...The Correlations Between Salaries of NBA Players and Their On-court Performance Indicators ZiWEI YONG, 460228934 21, FEB, 2016 Contents Abstract 2 1. Introduction and literature review 3 1. Introduction to Regression Model 3 1.1 Regression Model 3 1.2 Simple Linear Regression Model 3 1.3 Multiple Linear Regression Model 4 1.4 Multicollinearity 4 1.5 Conclusion for Multiple Linear Regression Modelling 5 2. Literature Review 5 2.1 Introduction of NBA 5 2.2 Reasons for High Average Salary in NBA 5 2.3 Salaries based on long term contract 6 2.4 Reasons for Choosing On-court Performance Data 7 3. Preparations for Running the Multiple Regression Model 8 3.1 Objectives of this paper 8 3.2 Introduction of the variables 8 3.2.1 Dependent Variable 8 3.2.2 Independent Variables 8 3.3 Data Source 9 3.4 Scattered Plots 9 3.4.1 Scattered Plots of Salary and Independent Variables 9 3.4.2 Residual Scattered Plots 10 4. Multiple linear regression modelling 11 4.1 The Adjusted R² 11 4.2 The Histogram 12 4.3 Model Generated by Analysis: 12 5. Ethical Problems 13 5.1 Sample Size 13 5.2 Data for Kobe Bryant 14 5.3 Excluded Related Independent Variables 14 5.4 Multi-collinearity 14 6. Conclusion 15 Abstract This paper examines the correlation between NBA players’ salaries and their on-court performance indicators. Before getting into the relationship, I would introduce the essence of what is regression model and how to interpret it, then we would...
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...Song) Accounting 581 Professor Thornock Case Study Questions 1. Q. Identify several possible drivers of salary costs for use in estimating a salary cost function. Using one of these cost drivers, apply the High-Low technique to estimate the salary cost function for the Delta Airlines. What driver did you select and why? How would Delta use this function to forecast costs? What are the advantages of this technique? The disadvantages? A. Some of the possible drivers of salary cost are Number or Departures, Revenue Ton Miles, Revenue Miles scheduled, Revenue passenger miles and there are countless more you could use. High low method for revenue Ton Miles (High-2,369, low-1,580) by Salary Cost(High-1,607, Low1,037) :(2,369-1,580)/(1,607-1,037)= 789/570=1.384 I selected this driver because the revenue directly correlates to the price you could pay your employees and still turn a profit. Delta can use this to see that they are making a profit when looking at just the salary cost and the revenue they bring in. The advantages of this technique are you have concrete evidence when and if employees ask how much they are getting paid and why and the disadvantages are that the revenue Ton Miles can fluctuate over the course of a year and make it hard to base the decision of salaries on just one variable. 2.Use simple regression to estimate the salary cost function for Delta Airlines. Comment on the statistical validity and significance of your results. What are the...
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...Title: A STATISTICAL ANAYLYSIS OF NBA PLAYER SALARIES USING A MULTIPLE REGRESSION. ABSTRACT Basketball is one of the most popular sports in the world and National Basketball Association (NBA) is the most popular basketball league in the world. The NBA league is based on the United States of America and it consists of 30 teams. The NBA is so popular that the NBA finals are the 2nd most watched televised event in the U.S. after the NFL (National Football League) Super Bowl. Sometimes when we think about NBA players and the enormous amount of money they are making, we become a little jealous. It is well known about how some star players make so much money or are over-paid and yet can hardly form a sentence. The greatest challenge for the board of NBA has been how to harmonize the salaries. Due to this various people have tried to come up with different solutions .Some argue that height ,weight and physical strength play a big role in team winning but this is not the case as some players who are short help their teams win in several occasions. To solve this problem a multiple regression analysis will be utilized to analyze the salary data. A relationship will be established between the salary and performance variables. The other challenge will be choosing the model parameters that will be significant in order to be included in the model that will be developed. This can be solved by arranging the factors affecting an NBA player salary in a decreasing order of importance then changes...
