标题：Implications of Fitting a Regression and Pearson Correlation Models in the Relationship Between Food Production, Production of Wood Products, CO2 Emissions and Climate: An Analysis of Time Series Data
摘要：This study investigates the most significant determinants of food production in Canada from among the following variables: Production of wood products, CO2 emissions from agriculture and forestry, CO2 emissions from fossil fuels, rainfall and temperature. It also verifies the relationship between food production, production of wood products, CO2 emissions from agriculture and forestry, CO2 emissions from fossil fuels, rainfall and temperature in Canada. The data for the analysis was essentially time series data spanning the period 1961-2010. The data on food production and production of wood products was obtained from FAOSTAT. The data on CO2 emissions from agriculture and forestry and fossil fuels was obtained from FAOSTAT and the US Department of Energy. The data on rainfall and temperature was obtained from Environment Canada, 2012. Data analysis was performed in the SPSS platform in which both multiple linear regression and bivariate linear correlations were used to fit the models. The results show that CO2 emissions generally increase with food production. Food production and CO2 emissions from agriculture and forestry have a correlation of about 0.87 while food production at the same time has a correlation of 0.84 with CO2 emissions from fossil fuels. Both CO2 emissions from agriculture and forestry and fossil fuels have a correlation of 0.94 which shows that they reinforce each other. The most significant variable that significantly correlates with food production is CO2 emissions from agriculture and forestry with a t-value of 2.63 and a p-value of 0.12.
