HW1‚ Spring 2013 Regression Analysis (STAT338) Please solve the following exercise and submit a group solution on Tuesday‚ 27 February 2013. The strength of paper (y) used in the manufacture of cardboard boxes is related to the percent of hardwood concentration (x) in the original pulp. Under uncontrolled conditions‚ a pilot plan manufacturers 16 samples‚ each from a different batch of pulp‚ and measured the tensile strength. The data are shown below. Strength of paper Percent of hardwood
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Introduction Malaysia is centrally located in the ASEAN region with a population of more than 500 billion people‚ Malaysia offers vast opportunities for global automotive and component manufacturers to set up manufacturing and distribution operations in the country. The rapid growth of the economy and the high purchasing power of its population have made Malaysia the largest passenger car market in ASEAN. At the same time‚ the establishment of national car projects‚ PROTON and PERODUA‚ has transformed
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CHAPTER 8 FORECASTING AND DEMAND PLANNING Have you ever gone to a restaurant and been told that they are sold out of their “special‚” or gone to the university bookstore and found that the texts for your course are on backorder? Have you ever had a party at your home only to realize that you don’t have enough food for everyone invited? Just like getting caught unprepared in the rain‚ these situations show
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Probability Primer 1 Chapter 2 The Simple Linear Regression Model 3 Chapter 3 Interval Estimation and Hypothesis Testing 12 Chapter 4 Prediction‚ Goodness of Fit and Modeling Issues 16 Chapter 5 The Multiple Regression Model 22 Chapter 6 Further Inference in the Multiple Regression Model 29 Chapter 7 Using Indicator Variables 36 Chapter 8 Heteroskedasticity 44 Chapter 9 Regression with Time Series Data: Stationary Variables 51
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Demand Forecasting Demand forecasting • Why is it important • How to evaluate • Qualitative Methods • Causal Models • Time-Series Models • Summary Production and operations management Product Development long term medium term short term Product portifolio Purchasing Manufacturing Distribution Supply network designFacility Partner selection location Distribution network design and layout Derivatuve Supply Demand forecasting is product developmentcontract the starting ? point
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CHAPTER 10 DETERMINING HOW COSTS BEHAVE 10-16 (10 min.) Estimating a cost function. 1. Slope coefficient = = = = $0.35 per machine-hour Constant = Total cost – (Slope coefficient Quantity of cost driver) = $5‚400 – ($0.35 10‚000) = $1‚900 = $4‚000 – ($0.35 6‚000) = $1‚900 The cost function based on the two observations is Maintenance costs = $1‚900 + $0.35 Machine-hours 2. The cost function in requirement
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TABLE OF CONTENTS List of Tables i List of Figures iv Abstract v Key Terms ix CHAPTER-1 Introduction 1.1 Introduction to Dividends 1 1.2 A Short History of Dividend Policy 6 1.3 Dividend Policy 9 1.4 Economic Rationale to Dividends 12 1.5 Dividend Policy and its Linkages with other Financial Policies 15 1.6 Pure Vs Smoothed Residual Dividend Policy 16 1.7 Dividend Declaration Process 17 1.8 Alternative Forms of Dividends 18
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experimental design‚ analysis of variance (ANOVA)‚ regression‚ generalized linear model (GLM)‚ analysis of deviance‚ restricted maximum likelihood (REML)‚ spatial data‚ precision agriculture‚ on-farm experimentation. Contents U SA NE M SC PL O E – C EO H AP LS TE S R S 1. Introduction 2. Current methodology 2.1. Experimental Design 2.2. Analysis of Variance 2.3. Regression Analysis 2.3.1. Linear Regression 2.3.2. Non-linear Regression 2.4. Generalized Linear Models (GLMs) 2.5. Residual or Restricted Maximum
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Model To Be Studied By Residual 1. The regression function is not linear. 2. The error terms do not have constant variance. 3. The error terms are not independent. 4. The model fits all but one or few outliers‚ 5. The error terms are not normally distributed. 6. One or several important predictor(s) have been omitted from the model. Diagnostic For Residuals Six diagnostic plots to judge departure from the simple linear regression model * Plot of residuals against predictor
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Xiaohui WAN Student Number: 43348802 Part A – Simple Linear Regression Analysis (a) Expectation Ŷi = β0 + β1xi + βi Where Ŷ =Amount of money the state spends on aid to local school districts per capita (AIDPC) Xi= State Income per capita (INCOMEPC) In general‚ we expect the increase of state Income per capita make the state spends more money on aid to local school districts per capita. Therefore‚ we expect β1>0. (b)Estimate the regression model Coefficients Standard Error t Stat Intercept
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