Eighth International IBPSA Conference Eindhoven‚ Netherlands August 11-14‚ 2003 BUILDING MORPHOLOGY‚ TRANSPARENCE‚ AND ENERGY PERFORMANCE Werner Pessenlehner and Ardeshir Mahdavi Department of Building Physics and Human Ecology Vienna University of Technology Vienna‚ 1040 - Austria ABSTRACT Certain energy-related building standards make use of simple numeric indicators to describe a building ’s geometric compactness. Typically‚ such indicators make use of the relation between the volume
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m Problem1: The demand for roses was estimated using quarterly figures for the period 1971 (3rd quarter) to 1975 (2nd quarter). Two models were estimated and the following results were obtained: Y = Quantity of roses sold (dozens) X2 = Average wholesale price of roses ($ per dozen) X3 = Average wholesale price of carnations ($ per dozen) X4 = Average weekly family disposable income ($ per week) X5 = Time (1971.3 = 1 and 1975.2 = 16) ln = natural logarithm The standard errors
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constant support and love. Regards‚ RESHAM SHARDA SONA CHAUDHARY SUGANDH KUMARIA SAMRIDHI SHARMA MANISHA NIRALA ABSTRACT This study focuses on the factors affecting the BSE Sensex. Using time series data from the years 1993-94 to 2013-2014 multiple regression analysis is applied to find out significant relationships between the dependent variable BSE Sensex and independent variables including Gold Prices‚ Foreign Exchange Reserves and the Exchange Rate. This study indicates a strong positive relation
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2) Basic Ideas of Linear Regression: The Two-Variable Model In this chapter we introduced some fundamental ideas of regression analysis. Starting with the key concept of the population regression function (PRF)‚ we developed the concept of linear PRF. This book is primarily concerned with linear PRFs‚ that is‚ regressions that are linear in the parameters regardless of whether or not they are linear in the variables. We then introduced the idea of the stochastic PRF and discussed in detail the nature
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1. Dataset 3 Questions related to Simple Regression 2. Solutions to Dataset 3 (including supporting figures) 3. Dataset 4 Questions related to Multiple Regression 4. Solutions to Dataset 4 (including supporting figures) University of Queensland | ECON 7300‚ STATISTICS FOR BUSINESS AND ECONOMICS‚ 2 STATISTICAL PROJECT October 14‚ 2013 [SANDEEP MAHAPATRA‚ 42982160 & WENQIAN ZHANG‚ 43260865 ] Instructions for Dataset 3: SIMPLE REGRESSION ANALYSIS (30 Marks) A statistician collected
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LECTURE 10 4TH STAGE OF QUANTITATIVE ANALYSIS: ANALYZING DATA Simple Regression to Multiple Regression Analysis: Introductory Material (Estimating and Evaluating the Estimated Model) PART – I: Simple/two-variable regression analysis Simple regression analysis: an example Assuming a survey of 10 families yields the following data on their consumption expenditure (Y) and income (X). Y (Thousands) X (Thousands) 70 80
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QNT/561 Final Examination Study Guide This study guide will prepare you for the Final Examination you will complete in the final week. It contains practice questions‚ which are related to each week’s objectives. In addition‚ refer to each week’s readings and your student guide as study references for the Final Examination. Week One: Descriptive Statistics and Probability Distributions Objective: Compute descriptive statistics for given data sets. 1. In 1995‚ the cost of unleaded gasoline
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had been taken up. Through DMAIC process initially 53 inputs were identified in Detailed Map‚ which was scaled down to 5 critical inputs after C&E Matrix and FMEA. Regression Analysis was done on real time data and best-fitted line were plotted to find out the effects of those critical inputs on hot metal silicon. Finally a Regression Equation was found and trial values approximately near to the calculated value of the
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allow us determine Doucette wants to decide whether or not to put an employee retention program in place. But first‚ he wants Sarah Jenkins to check whether manager tenure and crew tenure are related to store profit. Accordingly‚ run the three regression models per instructions given below; data for theseDefinitions of the different variables in the data are provided in the case itself. Briefly discuss the summary statistics presented in Exhibit 3 in Store24A and Exhibit 2 in Store24B. Use a maximum
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statistics are given in Table 2.1 while the OLS regression results are given in Table 2.2. Please answer the following questions using the information in the tables. (i) Using the estimated results: (a) Carefully interpret all of the parameters in the regression model including their magnitudes and expected and actual signs. (b) Comment on the statistical significance of each of the estimated coefficients cigarette demand. (d) Suppose the regression model was re-estimated under the hypothesis
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