Quantitative Decision Making Research Paper Example | Topics and Well Written Essays - 1000 words

Quantitative Decision Making - Research Paper Example - the independent and dependent variables can be determined incorrectly (in this case it is hard to determine whether change in export production of passenger cars influences export production of commercial vehicles or vice versa); - the perceived relationship may be a result of simultaneous influence of the third (moderator) variable on both of the variables separately (for example, correlation between export production numbers of passenger cars and commercial vehicles can be explained by the influence of the increase/decrease of the exchange rate on exports of the automotive industry in general). It may be true in this case, as the suggested regression model explains only 22.2% of variance (R-Squared value is indicated in the table below). Thus, if the export production number of passenger cars increases by 10,000 the export production number of commercial vehicles will go up by 572 (0.0572 coefficient). The constant 13,002 can be considered rather an anchor point for the regression line and should not be taken as the value of JCYF when JCYL is equal to 0 due to the fact that the data set available contains no observation with JCYF equals to or is close to 0. To project the export production number of passenger cars (... - the perceived relationship may be a result of simultaneous influence of the third (moderator) variable on both of the variables separately (for example, correlation between export production numbers of passenger cars and commercial vehicles can be explained by the influence of the increase/decrease of the exchange rate on exports of the automotive industry in general). It may be true in this case, as the suggested regression model explains only 22.2% of variance (R-Squared value is indicated in the table below). Linear regression equation JCYF = 13002 + 0.0572 x JCYL (coefficients indicated in the table): Multiple R-Square Adjusted StErr of Summary R R-Square Estimate 0.4708 0.2217 0.1730 4184.251262 Degrees of Sum of Mean of F-Ratio p-Value ANOVA Table Freedom Squares Squares Explained 1 79773016.56 79773016.56 4.5564 0.0486 Unexplained 16 280127337.9 17507958.62 Coefficient Standard t-Value p-Value Lower Upper Regression Table Error Limit Limit Constant 13002.19876 7187.903864 1.8089 0.0893 -2235.476735 28239.87425 JCYL 0.05720163 0.026797739 2.1346 0.0486 0.000392961 0.1140103 Thus, if the export production number of passenger cars increases by 10,000 the export production number of commercial vehicles will go up by 572 (0.0572 coefficient). The constant 13,002 can be considered rather an anchor point for the regression line and should not be taken as the value of JCYF when JCYL is equal to 0 due to the fact that the data set available contains no observation with JCYF equals to or is close to 0. To project the export production number of passenger cars (JCYL), given export of commercial vehicles (JCYF), the JCYF value should simply be plugged into the regression line equation. The point of estimate number would be