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...Executive Summary We find that there are some relationships with gender and salary in that firm referring to employee database (in Appendix ) because it is obvious that male employees' salary differ from female employees' salary. In order to confirm this doubt, it seems necessary to do some statistical analyses including a two-sample t test of male salaries against female salaries and a multiple regression to explain salary using age ,experience, and an indicator variable for gender . Through these statistical analyses, we can see that the discrimination truly exists in that firm and what affects the result of difference in salary between males and females. Introduction Just looking at the employee database, we can find easily that the average of females' salary is lower than average of males' salary by $9,000. However gender seems not be the only factor can affect the final result , some other things also looks important such as age , experience and training level. So our purpose is to indentify if gender is an important factor for final result and to exclude influences by other factors. Therefore, the result is as we expected, we are sure that the discrimination truly exists in that firm. Analysis and Methods This section begins with summaries of males' salary and females' salary. There are 28 females with average salary $39635.46 and 43 males with average salary $48726.84, From the histograms in Figure (a) and Box plot(b),we can see a big difference here...
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...C h a p t e r One The Nature of Econometrics and Economic Data C hapter 1 discusses the scope of econometrics and raises general issues that result from the application of econometric methods. Section 1.3 examines the kinds of data sets that are used in business, economics, and other social sciences. Section 1.4 provides an intuitive discussion of the difficulties associated with the inference of causality in the social sciences. 1.1 WHAT IS ECONOMETRICS? Imagine that you are hired by your state government to evaluate the effectiveness of a publicly funded job training program. Suppose this program teaches workers various ways to use computers in the manufacturing process. The twenty-week program offers courses during nonworking hours. Any hourly manufacturing worker may participate, and enrollment in all or part of the program is voluntary. You are to determine what, if any, effect the training program has on each worker’s subsequent hourly wage. Now suppose you work for an investment bank. You are to study the returns on different investment strategies involving short-term U.S. treasury bills to decide whether they comply with implied economic theories. The task of answering such questions may seem daunting at first. At this point, you may only have a vague idea of the kind of data you would need to collect. By the end of this introductory econometrics course, you should know how to use econometric methods to formally evaluate a job training...
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...Business Application of Regression Name Institutional Affiliation Regression is a mathematical tool used by statisticians to investigate the relationship that exists between variables. Regression also sheds light on the strength of the relationship between two variables. The following are examples of how regression is applied in business. 1. Regression is used to measure the extent to which a company’s profits can be affected in the event of an increase in profits. 2. Advertising is used to help sell the goods and services that a company produces. Regression helps the company’s staff understand how fast changes in advertising expenditure can affect their sales in general. 3. By using regression, a company can see how it stock prices are affected by a variable such as an increase or decrease in interest rates. 4. Using variables such as sales and other market factors, one can use regression to forecast the future demand of a company’s goods and services. 5. Regression can also help in finding out the consequences of changing the money supply on the current rate of inflation in a country. 6. Farming can be considered to be a business as well. Therefore, a farmer can use regression to find out how a change in the amount of rainfall will affect his crop yields. 7. In order to determine the salary of an employee, many factors have to be put in place. Regression can help in determining how the education of a person affects how much he or she will...
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...describe social, scientific, geophysical, actuarial, and many other types of observable phenomena. Pareto originally used this distribution to describe the allocation of wealth among individuals since it seemed to show rather well the way that a larger portion of the wealth of any society is owned by a smaller percentage of the people in that society. He also used it to describe distribution of income.[8] This idea is sometimes expressed more simply as the Pareto principle or the "80-20 rule" which says that 20% of the population controls 80% of the wealth. This distribution is not limited to describing wealth or income, but to many situations in which an equilibrium is found in the distribution of the "small" to the "large". The following examples, among others, are sometimes seen as approximately Pareto-distributed: * The sizes of human settlements (few cities, many hamlets/villages. * File size distribution of Internet traffic which uses the TCP protocol (many smaller files, few larger ones) * Hard disk drive error rates The Pareto distribution function may be given as follows: fxx0,θ= θx0θx-θ-1 , x ≥ x0, θ>1 a) Assume that x0>0 is given and that x1, x2, ….,xn is an i.i.d. sample. Find the MLE of θ. b. We are told that x0=3. Given the following sample of five observations: x1=3, x2=2, x3=3, x4=6, x5=5. Find the MLE of θ for this sample. θ=1ln 3+ln2+ln3+ln6+ln55 –ln3=11.2583-1.0986=6.2617 c. Construct an Excel spreadsheet showing the values...
