EPI/STA 553 Principles of Statistical Inference II Fall 2006 Regression: Testing Assumptions December 4‚ 2006 Linearity The linearity of the regression mean can be examined visually by plots of the residuals against any of the independent variables‚ or against the predicted values. Chart 1 shows a residual plot that reveals no Chart 2 C hart 1 0.4 0.4 0.3 0.3 0.2 0.1 0.1 Residual Residual 0.2 0.0 -0.1 0.0 -0.1 -0.2 -0.2 -0.3 -0.3 -0.4 -0.5
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Introduction This document presents the regression analysis of customer survey data of Hatco‚ a large industrial supplier. The data has been collected for 100 customers of Hatco on 14 parameters. The 14 variables are as follows: * Perceptions of Hatco: This data was collected on a graphic measurement rating scale consisting of a 10cm line ranging from poor to excellent. Indicator | Variable | Description | X1 | Delivery speed | amount of time it takes to deliver the product once an order
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Regression Analysis: Predicting for Detroit Tigers Game Managerial Economics BSNS 6130 December 13‚ 2012 By: Morgan Thomas Chad Goodrich Jake Dodson Austin Burris Brittany Lutz Abstract As there are many who invest in athletic events‚ the ability to better predict attendance to such events‚ such as the Detroit Tigers games‚ could benefit many. The benefits include being able to better stock concessions stands‚ allocate advertising budgets‚ and staff security. Therefore‚ the aim
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This season has been a lot different for Baltimore Orioles fans‚ where finally the Orioles are in contention to make their first ever MLB playoffs since 1997. The Orioles are playing some great baseball in September and are looking like a playoff team that can win it all. Last night the O’s played the Boston Red Sox in front of there all time greats; Frank Robinson‚ Jim Palmer‚ Eddie Murray‚ Earl Weaver and Cal Ripken who were there to commemorate Brooks Robinson a future Hall of Famer. It was an
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Chapter 12 Data Envelopment Analysis Data Envelopment Analysis DEA is an increasingly popular management tool. This write-up is an introduction to Data Envelopment Analysis DEA for people unfamiliar with the technique. For a more in-depth discussion of DEA‚ the interested reader is referred to Seiford and Thrall 1990 or the seminal work by Charnes‚ Cooper‚ and Rhodes 1978 . DEA is commonly used to evaluate the e ciency of a number of producers. A typical statistical approach is characterized
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Contents 1.0 Introduction and Motivation 2 2.0 Methodology 5 2.1. Descriptive Statistics 5 2.2 Matrix of pairwise correlation. 6 3.0 Model Specification 6 3.1 Linear Regression Model. 6 3.2 The Regression Specification Error Test 8 3.3 Non-linear models 9 3.4 Autocorrelation. 10 3.5 Heteroskedasticity Test 10 4.0 Hypothesis Testing 11 5.0 Binary (Dummy) Variables 11 6.0 Conclusion 13 Reference List 13 1.0 Introduction and Motivation Crude oil is one of the world’s most important natural
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assignments are to be handed in the classroom one week after the Session where the corresponding subjects are treated. Assignments sent via internet will not be considered. Session Subject and problems in the Assignment Chap. Due for Session 1 Data and Descriptive Statistics Chap. 1 and 2 Session 2 Problems Chap. 1: 2‚ 3 and 14 Problems Chap. 2: 7‚ 15‚ 29 and 30 Session 2 Descriptive Statistics and intro. to Prob. Chap. 3 and 4 Session 3 Problems Chap. 3: 8‚ 27‚ 29‚ 41‚ 45‚ 53 and 54
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from 18 most popular wineries in west coast US regions. Methodology This dataset contains 10 variables and 890 observations. Through regression of this dataset‚ an equation is created to quantify the correlation between different variables and wine value and therefore make the preudiction of wine value possible. 712 samples are used in the multiple regression models to generate the formula and 178 holdout samples are used to simulate the accuracy of the prediction resulting from the formula‚ which
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Demand Forecasting Problems Simple Regression a) RCB manufacturers black & white television sets for overseas markets. Annual exports in thousands of units are tabulated below for the past 6 years. Given the long term decline in exports‚ forecast the expected number of units to be exported next year. |Year |Exports |Year |Exports | |1 |33 |4 |26
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Unit 5 – Regression Analysis Darryl Gamble American InterContinental University Abstract The following analysis charts will help determine the overall satisfaction or dissatisfaction that employees feel about the company they work for and the management team of that company. The final analysis will let management know if anything needs to be corrected. Introduction Job satisfaction is made up of many things. Management sometimes needs some hope in evaluating how good of a job
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