CHAPTER 1 INTRODUCTION A. Background of the Study The Philippine Food Manufacturing Industry is said to be the most dominant and dynamic industry in our country today‚ having an approximate contribution of 20 percent in our Gross Domestic Product annually. The 2004 Philippine Bureau of Food and Drugs’ Statistical Report of Establishments listed a total of 11‚601 food processing establishments nationwide. The food manufacturing has ten subsectors‚ namely: Bakery products‚ Coconut
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1. Calculate real GDP for 2004 and 2005 using 2004 prices. To calculate the real GDP we use the constant price for 2004 which was $20. Real GDP (base year 2004) 2004 ($20 per CD x 100 CD’s) + ($110 per racquet x 200 racquets) = 24000 2005 ($20 per CD x 120 CD’s) + ($110 per racquet x 210 racquets) = 25500 By what percentage did real GDP grow? Because the Real GDP was $24000 in 2004 and $25500 in 2005‚ real GDP grew by ($25500 - $24000) / $24000 = 0.0625 or 6.25% 2. Calculate the
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x4 5. Furniture: x5 Issues identified • To build a regression model that accurately predicts the sale price of a condominium unit sold at auction. • Use graphs to demonstrate how each of the independent variables in the model affects price. B. Case Analysis 1. Creating the deterministic regression model Since there are five independent variables‚ there shall be 5 variables in our initial first order regression model: E(y) = β0 + β1x1 + β2 x2 + β3 x3 + β4 x4 + β5 x5 + (
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operating costs that we as a delivery company have is gasoline. We use gasoline daily in massive quantities. The cost of gas affects American’s daily‚ and people can be heard complaining about the high prices. What about delivery companies? In this paper‚ we will be discussing the effect of rising gas prices on our company throughout the next ten years. Gas prices change daily‚ and throughout the year it is amazing to look at the monthly averages changing. In 2008 these averages varied from a low
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Simple Linear Regression Model 1. The following data represent the number of flash drives sold per day at a local computer shop and their prices. | Price (x) | Units Sold (y) | | $34 | 3 | | 36 | 4 | | 32 | 6 | | 35 | 5 | | 30 | 9 | | 38 | 2 | | 40 | 1 | | a. Develop as scatter diagram for these data. b. What does the scatter diagram indicate about the relationship between the two variables? c. Develop the estimated regression equation and explain what the
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purchase which does not affect their normal daily life without consuming it. The opportunity cost of purchasing beverage can transfer to other more useful goods that may bring them more benefits when consume‚ such as literature books etc. This paper looks at the student behaviour on
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these characteristics and modeled the relationship between them and the price of real estate for a specific area. How are these characteristics used in determining the price? A model that is commonly used in real estate appraisal is the hedonic regression. This method is specific to breaking down items that are not homogenous commodities‚ to estimate value of its characteristics and ultimately determine a price based on the consumers’ willingness to pay. The approach in estimating the values is done
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Thesis Concept Paper Proposed Title: ‘Modelling and Forecasting Electricity Consumption of the Philippines’ Researcher: Alejon P. Padriganda Degree Program: Bachelor of Science in Applied Mathematics Adviser: Dennis A. Tarepe Ph.D Introduction Backgorund of the Study In the Philippines‚ electric power is becoming the main energy form relied upon in all economic sectors of the country. As time goes by‚ while different establishments and properties were built and developed‚ the demand
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40 JOURNAL FOR ECONOMIC EDUCATORS‚ 10(1)‚ SUMMER 2010 UNDERGRADUATE RESEARCH Public Transportation Ridership Levels Christopher R. Swimmer and Christopher C. Klein 1 Abstract This article uses linear regression analysis to examine the determinants of public transportation ridership in over 100 U. S. cities in 2007. The primary determinant of ridership appears to be availability of public transportation service. In fact‚ the relationship is nearly one to one: a 1% increase in availability is
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Howland* Wabash College Abstract This paper corrects a fundamental error in the literature examining the OkunÕs Law relationship between the unemployment rate and the rate of growth of output. Since OkunÕs original work‚ biased estimates of the Okun Coefficient on Unemployment‚ output gaps‚ and potential GNP have been reported by authors who mistakenly assume that unbiased coefficient estimates of the reverse regression are reciprocals of their direct regression analogues. Thus‚ for example‚ there
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