I am working on an economic project where I am creating a demand model for gasol

Question

I am working on an economic project where I am creating a demand
model for gasoline in one specific state. I've chosen the following dependent
variable: Quantity of Gasoline Demanded.

1. From an economic standpoint, does it make sense to include those variables?
2. I'm hypothesizing that there will be a negative relationship between Quantity Demanded and Historic Price of Gasoline and Average High Temperature and a positive relationship between quantity demanded and population and average income. Is that hypothesis correct?
3. I've uploaded my data as well as my regression results. Does it appear correct?

4. I need some help analyzing this data. How does the sign and magnitude related to the qty demanded? What about the significance of the estimated coefficients>
5. Does the model explain the data for both r^2 and F-stat?
6. What would the estimated model be?
7. What is the standard error of each variable? What are the T-values?
8. What is the point elasticity of each variable? (I assume a reasonable value will need to be chosen for this).

Historical Price of Gasoline Average High Average IncomeTemperature Date Qty of Gas Demanded Population >18 $36,183 $36,240 $36,297 $36,353 $36,410 $36,467 $36,523 $36,580 $36,637 $36,693 $36,750 $36,823 $36,895 $36,968 $37,040 $37,113 $37,185 $37,258 $37,330 $37,403 $37,475 $37,548 $37,620 $37,677 Jul-13 Jun-14 343,866,117 372,249,622 370,684,659 291,088,678 365,665,817 203,159,270 407,209,791 487,730,800 333,020,366 354,727,311 356,413,325 360,698,113 337,115,582 397,225,894 425,625,136 356,389,882 363,070,527 352,129,106 374,537,963 351,568,078 314,098,712 350,463,754 353,357,776 367,146,544 2,269,776 2,269,897 2,270,018 2,270,140 2,270,261 2,270,382 2,270,504 2,270,625 2,270,746 2,270,868 2,270,989 2,271,110 2,271,232 2,271,041 2,270,851 2,270,660 2,270,470 2,270,280 2,270,089 2,269,899 2,269,709 2,269,518 2,269,328 2,269,137 $3.38 $3.19 Nov-13 Dec-13 Jan-14 Feb-14 Mar-14 $3.17 $3.36 May-14 $3.52 Jul-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 $3.05 $2.78 $1.99 $2.09 $2.29 $2.32 $2.53 Jun-15

Explanation / Answer

Question 1

the variables you have chosen are good as historic prices will influence the quantity demanded, but as gasoline is necessary good so the quantity demanded will not much influence gasoline consumption. Other factors like population, income and average high temperature are also good estimators. As more populations will prompt more gasoline consumptions. similrly, more income will also prompt more gasoline consumption. temperture fall will also incrase gasoline consumption for fire production.

Question 2

Here as i have described in question 1. the alternative hypothesis would be there will be a negative relationship between Quantity Demanded and Historic Price of Gasoline and Average High Temperature and a positive relationship between quantity demanded and population and average income. Yes, You hypothesized it correctly.

Question 3

Here the regression results presented here are correct. Here noticable thing is that R - square is negative here. Which is the result of when the model contains terms that do not help to predict the response.

Question 4

Here in the question the Coefficients results for each X variable are coefficient of that varaibles. That means 1 unit chnage in these varaibles will create a change of that magnitude in the dependent variable which is Quantity of gase demanded.

Here all the p - values associated with the coefficients are more than 0.05 so none of the variables are significant here in nature.

Question 5

Here R- square = 0.0479 that means there are 4.79% variation is explained by given variables. which is too less.

Her F- stat = 0.2391 so which is also not significant in nature. So, overall this model is not significant.

Queston 6

The estimated model would be

Y = -33292099043 + 10764421 x1 + 14456.22328 x2 + 21345.49 x3 + 130518x4

Question 7

Estiamted error and t - variable are given here

for variable X1 =

SE = 73435706.83

t = 0.14658

for varaible X2

SE = 32396.0295

t = 0.4462

For variable X3

SE = 53919.72

t = 0.395875

for variable X4

SE = 1388956.97

t = 0.093968

Question 8

Point elasticisty of each variable is the coefficient of each variable will be

Point elasticity = The partial slope regression coefficient * ( independent variable / dependent variable)

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