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Gasoline Demand To The Year 2017

Question

I want a forecast written report of 6 pages on the uploaded data.

	year	2017 (projection)	R2	linear regression
AK	ARICB	972.056	0.0054	y = -22.232x + 45814
AL	ARICB	31028.01	0.0217	y = -127.47x + 288135
AR	ARICB	5764.583	0.0147	y = -58.201x + 123156
AZ	ARICB	24279.14	0.0727	y = 207.42x - 394087
CA	ARICB	443299.57	0.0715	y = -771.79x + 2E+06
CO	ARICB		0.2595	y= -501.09x + 1E+06
CT	ARICB		0.2772	y= -296.77x + 601768
DC	ARICB		0.3859	y= -2.8686x + 5896.6
DE	ARICB		0.2644	y= 129.35x - 255978
FL	ARICB		0.1842	y= -652.23x + 1E+06
GA	ARICB		0.0691	y = -236.48x + 509641
HI	ARICB		0.2644	y = 129.35x - 255978
IA	ARICB		0.0502	y = 115.89x - 218026
ID	ARICB		0.0031	y = -33.842x + 80002
IL	ARICB		0.0537	y = -330.97x + 718397

On the gas info data ( the chart is in the Excel file I sent. The data is in a plain word doc that you should be able to open. The y=mx+b equations for each state are for the given MSN code, which is labeled.

Solution

Title: Gasoline Demand To The Year 2017
Length: 7 pages (1925 Words)
Style: MLA

Preview

Forecasting The Demand of Gasoline to 2017

It is possible to forecast the demand of gasoline to the year 2017 using various econometrics techniques. The data that shows the consumption of gasoline through history is time series data and through rigorous econometrics techniques, it is possible that we estimate future demands given different variables.

The methods that are employed hereafter to forecast the demand of gasoline are, causal economic time series, traditional time series (moving average), trend time series, multivariate causal economic cross sectional regression forecast. For accurate forecasting the regression models have to be well defined and thought of.

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