The method in which experimenters observe the impact of an event, such as increased advertising or a sale price, on actual purchases made by experimental groups of consumers is called
Consumer focus groups
The consumer survey
A statistical demand analysis
The barometric method
In regression analysis, the existence of a high degree of inter-correlation among some or all of the explanatory variables in the regression equation constitutes
In regression analysis, the existence of a significant pattern in successive values of the error term constitutes
The standard deviation of the error terms in an estimated regression equation is known as
coefficient of determination.
standard error of the estimate.
When using a multiplicative power function (Y = a X1b1 X2b2 X3b3) to represent an economic relationship, estimates of the parameters (a, b's) using linear regression analysis can be obtained by first applying a ____ transformation to convert the function to a linear relationship.
semilogarithmic (using a logarithmic transformation on one side of the equation only)
double-logarithmic (using a logarithmic transformation on both sides of the equation)
When testing whether each of the independent variables (Xs) in a multiple regression equation is statistically significant in explaining variations in the dependent variable (Y), one uses the
One shortcoming of the use of ____ in demand analysis is that the participants are generally aware that their actions are being observed and hence they may seek to act in a manner somewhat different than normal.
consumer focus groups
statistical (econometric) methods
a and b
Question 8 The constant or intercept term in a statistical demand study represents the quantity demanded when all independent variables are equal to:
their minimum values.
their average values.
The estimated slope coefficient (b) of the regression equation (Y = a + bX) measures the ____ change in Y for a one ____ change in X.
The type of data or information obtained from focus groups is generally
the same as what would be obtained through telephone or in person consumer surveys.
One commonly used test in checking for the presence of autocorrelation when working with time series data is the
The identification problem arises in econometric estimation when which of the following is not accounted for in estimating the model?
The variance of the demand elasticity
The consistency of quantity demanded at any given point
The negative slope of the demand function
The simultaneous relationship between the demand and supply functions
The data in the table below are the results of a random sample of recent home sales in your neighborhood that your boss has asked you to use to estimate the relationship between the selling price of the house and the number of square feet in it.
Observation Number Sale Price (in thousands) Square Feet (in hundreds)
1 280 20.3
2 328 30.0
3 281 21.5
4 293 25.4
5 263 14.5
6 291 22.3
7 320 31.0
8 256 37.2
9 311 27.1
10 352 30.2
11 288 21.2
12 356 37.2
13 293 23.0
14 272 26.7
15 308 26.5
a. First plot the data, with number of square feet on the "X" axis and the price of the house on the "Y" axis. Explain why housing price is the dependent variable and square feet is the independent variable.
b. What is the estimated regression line? What does the coefficient of square feet represent?
c. Is the sample size large enough for the estimated coefficient of square feet to be statistically significant at the 5% level?
d. What is the coefficient of determination (R2)?
e. Perform an F-test, again at the 5% level.
You are given the following regression results estimating the demand for widgets based on time series data for the past 40 months.
Qt = 2.5 - 0.3 x Pt + 12 x Mt
Where Qt represents the quantity of widgets sold per period t, Pt represents the price of widgets during period t, and Mt represents average household income of customers during period t.
You are also given the following information about the regression results
R2 = 0.75 F-statistic = 23 Durbin-Watson (d) statistic = 0.66
standard deviation of constant = 0.52; standard deviation of P = 0.16
standard deviation of M = 2.0
a. Which of the independent variables are statistically significant at the 5% level?
b. Can you reject the null hypothesis that price does not affected quantity demanded? Can you reject the null hypothesis that income does not affect quantity demanded?
c. What proportion of total variation in Q is explained by the regression equation?
d. Is the F-statistic significant at the 5% level? What is the meaning of the F-statistic and F test?
The answers for each of the questions have been presented along with the rationale for the solutions.