Chapter 5 True or False Quiz

. Cross-sectional data are made up of observations that are collected across a period of time.

. The demand curve for a commodity can generally be approximated by drawing a graph with price on the horizontal axis and quantity on the vertical axis, plotting a series of points that represent observed combinations of price and quantity, and then drawing lines that connect the points.

. If the price of a commodity rises and the quantity sold increases, it does not prove that the demand curve for the commodity slopes upward.

. If the supply curve for a commodity shifts while the demand curve does not shift, then the demand identification problem will not be encountered.

. The identification problem is dealt with in practice by including all of the determinants of demand in the estimated demand function.

. Observational research involves questioning a sample of consumers about their responses to actual and potential market conditions.

. One advantage of consumer clinics over market experiments is the ability to control the environment and screen out the effects of external events.

. A market experiment is carried out by providing consumers with a sum of money that must be spent in a simulated store.

. The use of electronic devices designed to gather information about which television stations people are watching is an example of observational research.

. A scatter diagram is a graph of a linear function.

. In the linear function Y = a + bX, Y is the intercept and X is the slope of the function.

. The slope of a linear function is equal to the change in the dependent variable divided by the corresponding change in the independent variable.

. The Y intercept of a linear function is equal to the value of X when Y is equal to zero.

. If a one unit increase in the value of X results in a two unit decrease in the value of Y, then b = -2.

. If a linear function that is plotted on a graph passes through the origin of a graph, then b = 0.

. If a regression line that was calculated by ordinary least squares is plotted on a scatter diagram, all of the points in the data set will be on the line.

. A regression line that is calculated by ordinary least squares will have an intercept and slope that minimize the sum of the squared differences between the observed value of the Y variable and the regression line.

. Unexplained variation in the Y variable is denoted et.

. If OLS is used to estimate a linear function, then the sum of the et will always be equal to zero.

. One of the crucial assumptions of regression analysis is that the error term has a normal probability distribution.

. A significance test on the slope coefficient using the t ratio tests the hypothesis that the slope is equal to zero.

. If the absolute value of the t ratio is larger than the t value taken from the table, then the conclusion is that the slope does not differ from zero.

. A t test on the slope takes, as its alternative hypothesis, the position that there is no relationship between the dependent variable and the relevant independent variable.

. For a given sample size, the more independent variables are incorporated in a regression model, the more degrees of freedom the relevant t distribution has.

. The significance level of a t test on the slope of a simple linear regression equation measures the probability of drawing an incorrect conclusion when the test indicates that X and Y have a significant relationship.

. The coefficient of determination is equal to the explained variation in the Y variable divided by the unexplained variation in the Y variable.

. Ordinary least squares minimizes the total variation in the dependent variable.

. Ordinary least squares maximizes the coefficient of determination.

. If R2 is equal to one, then the coefficient of correlation must also be equal to one.

. The adjusted coefficient of determination is generally larger than the unadjusted coefficient of determination.

. Omission of an important independent variable from a multiple regression model tends to bias estimates.

. The more explanatory power a regression model has, the smaller the F test statistic is.

. The F test has, as its null hypothesis, the proposition that none of the estimated slope coefficients is different from zero.

. If the F test statistic is greater than the appropriate critical value, it means that at least one of the estimated slope coefficients is significantly different from zero.

. The standard error of the regression line is an estimate of the standard deviation of the dependent variable relative to the regression line.

. Multicollinearity refers to a situation in which two or more of the independent variables in a regression model are highly correlated.

. Heteroskedasticity refers to violation of the assumption that the mean of the error terms is zero.

. Autocorrelation refers to a situation that is often encountered in time-series data.

. The Durbin-Watson statistic is used to test the significance of the standard error of the regression.

. If the observed values of the dependent and independent variables are transformed into their natural logarithms, then each estimated slope coefficient is equal to the elasticity of the dependent variable with respect to the corresponding independent variable.

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