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# Plots and multiple choice

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I have some economics questions that I need help with. They are in the attached file. Thanks!

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https://brainmass.com/economics/supply-and-demand/plots-and-multiple-choice-397488

#### Solution Preview

1. 1. The annual Supply and demand for the Paper Firm is given by:

QS = 100P - 5000
and
QD = 0.5 I + 0.2 A - 100P + 5000
where Q is the quantity per year, P is price, I is income per household, and A is advertising expenditure.

a. If A = \$10,000 and I = \$25,000, what is the demand curve?
QD = 12500 + 2000 - 100P+ 5000
=19500-100P

b. Plot the demand curve found in part a with the supply curve, then use the graph to find the equilibrium price and quantity.
See the attached file. The equilibrium price is about 122.5 and the equilibrium quantity is about 7250.

c. If consumer incomes increase to \$30,000, what will be the new equilibrium price and the new equilibrium quantity?
The new demand curve is QD = 15000 + 2000 - 100P + 5000 = 22000 -100P
The equilibrium price is
100P - 5000=22000-100P
200P = 27000
P = 135

2. What will be the effect on the demand curve, and what will happen to market equilibrium price and quantity in the short run if, Consumers expect that the price of the good will be higher in the future. (Points : 2)
Demand increases ; equilibrium price and quantity increase.
An expectation of higher prices causes the demand curve to shift outward. This results in higher prices and quantity sold.

3. From 2001 to 2004, the real price of eggs decreased and the total annual consumption of eggs decreased. Which of the following would cause an unambiguous decrease in the real price of eggs and an unambiguous decrease in the quantity of eggs ...

#### Solution Summary

Multiple choice questions related to equilibrium price and quantity; plotting of a firm's demand curve.

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## Multiple choice question form regression analysis

Please determine if the question is true or false. If the question is false than give a brief description why

T F 1. One of the objectives of simple linear regression is to predict the value of the independent variable X as a linear function of the dependent variable Y.

T F 2. Regression analysis is limited to establishing a relationship between two variables, X and Y.

T F 3. If a deterministic relationship exists between two variables, x and y, any value of x that is selected will determine a unique value of y.

T F 4. When trying to uncover relationships between variables, the recommended practice is to construct a scatter plot first before conducting a statistical analysis.

Use the following scatter plot to answer questions 5 - 9.

T F 5. The dependent variable shown in the plot is the selling price of the real estate property.

T F 6. The plot shows a total of 10 pairs of observations that incorporate the two variables, living area and selling price.

T F 7. The relationship between the two variables, living area and selling price, is such that a decrease in living area is accompanied by a decrease in selling price.

T F 8. There is likely a strong relationship between the two variables, living area and selling price, so a linear model is appropriate.

T F 9. Assuming the data was derived from a subdivision of houses, one would expect to see a selling price of \$300,000 for a house that has 2,200 sq. ft. of living area.

T F 10. Whenever regression analysis is used to predict values of Y that are within the range of the X data, the process is known as interpolation.

T F 11. Extrapolation is most advisable if it is difficult to predict what the data relationship actually is beyond the range of the existing observations.

T F 12. Residuals can be computed by taking the difference between observed and predicted values of Y and squaring them to eliminate negative numbers.

T F 13. The sum of the residuals that surround a regression line will always be greater than or equal to zero.

T F 14. The stronger the relationship between X and Y, the closer the plotted points will be to the regression line.

T F 15. If two variables are highly correlated, the correlation coefficient will be at or near zero.

T F 16. The power of regression analysis is best illustrated by the fact that the presence of outliers has practically no impact on the values of the coefficients or their standard deviations.

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