Decision Making From Data Anaylsis

¶ … Decision Making This section analyzes the farm data of the Department of Agriculture and explores the relationship between the total number of the U.S. farms (x) and average farm size (y). The paper carries out the regression analysis for the interpretation. From the analysis, the average farm size (y) is the dependent variable and U.S. farms (x) is the independent variable.

The paper uses the following equation to determine the relationship between y (dependent variable) and x (the independent variable): y =bx +a The summary output of the regression analysis is as follows: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error 11,10 Observations 59,00 ANOVA df SS MS F Significance F Regression 1,00 304747,47 304747,47 2474,96 0,00 Residual 57,00 7018,53 123,13 Total 58,00 311766,00 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0% Intercept 598,61 4,55 131,58 0,00 589,50 607,72 589,50 607,72 Number of U.S.

Farms 0,00 0,00 -49,75 0,00 0,00 0,00 0,00 0,00 Interpretation of the Regression Analysis Based on the output of the regression analysis, the R-Square is 98% showing that there is 98% chance that average farm size in acre is projected to increase as the number of the U.S. farms decline. The result of the analysis reveals that as the number U.S. farms decline, the average size of acre increases. The paper suggests that the federal government should offer financial incentives to farmers to increase the number of the U.S. farms.

This policy will assist the United States to increase their agricultural exports globally, which will assist the government to earn foreign exchange and improve the country GDP (gross domestic product). 2a. This section intends to predict the peak power load (y) which refers to the maximum amount of power that should be generated daily to meet the demand where (x) is the function of the generated daily high temperature. The data reveals a chosen sample of 25 summer days, the high temperature, and peak power load are recorded daily.

Fig 1 provides the scatter plot revealing extent of the relationship between the two variables. Fig 1: Scatter Plot showing Relationship btw Power Load and the Daily High Temperature As being revealed in fig 1, there is a very little linear correlation between the dependent and independent variable. A scatter plot is a set of bivariate analysis that gives a visual presentation of the relationship between dependent and independent variables, which assists in the interpretation of regression model.

b) The regression equation is as follows: Regression equation: Y= -0.8711x +136.75 By substituting the value of x in the equation, the paper reveals that the peak load is 101.6 and 96.5 megawatts when the daily temperature is 67 degree as being revealed in the table below: Day Peak Load Daily High Temperature (x) Substitution from y= -0.8711x +136.75 Peak Power Load 1 118,5 89 =-0,8711*89 +136,75 59,2 2 136,0 94 =-0,8711*94 +136,75 54,9 3 143,6 =-0,8711*100 +136,75 49,6 4 153,2 97 =-0,8711*97 +136,75 52,3 5 140,7 95 =-0,8711*95 +136,75 54,0 6 151,9 =-0,8711*100 +136,75 49,6 7 135,1 92 =-0,8711*92 +136,75 56,6 8 178,2 =-0,8711*106 +136,75 44,4 9 101,6 67 =-0,8711*67 +136,75 78,4 10.