Question 1

A simple regression model necessarily involves:&#10;A)one variable.&#10;B)more than one X variable.&#10;C)more than one Y variable.&#10;D)none of these.

Accepted Answer

A simple regression model involves one independent variable (X) and one dependent variable (Y), so none of the given options correctly describe a simple regression model.

Question 2

Tests of the b < 0 hypothesis are:&#10;A)tests for the share of dependent variable variation explained by the regression model.&#10;B)one-tail F tests.&#10;C)two-tail t tests.&#10;D)tests of direction or comparative magnitude.

Accepted Answer

Tests of direction or comparative magnitude are the only ones that directly test the hypothesis that the coefficient of the independent variable is negative (b < 0). One-tail F tests and two-tail t tests are used to test the significance of the coefficient, but do not specifically test the direction of the relationship. Tests for the share of dependent variable variation explained by the regression model (such as R-squared) are used to evaluate the overall fit of the model, but do not test the specific hypothesis that b < 0.

Question 3

Holding all else equal, the corrected coefficient of determination rises with:&#10;A)an increase in the number of estimated coefficients.&#10;B)a decrease in sample size.&#10;C)an increase in R2.&#10;D)an increase in the standard error of the estimate.

Accepted Answer

The coefficient of determination, R2, measures the proportion of variance in the dependent variable that can be explained by the independent variables. Therefore, an increase in R2 indicates an improvement in the model's ability to explain the dependent variable, leading to a higher corrected coefficient of determination. A and B are incorrect because they would decrease the corrected coefficient of determination. D is incorrect because the standard error of the estimate measures the uncertainty in the regression coefficients, not the explained variance. The coefficient of determination (R2) measures the proportion of variation in the dependent variable that is explained by the independent variables. Therefore, an increase in R2 means that more of the variation in the dependent variable is explained, which leads to a higher corrected coefficient of determination. Choices A and B are incorrect because the number of estimated coefficients and sample size affect the standard error of the estimate, which is used to calculate the corrected coefficient of determination but not the R2. Choice D is also incorrect because an increase in the standard error of the estimate means that there is more variance in the residuals, which reduces the explained variation and leads to a lower R2 and a lower corrected coefficient of determination.

Question 4

Endogenous determinants of demand include:&#10;A)competitor prices.&#10;B)the weather.&#10;C)interest rates.&#10;D)price.

Accepted Answer

Endogenous determinants of demand are factors that originate within the market and affect consumers' willingness and ability to buy a product or service. Price is one of the key endogenous determinants of demand, as an increase in price will typically result in a decrease in demand due to the law of demand. Endogenous determinants of demand are factors that are internal to the market and directly affect the quantity demanded of a good or service. Price is one such factor, as a change in price will result in a change in the quantity demanded. The other options, competitor prices, the weather, and interest rates, are all exogenous determinants of demand as they are external factors that affect demand but are not directly related to the market or the particular good or service being sold.

Question 5

A measure of the statistical significance of individual coefficient estimates is the:&#10;A)coefficient of determination.&#10;B)t statistic.&#10;C)corrected coefficient of determination.&#10;D)F statistic.

Accepted Answer

The t statistic measures the significance of individual coefficient estimates by comparing them to their standard errors. The t statistic measures the significance of individual coefficient estimates in a regression model, and is calculated by dividing the coefficient estimate by its standard error.

Question 6

An uncertain relation that is true on average is a:&#10;A)statistical relation.&#10;B)deterministic relation.&#10;C)cross-section relation.&#10;D)time-series relation.

Accepted Answer

A statistical relation describes a relationship between two or more variables that is not certain, but is true on average or in the long run. It is based on statistical analysis and probability, rather than on deterministic principles. An uncertain relation that is true on average is a statistical relation. This means that there may be variability in the individual data points, but overall there is a consistent pattern or trend in the data when considered as a whole.

Question 7

The percentage of variation in the dependent Y variable explained by all independent X variables as a group, after controlling for sample size and the number of estimated coefficients, is given by:&#10;A)R2.&#10;B)corrected R2.&#10;C)SEE.&#10;D)Regression

Accepted Answer

The corrected R2 takes into account the sample size and the number of estimated coefficients, making it a more accurate measure of the true explanatory power of the independent variables. R2 does not account for these factors and may overestimate the explanatory power. SEE is the standard error of estimate, which measures the average distance between the predicted values and the actual values, and regression just refers to the overall statistical model used to analyze the relationship between the dependent and independent variables. The corrected R2 takes into account the sample size and the number of estimated coefficients, giving a more accurate measure of the true relationship between the variables. R2 is not adjusted for the number of variables in the model, and SEE (standard error of estimate) and Regression are not measures of explained variation.

Question 8

A multiplicative model implies:&#10;A)a constant effect of X on Y.&#10;B)changing elasticity.&#10;C)a log-linear relation.&#10;D)a constant effect of Y on X.

Accepted Answer

A multiplicative model implies that the relationship between X and Y is log-linear, meaning that a change in X leads to a proportional change in Y, rather than a constant effect. A multiplicative model involves taking the logarithm of Y and treating it as the dependent variable. This results in a log-linear relationship where the effect of X on Y is not constant but changes depending on the level of X. Therefore, option C is the correct answer which states that a multiplicative model implies a log-linear relation. Option A is incorrect as it implies a constant effect of X on Y. Option B is incorrect as it implies changing elasticity which is true for a multiplicative model. Option D is also incorrect as it states a constant effect of Y on X which is not relevant to a multiplicative model.

Question 9

In a simple regression model, the correlation coefficient:&#10;A)shows the effect of X on Y.&#10;B)shows the effect of Y on X.&#10;C)is greater than one.&#10;D)is the square root of the coefficient of determination.

Accepted Answer

The correlation coefficient is indeed the square root of the coefficient of determination (R^2), which measures the proportion of the variance in the dependent variable that is predictable from the independent variable.

Question 10

In a linear demand model, the income elasticity of demand can be influenced by:&#10;A)income.&#10;B)interest rates.&#10;C)price of other goods.&#10;D)all of these.

Accepted Answer

The income elasticity of demand measures the responsiveness of quantity demanded to changes in income. Therefore, it is influenced by income only. Interest rates and the price of other goods may affect demand for a particular good, but they do not specifically influence the income elasticity of demand.

Deck 5: Demand Estimation