...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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...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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...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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...139 Part 2 Costs and Decision Making Chapter 5 Cost Behavior and Relevant Costs Chapter 6 Cost-Volume-Profit Analysis and Variable Costing Chapter 7 Short-Term Tactical Decision Making Chapter 8 Long-Term (Capital Investment) Decisions 140 Chapter 5 Cost Behavior and Relevant Costs Chapter 5 U 141 Cost Behavior and Relevant Costs nderstanding the behavior of costs is of vital importance to managers. Understanding how costs behave, whether costs are relevant to specific decisions, and how costs are affected by income taxes allows managers to determine the impact of changing costs and other factors on a variety of decisions. This chapter introduces concepts and tools that will be used in Chapters 6 through 8. Chapter 5 begins with a definition of cost behavior and illustrates the concepts of fixed costs, variable costs, and mixed costs. Next, the chapter revisits the concept of relevant costs (introduced in Chapter 1) as it applies to variable and fixed costs. The chapter also describes the impact of income taxes on costs. Learning Objectives After studying the material in this chapter, you should be able to: 1 Describe the nature and behavior of fixed, variable, and mixed costs Analyze mixed costs using regression analysis and the high/low method 2 Distinguish between relevant and irrelevant costs and apply the concept to decision making 3 Illustrate the impact of income taxes...
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...Business Statistics Topic: Correlation and Regression Recommended Readings: Lind D.A., Marchal W.G., and Wathen S.A. (2012), Statistical Techniques in Business and Economics, 15th International Ed., McGraw Hill [Chapter 13] Earlier edts are also suitable. Waters, D., (2008) Quantitative Methods for Business,4th Ed., Financial Times, Prentice Hall [Chapter 9] When we look at interval or ratio scale variables there is often a relationship, eg: price and quantity demanded; time spent studying and exam results obtained; gardai (police) on duty and number of crimes as well as alcohol consumed and sensibility! Regression and correlation analysis is useful because it allows us predict the value of one variable from the knowledge of another. The said relationship can be positive or negative. One first step in establishing if any of these relationships exist is to draw a scatter graph. A Scatter plot or diagram is a chart that portrays the relationship between the two variables. It is the usual first step in correlation analysis * The Dependent variable is the variable being predicted or estimated. * The Independent variable provides the basis for estimation. It is the predictor variable. Correlation Analysis From a scatter plot we have a first picture of the data. The next step is to calculate a measure which can assess the strength of that relationship. The correlation coefficient r which represents correlation in a sample is calculated as: r =...
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...BUAD 310 – Spring 2011 - Dr. Arif Ansari Topics Covered –Simple Regression Homework # 4 - 100 points (Due date 4/4/2011- Monday) For Home Work 4, Turn in Part 1 (15 points), Question 1(20 points), Question 2 (20 points), Question 3 45 points). Part 1 MULTIPLE CHOICE [3 point each] 1. In Least squares regression, the regression line is obtained by minimizing, a) The total variation in the dependent variable. b) The sum of squares for error (SSE). c) The sum of squares for regression (SSR) d) The sum of squares for total (SST). e) None of the above 2. In a simple regression analysis involving 25 data points, the standard error of estimate is calculated as S( = 2.0 and the Fts = 10, then the information from regression line (SSR) should be, a) 60 b) 50 c) 40 d) 30 e) None of the above 3. In a statistics course, a linear regression equation was computed to predict the final exam score from the first quiz score. The equation obtained was Y = 10 + 0.9 X, where Y is the final exam score and X is the first quiz score. A prediction interval for Al Bundy who scored 95 on the first quiz and on the final exam scored 98 was computed. Also a confidence interval for mean score of 95 on the first quiz was computed. From this we can conclude: a) Al Bundy’s prediction interval in wider than the confidence interval. b) Al Bundy’s prediction interval in shorter than the confidence interval...
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...= $30 + $0.20X Contract 3: y = $1X where X is the number of miles traveled in the day. |3. |Contract |Cost Function | | |1 | Fixed | | |2 |Mixed | | |3 |Variable | Solution Exhibit 10-17 Plots of Car Rental Contracts Offered by Pacific Corp. [pic] 10-18 (20 min.) Various cost-behavior patterns. 1. K 2. B 3. G 4. J Note that A is incorrect because, although the cost per pound eventually equals a constant at $9.20, the total dollars of cost increases linearly from that point onward. 5. I The total costs will be the same...