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...Model Specification: More able players and coaches should have higher salary, thus the team who spend the most on salary should has the best performance. Therefore we want to put this simple labor economics theory into a simple regression test to see whether the team total salary has a positive effect on the team number of win. Data: To answer this question we need to run a single variable linear regression of each team total salaries on the number of win by each team. The cross sectional data we had obtained from the NFL official website consists each team total salaries and their number of total win per season from 2010 to 2014. The reason we did not take data from different decade is because we are trying to use relevant data to prevent the problem of inflation and economic cycle effect. Results: We have find that the intercept is 1.72 and the slop of the regression line is 0.0628, which...
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...analysis of investment behavior of 50 couples who are selected from a sample size of 194 couples. 1. Descriptive statistics 2.Histograms 3.Pivot tables 4.Multiple Regression In this model , a scrutiny of the above statistical data will give the tendency to invest in retirement plans and the type of couples who invest and take advantage of the attractive investments in order to avail tax exemption. This report also elaborates on how the different independent variables - Number of children, Salary, Mortgage and Debt- have an effect on the dependent variable, i.e. the percentage of salary invested. Consequently the below tasks will be fulfilled. Step 1-Extracting a sample of 50 couples Step 2-Constructing histograms and point estimates with given confidence intervals Step 3-Inference from pivot tables to explain the preferences of different couples on investments based on independent variables Step 4-Performing multivariate regression and conducting significance tests on beta coefficients R^2 and F-tests and hence establish a correlation of different variables and ensuing effect on investments made. Dataset We have a sample of 194 couples whose financial data along with number of children belonging to each couple have been listed. The financial data includes the following: 1. combined annual salary of husband and wife 2.current mortgage on home 3.average amount of other debt 4.percentage of income invested in retirement plans Step1- Sample extraction In this...
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...ACCT 601– Managerial Accounting Investigating Cost Drivers Part A – Instructions The object of this exercise to gain a better understanding of overhead costs through the use of regression analysis. In particular, you will investigate which service department activities appear to drive or cause departmental overhead. The data you will use is from a proprietary database which contains detailed departmental (i.e. USED, NEW CAR, SERVICE, BODY SHOP) financial information for approximately 250 U.S. car dealerships. The data is in EXCEL format and is called ABC.xls. We will direct our attention to the service department overhead which includes the following costs: office supplies; tools; advertising; policy expense; laundry and uniforms; travel and entertainment; membership dues; legal and auditing; freight and express; telephone; postage; training; bad-debts; data processing; heat/light/power/water; equipment repair and maintenance, plus other miscellaneous departmental expenditures. As you are well aware, because a dealership’s annual overhead amounts are highly serially correlated from one year to the next, we will need to conduct our analysis in changes. The spreadsheet contains the following variables: dOH = The change in service department overhead costs. dSALES = The change in service department sales revenue dSUB = The change in departmental revenues that were subcontracted out-of-shop dUCRET = The change in the number of USED CAR retail sales units...
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...hypothesize that the return of the stock are related to debt capital ratio, earnings per share, salary of the CEO, net income, logarithm of net income and salary, and the stock price at the end of 1990 and 1994. II. Literature Review We are formulating how the CEOs compensation can affect the stock prices within a four-year period between the end of 1990 and 1994.An investigation based on the use of individual evaluation in CEO’s incentive plans that contrasts with objective stock price based measures may involve. Using complementary data evidence can be shown that individual performance evaluation increases growth opportunities (Bushman p. 161-193). An examination of the executive compensation structure of 153 randomly-selected manufacturing firms in 1979–1980 provides evidence supporting advocates of incentive compensation, and also suggests that the form rather than the level of compensation is what motivates managers and CEOs to increase firm value (Hamid p. 163-184). III. Data and Methodology Our data is a list of observations from 142 different stock prices in 1990 and 1994. The data table includes information of the stocks like percent of return, return on equity and capital, and earning per share. We also have other related data such as the salary of the CEO and net income of the firms in 1990 as well as their logarithms. We performed 3 data analysis using regression analysis and here are the results: dkr = debt capital ratio...
