STATISTICS FOR MGT DECISIONS FINAL EXAMINATION Forecasting – Simple Linear Regression Applications Interpretation and Use of Computer Output (Results) NAME SECTION A – REGRESSION ANALYSIS AND FORECASTING 1) The management of an international hotel chain is in the process of evaluating the possible sites for a new unit on a beach resort. As part of the analysis‚ the management is interested in evaluating the relationship between the distance of a hotel from the beach and the hotel’s
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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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the following terms: a. In what type of regression is it likely to occur? b. What is bad about autocorrelation in a regression? c. What method is used to determine if it exists? (Think of statistical test to be used) d. If found in a regression how is it eliminated? Problem 8 Define Multicollinearity in the following terms: a. In what type of regression is it likely to occur? b. Why is multicollinearity in a regression a difficulty to be resolved? c. How
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F-2‚Block‚ Amity Campus Sec-125‚ Nodia (UP) India 201303 ASSIGNMENTS PROGRAM: SEMESTER-I Subject Name : Study COUNTRY : Permanent Enrollment Number (PEN) : Roll Number : Student Name : INSTRUCTIONS a) Students are required to submit all three assignment sets. ASSIGNMENT DETAILS MARKS Assignment A Five Subjective
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appropriate sampling populations and instruments. Other topics include descriptive statistics‚ probability concepts‚ confidence intervals‚ sampling designs‚ data collection‚ and data analysis—including parametric and nonparametric tests of hypothesis and regression analysis. Policies Faculty and students will be held responsible for understanding and adhering to all policies contained within the following two documents: • University policies: You must be logged into the student website to view this
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Problems on Regression and Correlation Prepared by: Dr. Elias Dabeet Q1. Dr. Green (a pediatrician) wanted to test if there is a correlation between the number of meals consumed by a child per day (X) and the child weight (Y). Included you will find a table containing the information on 5 of the children. Use the table to answer the following: Child Number of meals consumed per day (X) child weight (Y) X² Y² XY Ahmad 11 8 121 64 88 Ali 16 11 256 121 176 Osama 12 9 144
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2. (30%) Trip Generation Model 下列為針對淡水區進行的旅次產生調查，共分為 6 個交通分區(traffic zone)： Zone Trip production Car ownership 1 650 250 2 450 190 3 950 715 4 850 625 5 750 290 6 290 135 (1) 試建立一線性迴歸函數(linear regression model)，進行參數校估，列出校估後之函 數，並計算模式之 R2、透過 t 檢定(t-test)檢驗顯著性。 (2) 試建立一對數線性迴歸函數(log-linear regression model) (即乘冪迴歸模式) 進行參 ， 2 數校估，列出校估後之函數，並計算模式之 R 、透過 t 檢定(t-test)檢驗顯著性。 (3) 比較上述兩個模式之差異，討論孰優孰劣。 共 2 頁 第 1 頁 3. (30%) Mode Choice Model 考慮旅運者對三種運具的(負)效用函數： COSTk U k ak 0.3OVTk 0.15IVTk
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LINEAR REGRESSION MODELS W4315 HOMEWORK 2 ANSWERS February 15‚ 2010 Instructor: Frank Wood 1. (20 points) In the file ”problem1.txt”(accessible on professor’s website)‚ there are 500 pairs of data‚ where the first column is X and the second column is Y. The regression model is Y = β0 + β1 X + a. Draw 20 pairs of data randomly from this population of size 500. Use MATLAB to run a regression model specified as above and keep record of the estimations of both β0 and β1 . Do this 200 times. Thus you
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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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Chap 13 44 1.4 100 1.3 110 1.3 110 0.8 85 1.2 105 1.2 105 1.1 120 0.9 75 1.4 80 1.1 70 1.0 105 1.1 95 A sample of 12 homes sold last week in St. Paul‚ Minnesota‚ is selected. Can we conclude that‚ as the size of the home (reported below in thousands of square feet) increases‚ the selling price (reported in $ thousands) also increases? * Compute the coefficient of correlation. * = [12(1344) – (13.8)(1160)]/12(16.26) – (13.8)2][12(114850)
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