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...times the probability of each event’s occurrence. Sum of the conditional profit (loss) for each event. Sum of the conditional profit (loss) for each event times the probability of each event’s occurrence. [Fact Pattern #1] Proper Propeller, Inc. plans to manufacture a newly designed high-technology propeller for airplanes. Proper Propeller forecasts that as workers gain experience, they will need less time to complete the job. Based on prior experience, Proper Propeller estimates a 70% cumulative learning curve and has projected the following costs. Cumulative number Manufacturing Projections of units produced Average cost per unit Total costs 1 2 $20,000 14,000 $20,000 28,000 [2] Gleim #: 1.3.98 -- Source: CMA 0408 1-148 (Refers to Fact Pattern #1) If Proper Propeller produces eight units, the average manufacturing cost per unit will be A. B. C. D. $14,000 $9,800 $1,647 $6,860 [3] Gleim #: 1.4.104 -- Source: CMA 1293 4-25 The four components of time series data are secular trend, cyclical variation, seasonality, and random variation. The seasonality in the data can be removed by A. B. C. D. Ignoring it. Taking the weighted average over four time periods. Subtracting a seasonality factor from the data. Multiplying the data by a seasonality factor. Copyright 2008 Gleim Publications, Inc. Printed for Mohammad Al-Sharafi Page 1 Gleim CMA Test Prep:...
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...Upadhyayula and Prof. Sumit Mitra, coordinators of the “Pricing” course at Indian Institute of Management Kozhikode, for giving us the opportunity to work on this project. We also express our gratitude to the students of PGP-17 and PGP-18 who volunteered to participate in our survey. This helped us to get a good sample size to analyze the data. TABLE OF CONTENTS ACKNOWLEDGEMENT 1 ABSTRACT 3 INTRODUCTION 4 OBJECTIVE 5 METHODOLOGY 5 ANALYSIS AND RESULTS 6 RECOMMENDATIONS 8 LIMITATIONS 9 APPENDIX 10 ABSTRACT Our study identifies the most important and influencing factors for consumer preferences to eat at a pizza outlet like pizza hut, dominoes and pizza corner. With the help of factor analysis and regression we are trying to find out the willingness of a customer to pay at a particular outlet and hence what should be the pricing strategy of the pizza chain. The main elements in our study which affect the spending capacity and willingness are combo offers, services and variety offered by the outlets. Hence using this analysis we have also tried to find the premium which these outlets can charge over to its customers for the same products. INTRODUCTION As the society and changing eating habits is influencing a lot of people to savor variety of...
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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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...Unit 4 [GB513: Business Analytics] Assignment This assignment requires you to use Excel. There is no template for this assignment. Make sure you explain your answers and provide the regression output tables for questions 1 and 2. Question 1 Shown below are rental and leasing revenue figures for office machinery and equipment in the United States over a seven-year period according to the U.S. Census Bureau. Use these data to run a linear regression and then forecast the rental and leasing revenue for the year 2012. Year Rental and Leasing ($ millions) 2004 5,860 2005 6,632 2006 7,125 2007 6,000 2008 4,380 2009 3,326 2010 2,642 Question 2 Suppose a researcher gathered survey data from 19 employees and asked the employees to rate their job satisfaction on a scale from 0 to 100 (with 100 being perfectly satisfied). Suppose the following data represent the results of this survey. Assume that relationship with supervisor is rated on a scale from 0 to 50 (0 represents poor relationship and 50 represents an excellent relationship), overall quality of the work environment is rated on a scale from 0 to 100 (0 represents poor work environment and 100 rep resents an excellent work environment), and opportunities for advancement is rated on a scale from 0 to 50 (0 represents no opportunities and 50 represents excellent opportunities). Answer the following questions: A) What is the regression formula? B) How reliable do you think the estimates will be based on this formula? How...
