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Gasoline demand to the year 2017
Economics 7 Pages

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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