Biofuel 2019. A biofuel is a fuel produced from biomass through contemporary processes and is one of the largest sources of renewable energy in use today. In the transport sector, they are blended with existing fuels such as gasoline and diesel (that are in contrast produced by the very slow geological processes). The data for the production of biofuel in 2019 by different countries (in millions of liters) depending on the amount of usage (in millions of liters), the amount of export (in millions of liters), the average producer price

($ per liter), and the area harvested (in thousands of hectare)

is collected. Here is the resultant multiple regression model:.

Regression Statistics Multiple R 0.9962 R Square 0.9924 Adjusted R Square 0.9910 Standard Error 3624.2259 Observations 27 ANOVA df SS MS F Significance F

Regression 4 37,616,179,457.7241 9,404,044,864.4310 715.9524 0.0000 Residual 22 288,970,301.9621 13,135,013.7256 Total 26 37,905,149,759.6862 Variable Coefficient SE(Coeff) t-ratio P-value Intercept -3791.7130 943.9261 -4.0170 0.0006 Usage 0.9069 0.0489 18.5519 0.0000 Export 0.9487 0.1487 6.3797 0.0000 Producer price -0.0001 0.0006 -0.0956 0.9247 Area harvested 0.4124 0.2296 1.7961 0.0862

a) Write the regression model.

b) Are the assumptions and conditions met?

c) State and test the standard null hypothesis for the coefficient of Producer Price. Use the standard-level of a = 0.05, and state your conclusion.

d) What effects does your observation in response to part b have on your test in part c?

e) If you have statistics program, open the data file Biofuel 2019. Look at the plots of individual variables and pairs of variables from this analysis to see if you can find the outliers.
What do you conclude about these outliers?

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Frequency Resdual -6000 -8000 Residuals Plot 6000 4000 2000 -20000 -2000 -4000 20000 40000 60000 80000 100000 120000 140000 100000 -10000 -12000 Predicted Histogram 12 10 8- T 6 4 2. 0+ -8022.91 -4867.14 -1711.37 1444.40 1444.40 4600.17 Residuals