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...Running head: REGRESSION Regression Names RES/342 - Research and Evaluation II Date Professor Table of contents Executive Summary 3 Dataset 3 Data Observations 3 Statistical Analysis 4 Conclusion 4 Dataset for the 2004 season 5 Regression Analysis taking LOG (Y) 6 Regression Analysis 8 Executive Summary This report is to determine whether total team payroll for major league baseball teams directly varies with each team’s home attendance. This is an important statistical analysis because if we can prove that there is a relationship between salary and attendance then we can see that more fans in the stands will give a team more buying power when it comes to signing players. Dataset The independent variable is team payroll and the dependent variable is team home attendance. Each team plays 81 home games. The dataset consists of 30 Major League Baseball teams from the 2004 season. Data Observations For the independent variable: The arithmetic mean for home attendance is: 30,453.67 The median for total home attendance is: 31,499.50 The standard deviation for total home attendance is: 8,132.28139 The minimum for total home attendance is: 14,052 The maximum for total home attendance is: 50,499 For the dependent variable: The arithmetic mean for total payroll is: $73,052,363.27 The median...
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...2014 Data Science Salary Survey Tools, Trends, What Pays (and What Doesn’t) for Data Professionals John King & Roger Magoulas Take the Data Science Salary and Tools Survey As data analysts and engineers—as professionals who like nothing better than petabytes of rich data—we find ourselves in a strange spot: We know very little about ourselves. But that’s changing. This salary and tools survey is the second in an annual series. To keep the insights flowing, we need one thing: People like you to take the survey. Anonymous and secure, the survey will continue to provide insight into the demographics, work environments, tools, and compensation of practitioners in our field. We hope you’ll consider it a civic service. We hope you’ll participate today. Make Data Work strataconf.com Presented by O’Reilly and Cloudera, Strata + Hadoop World is where cutting-edge data science and new business fundamentals intersect— and merge. n n n Learn business applications of data technologies Develop new skills through trainings and in-depth tutorials Connect with an international community of thousands who work with data Job # 15420 2014 Data Science Salary Survey Tools, Trends, What Pays (and What Doesn’t) for Data Professionals John King and Roger Magoulas 2014 Data Science Salary Survey by John King and Roger Magoulas The authors gratefully acknowledge the contribution of Owen S. Robbins and Benchmark Research Technologies...
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...differ from regression analysis? Correlation analysis identifies the relationship between independent and dependent variables while regression analysis uses independent variables to predict dependent variable by means of predictive model. (b) What does a correlation coefficient reveal? Correlation coefficient reveals the degree of association between independent and dependent variables. The closer to the one the value is the more strong relationship. Negative values means negative relationship with r=-1 as perfect negative relationship whereas positive values means positive association with perfect positive association as r=1. (c) State the quick rule for a significant correlation and explain its limitations. r=1 means perfect positive relationship r=-1 means perfect negative relationship r=0 means no relationship Limitations are with respect to its subjectivity because r slightly greater than 0.5 may be treated as good relationship whereas r slightly less than 0.5 may have opposite meaning. (d) What sums are needed to calculate a correlation coefficient? Sums required are: 1) −(−) 2) (−)2 3) (−)2 (e) What are the two ways of testing a correlation coefficient for significance? Two methods are: 1) t-test: The test statistic is:=−21−2 2) z-test: Test statistic is:=ln[+1−1]2 12.48 In the following regression, X = weekly pay, Y = income tax withheld, and n = 35 McDonald’s employees. (a) Write the fitted regression equation...
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...REVISED M04_REND6289_10_IM_C04.QXD 5/7/08 2:49 PM Page 46 C H A P T E R Regression Models 4 15 9 40 20 25 25 15 35 6 4 16 6 13 9 10 16 TEACHING SUGGESTIONS Teaching Suggestion 4.1: Which Is the Independent Variable? We find that students are often confused about which variable is independent and which is dependent in a regression model. For example, in Triple A’s problem, clarify which variable is X and which is Y. Emphasize that the dependent variable (Y ) is what we are trying to predict based on the value of the independent (X) variable. Use examples such as the time required to drive to a store and the distance traveled, the totals number of units sold and the selling price of a product, and the cost of a computer and the processor speed. Teaching Suggestion 4.2: Statistical Correlation Does Not Always Mean Causality. Students should understand that a high R2 doesn’t always mean one variable will be a good predictor of the other. Explain that skirt lengths and stock market prices may be correlated, but raising one doesn’t necessarily mean the other will go up or down. An interesting study indicated that, over a 10-year period, the salaries of college professors were highly correlated to the dollar sales volume of alcoholic beverages (both were actually correlated with inflation). Teaching Suggestion 4.3: Give students a set of data and have them plot the data and manually draw a line through the data. A discussion of which line is “best” can help them appreciate...
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