Question about demand function/demand curve

@James010 – any images you can take and upload to imgur here?

To add a bit on @Sascha’s explanation, in the example given we have an equation that represents how the quantity of gasoline (Qx) varies with the price of gasoline (Px) as well as the other two variables: income (I) and the price of cars (Py). The coefficients in the equation (-0.4 for the own-price Px, +0.06 for the income, and -0.01 for the price of cars) represent the dependence of the quantity of gasoline demanded on those 3 variables. In a way this is a model that a gasoline retailer could use to help figure out how much gasoline he should buy (to resell to consumers) and at what price he should offer the gasoline. It is a model in that the coefficients might be slightly different than the ones presented, and they are likely to be estimated using statistical methods (something we’ll see in LII: regression analysis).

The microeconomic analysis of the example shows the model as given for illustration purposes: it makes sense that the signs are what they are (if income is raised, expect more gasoline to be bought; if the price of cars go up, expect less gasoline; if the gasoline price goes up, expect less gasoline).

@james010,

Strictly speaking, the intercept value of 8.4 represents the amount of gasoline that would be purchased if the gasoline price, the income, and the price of cars (the 3 independent variables in this model) were all zero. Of course, it’s nonsensical to think about such a situation. However, one may get to an estimate such as 8.4 for the intercept by looking at real data and coming up with a relationship between this dependent variable (quantity demanded for gasoline) and the independent variables, using statistical methods.

I would not say it’s a “made up” number; I prefer to think of it as a “measured” number where the measurement is not direct because you cannot set prices and income to zero to observe the quantity demanded. So you measure it indirectly by observing the quantity demanded for different combinations of {Px, Py, I}, and make it fit in a linear functional relationship.

Hope this makes sense to you…

Just want to add to the very good advice already mentioned. In plain terms, the position in the equation where the 8.4 is (the beta intercept) usually represents a fixed-cost, base-price or base-amount to which all other market (explanatory) variables affect.

In your example, Px, I, Py are the explanatory (market) variables that change 8.4 which, in turn, result in an estimated quantity demanded.

The coefficients (-0.4, +.06, -.01) attached to each explanatory variable tell us what ratio of that variable will change the intercept (and in what direction, + or – ). Here, 8.4 will be decreased by 40% of Px, thus decreasing estimated quantity demanded.

It is not so confusing in the real world; instead of Px, we would simply denote the variable as GAS . :-” “

Hi James,

It is a hypothetical equation in both cases, so these numbers are simply made up.

In case of Q(dx) = 8.4 – 0.4Px + 0.06 I – 0.01Py, note that 0.01 is not the average price of a car in thousands. Py is the average price of a car in thousands.

Also, don’t worry too much about the numbers here, it is more important to understand how the variables interact, in this case for example: as the price of cars goes up, the quantity of gasoline demanded goes down.

Hope this helps 🙂