Regression Analysis of Land Area, Machinery, and Value Added Tax on Food Production
REGRESSION ANALYSIS OF LAND AREA, MACHINERY AND VALUE ADDED TAX ON FOOD PRODUCTION INDEX
Table of Contents
I. Introduction
A. Background of the Study
B. Statement of the Problem
C. Objective of the Study
D. Significance of the Study
E. Scope and Limitations
II. Review of Related Literature
III. Operational Framework
A. Description of Variables Used
B. A-priori Expectation
C. Introduction to the Hypothesized Econometric Model
IV. Methodology
A. Data
B. Summary of Variables
C. Empirical Procedures
V. Empirical Results and Interpretations
A. Estimated value of OLS Regression
B. Test for Multicollinearity
C. Test for Autocorrelation
D. Corrective Measures and the Corrected Model VI. Conclusion and Recommendation Bibliography Appendices
A. Summary of Data Set
B. Summary Statistics
Abstract This empirical research shows the relationship of food production index, agricultural land area, agricultural machinery, and agricultural value added tax. The researchers have done a wide set of econometric testing to see if there is a strong relationship between these variables. After doing the tests, we found out that agricultural machinery and agricultural land area greatly affects the food production index. However, the agricultural value added has a weak effect since it is not significant to the model.
I. Introduction
A. Background of the Study
The food industry is a really big industry here in the Philippines. Well, it’s not only in the Philippines because food is demanded everywhere you go since it’s a basic necessity and your body needs it in order to survive. In fact, when your work is related with food and you are popular already, you will gain a huge profit since the demand for food is high. Companies such as CDO, Purefoods, Argentina and other food businesses are some of the big and famous food manufacturers here in our country and they are already multimillion peso companies.
Food production can be defined as the
Table of Contents
I. Introduction
A. Background of the Study
B. Statement of the Problem
C. Objective of the Study
D. Significance of the Study
E. Scope and Limitations
II. Review of Related Literature
III. Operational Framework
A. Description of Variables Used
B. A-priori Expectation
C. Introduction to the Hypothesized Econometric Model
IV. Methodology
A. Data
B. Summary of Variables
C. Empirical Procedures
V. Empirical Results and Interpretations
A. Estimated value of OLS Regression
B. Test for Multicollinearity
C. Test for Autocorrelation
D. Corrective Measures and the Corrected Model VI. Conclusion and Recommendation Bibliography Appendices
A. Summary of Data Set
B. Summary Statistics
Abstract This empirical research shows the relationship of food production index, agricultural land area, agricultural machinery, and agricultural value added tax. The researchers have done a wide set of econometric testing to see if there is a strong relationship between these variables. After doing the tests, we found out that agricultural machinery and agricultural land area greatly affects the food production index. However, the agricultural value added has a weak effect since it is not significant to the model.
I. Introduction
A. Background of the Study
The food industry is a really big industry here in the Philippines. Well, it’s not only in the Philippines because food is demanded everywhere you go since it’s a basic necessity and your body needs it in order to survive. In fact, when your work is related with food and you are popular already, you will gain a huge profit since the demand for food is high. Companies such as CDO, Purefoods, Argentina and other food businesses are some of the big and famous food manufacturers here in our country and they are already multimillion peso companies.
Food production can be defined as the
Bibliography: The University of Reading. (n.d.). Retrieved August 31, 2012, from Mechanization and Technology: http://www.ecifm.rdg.ac.uk/mehanisation_and_technology.htm Asari, F Paul B. McNulty, P. M. (2009). Agricultural Mechanization and Automation (Vol. Vol 1). Dublin, Ireland: Eolss Publishers Company Limited, 2009. Stefan Wirsenius, C. A. (2010, July 22). How much land is needed for global production under scenarious of dietary changes and livestock productivity increases in 2030. Agricultural Systems , 18. Food for the Cities. (n.d.). Retrieved August 31, 2012, from Food and Agriculture Organization of the United Nations: ftp://ftp.fao.org/docrep/fao/011/ak003e/ak003e11.pdf Food: from farm to fork statistics Danao, R. A. (2002). Introduction to Statistics and Econometrics. Quezon, Philippines: University of the Philippines Press, 2002.