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...2) Introduction a) Purpose and Scope of Paper b) Questions of Interest, and/or hypotheses c) Describe the nature of the data set 3) Analysis and methods section a) Interpret the statistical summaries i) Tell the reader what you have found in the data (results, facts only). ii) Explain what those findings mean with regard to the problem (interpret results). b) Design – describe the most important aspects of how the data was collected. 4) Conclusions and summary section a) What has the analysis revealed? How have your questions been answered? (Refers back to the questions of interest, problem statement, and/or hypothesis b) Why was the analysis done (Refer back to your background) c) What of value was discovered? (Any unexpected results) 5) References 6) Appendix Executive Summary Based on my analysis I was able to determine that the size of the bill has an effect on the number of days the bill is late. I was also able to determine that commercial and residential bills get paid at different times based on the amount of them. We can also say that with the slope of .0166 (residential) means for each increase of one unit in X, the Y is estimated to increase .0166 units. On the other side the commercial business the slope of -0.191 for each increase of one unit in x, the Y is estimated to increase -0.191. The regression analysis for commercial represents 95.7% of our data, but we cannot say with 95% certainty that for residential customers that the size of the...
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...|Economics for Managerial Decision Making – II |2 | |4. |Management Information System & KM |2 | |5. |Human Resource Management |2 | |6. |Financial Management |2 | |7. |Executive Communication |6 | |8. |National Economic Planning – I (Presentation Only) |2 | |9. |National Economic Planning - II |2 | BUSINESS STATISTICS (As per University Syllabus) UNIT 1. BUSINESS STATISTICS - WHAT AND WHY? INTRODUCTION • Definition of statistics • Five stages of statistical investigation - Collection - Organization - Presentation - Analysis - Interpretation • Functions of statistics • Limitations of statistics COLLECTION OF DATA • Primary data: use and...
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...estimate future costs? Q4 How are the engineered estimate, account analysis, and two-point methods used to estimate cost functions? Q5 How does a scatter plot assist with categorizing a cost? Q6 How is regression analysis used to estimate a mixed cost function? Q7 What are the uses and limitations of future cost estimates? These learning questions (Q1 through Q7) are cross-referenced in the textbook to individual exercises and problems. COMPLEXITY SYMBOLS The textbook uses a coding system to identify the complexity of individual requirements in the exercises and problems. Questions Having a Single Correct Answer: |No Symbol |This question requires students to recall or apply knowledge as shown in the textbook. | |e |This question requires students to extend knowledge beyond the applications shown in the textbook. | Open-ended questions are coded according to the skills described in Steps for Better Thinking (Exhibit 1.10): ( Step 1 skills (Identifying) ( Step 2 skills (Exploring) ( Step 3 skills (Prioritizing) ( Step 4 skills (Envisioning) QUESTIONS 2.1 This function has both fixed costs and variable costs. If at least part of the cost is variable; total cost increases as production volumes increase. If at least part of the cost is fixed, the average total per-unit cost decreases because the average fixed cost decreases as volume increases...
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...and add (.4 x smallest in each row) – Hurwicz thing to do Equally likely Multiply .5 by smallest and largest in each row. Large + small = Laplace thing to do Minimax regret 100 – largest in row, 0 – smallest in row. Pull largest out to side and smallest is thing to do. Decision Making is uncertainty – DECISION UNDER RISK For this use EMV – Expected Monetary Value Chart = Good Normal Bad – Added Normal. Good = 40%, Normal and Bad are 30% each. Good = 100, Normal = 20, Bad = -70 .4x100+.3x20+.3x-70 Answer is EMV Largest between LP, SP, Nothing is your decision under risk Example in text book EMVI – check box in software Opportunity Lost .5 x each number, add rows. Pick smallest answer because want least amount of opportunity loss. Sensitivity Analysis Examines how the decision might change with different input data P = probability of a favorable market (1 – P) = probability of an unfavorable market (Large number x P) – (small number (1 - P)) Profit = Revenue – Expenses Profit = Revenue – (Fixed Cost + Variable Cost) Profit = (Selling price/unit)(# units sold) – (Fixed cost + (Variable cost /unit)(# units sold)) Profit = sX – (f + vX) Profit = sX – f – vX Where s = selling price per unit, f = fixed cost, v = variable cost/unit, X = number of units sold * Quantitative analysis The scientific approach to managerial decision making Quantitative Analysis Approach Define the Problem Problem = a statement, which should come from a manager...
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