesting the form of an appropriate model. To check the validity of the constant variance assumption, we need to examine residual plots. A residual plot with a horizontal band appearance suggests that the spread of the error terms around 0 is not changing much as the horizontal plot value increases. Such a plot tells us that the constant variance assumption approximately holds. To check the independence assumption, we need to detect if any positive autocorrelation...
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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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...Business Analytics- * Exploring data to find new patterns and relationships (data mining) * Explaining why a certain result occurred (statistical analysis, quantitative analysis) * Experimenting to test previous decisions (A/B testing, multivariate testing) * Forecasting future results (predictive modeling, predictive analytics) Answers the Important Questions Such As: -Why did it happen? Will it happen again? What will happen if we change x? What else does the data tell us that never thought to ask? By Using: Statistical/Quantitative Analysis Data Mining Predictive Modeling Multivariate Testing Microsoft Excell- Microsoft Excel is a spreadsheet application . It features calculation, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications. It has been a very widely applied spreadsheet for these platforms, SAS- SAS is a software suite developed by SAS Institute for advanced analytics, business intelligence, data management, and predictive analytics. It is the largest market-share holder for advanced analytics. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. It is widely used in insurance, public health, scientific research, finance, human resources, IT, utilities, and retail, and is used for operations research, project management, quality improvement, forecasting and decision-making. JMP Pro- JMP is used...
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...–Managerial Economics and Globalization July 21, 2013 College Students buy pizza in large quantities for a cheap price, but if the prices were to increase, then these same students may look for similar alternatives that will not empty their wallets. These are possible alternatives that offer a large quantity of food at a reasonable price that can affect the demand of pizza. However, monitoring the costs of the competing fast food restaurants in the Charlotte, North Carolina, area will allow Domino’s Pizza to offer certain specials and pizza deals to the community that can keep their demand at a high rate. A market demand analysis is used to help understand how much consumer demand there is for a given product or service. This type of analysis will help determine if a business can successfully enter a market and generate enough revenue and profit to maintain the business. One must identify the market and the growth potential. Domino’s Pizza was incorporated in 1963 and has been franchising since 1967. A traditional Domino’s store is located in shopping centers and/or strip malls with appropriate parking for delivery vehicles and walk-in customers for carry-out services. The initial investment of a traditional store is approximately $119,500 (low estimation).To determine if a Domino’s Pizza can be opened in Morehead City, North Carolina one must estimate consumer demand for this area. Some of the variables...
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...การวิเคราะห์เส้นทาง (Path Analysis) ความหมายการวิเคราะห์เส้นทาง การวิเคราะห์เส้นทาง เป็นการวิเคราะห์สาเหตุ โดยนักวิจัยต้องการศึกษาสาเหตุ ของปรากฏการณ์ต่าง ๆว่ามาจากอิทธิพลของสิ่งใด กล่าวอีกนัยหนึ่งก็คือ นักวิจัยต้องการ ค้นหาว่าตัวแปรตามที่กำลังศึกษานั้น เกิดจากอิทธิพลของตัวแปรอิสระอะไรบ้าง และตัว แปรอิสระนั้นมีอิทธิพลต่อตัวแปรตามมากน้อยเพียงใด (สำเริง บุญเรืองรัตน์, หน้า 91) การอธิบายสาเหตุของปรากฏการณ์ นักวิจัยต้องอาศัยความรู้และทฤษฏีต่าง ๆ มา วิเคราะห์เพื่อตั้งสมมติฐานด้วยการสร้างแผนภาพ (Diagram) แสดงเหตุต่าง ๆ ที่มีอิทธิพล ต่อสิ่งที่ตนเองกำลังศึกษาอยู่ เมื่อนักวิจัยสร้างรูปแบบแผนภาพแสดงสาเหตุดังกล่าวแล้ว ก็หาวิธีทดสอบว่า แผนภาพดังกล่าวเป็นไปตามสมมติฐานที่ตั้งไว้หรือไม่ โดยการวิเคราะห์เส้นทาง (Path Analysis) เทคนิคนี้ ไรท์ (Wright,1934) เป็นผู้คิดค้นขึ้นมา การทดสอบสมมติฐานอาจใช้ วิธีการคำนวณโดยใช้โปรแกรมลิสเรล (LISREL) (การเขียนแผนภาพเพื่อนำไปเขียนคำสั่ง วิเคราะห์ในโปรแกรม LISREL จากตัวแปรอิสระไปยังตัวแปรตามใช้สัญลักษณ์เป็น GA และจากตัวแปรตามหนึ่งไปยังอีกตัวหนึ่งใช้สัญลักษณ์เป็น BE และเขียนตัวเลขกำกับ เส้นทางเรียงจากปลายลูกศรไปต้นลูกศร) ซึ่งโปรแกรม LISREL จะคำนวณค่าสัมประสิทธิ์ สหสัมพันธ์และระดับนัยสำคัญของแต่ละเส้นทาง พร้อมแนะนำเส้นทางที่เหมาะสมให้ด้วย ดังตัวอย่างที่จะกล่าวต่อไปในตอนสุดท้าย ความรู้เบื้องต้นเกี่ยวกับการวิเคราะห์เส้นทาง การวิเคราะห์เส้นทาง (Path analysis) เป็นการศึกษาความสัมพันธ์ของตัวแปรในเชิงเหตุและผล เป็นวิธีที่มีพื้นฐานทางสถิติมาจากการวิเคราะห์การถดถอย (Regression analysis) โดยอาศัยแผนภาพและสมการโครงสร้างของแผนภาพเป็นหลักในการนำม...
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...Lupus and Multiple Sclerosis Student’s Name University Abstract This study seeks to classify Lupus Multiple Sclerosis. This study, therefore, is comparing and contrasting the Pathophysiology, clinical manifestations, nursing or medical management and prognosis of two different disease processes (Bates, 2011). It will involve a survey research design; the sampling strategy included in this study will be simple random sampling as the required is readily available from secondary sources. About 90 patients suffering from Lupus Multiple Sclerosis will participate in the study. Analysis of data is through the use of regression analysis and descriptive statistics; the research findings will be on the research question and the research objectives. Multiple Sclerosis could have detrimental effects on the human nervous system, especially when misdiagnosed happens or when it is not detected early. It is a disease that requires close monitoring and proper treatment (Schopick, 2011). Table of Contents 1.0 Introduction/Background of the Study…………….………………………………....4 2.0 Problem……………………………………………………………………………….4 3.0 Purpose………………………………………………………………………………..5 4.0 Objectives……………………………………………………………………………..5 5.0 Research Questions…………………………………………………………………...5 6.0 Significance to Nursing……………………………………………………………….6 7.0 Methodology….............................................................................................................6 8.0 Results ………………………………………………………………………………...
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...954 Mode 22 2 2921 Median 40 3 4,009 Max. 67 7 5,678 Min. 21 1 2,448 Range 46 6 3,230 St. Deviation 15.29 1.81 920.90 Skewness 0.18 0.44 0.09 While most people earn $22, 000, the average income of the sample population is $42, 240 The household data is skewed due to the fact that 2 persons compose the majority of households. The data points are spread out over a wider range of values as indicated by the high standard deviation of $15, 290. The household incomes range from anywere between $21, 000 to $67, 000 while the size of each household ranges from 7 to 1. The amount charged data seems to be normally distributed. Simple linear regression: Amount Charged vs. Annual Income = 2388.83 + 37.06 Xi Where: Yi = estimated, or predicted, Y value for Amount Charged in $ Xi = value of the independent variable,...
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