9.‘e_raz)Vjt Alet c j 54-1 0144 ,A11 Oil Problem — A small sample data is collected for two variables. Y represents the price of all gas types per gallon (Bureau of Labor Statistics), and X represents the average price of crude oil per barrel (Department of energy), for a period of ten years, Yr2000 1.56 27.39 2001 1.53 23.00 2002 1.44 22.81 2003 1.64 27.69 2004 1.92 37.66 2005 2.34 50.04 2006 2,63 58.30 2007 2.85 64,20 2008 3.32 91,48 2009 2.40 53.481, Plot the Scatteraram. 2. Fit a model that best fits the data, using Least Squares Method and obtain the summary output. 3. Write the Least Squares equation. 4. Predict the price of gas at the pump in a year in which the price of crude oil is $100 per bbl. 5. Superimpose the Least Squares Line in the scatter diagram above. 6. Interpret the intercept and slope in the context of the problem. 7. What is the value of SSE and what does it mean? 8. What is the value of the variance of regression, S2. and what does it mean in the context of the problem. What other notation (abbreviation) is used to represent the variance of regression? 9. What is the value of S, the standard error of estimate. and explain its meaning. 10. What is the value of the coefficient of correlation and what does it mean in the context of the Problem. 11. Compute the coefficient of determination and interpret its meaning in the context of the problem.Note that the above are not steps required in every regression analysis. These are Just some questions posed in this p-oblein.
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