= 12.140 5.8546p. Q – Using Excel, the equation for the demand function with 2.7 as the quantity in the second row is = 12.493 6.1545p. Q – 3.3 From the demand equation, is so the effect of an increase in on the –1.438, quantity of Camry’s demanded is = ΔQ –1.438(Δp) = ΔQ –1.438(1) = thousand. ΔQ –1.438 From the demand equation, if the price is $20 thousand, then demand is Q = 53.857–1.438p Q = 53.857–1.438(20) Q = 25.097 thousand The elasticity of demand is the percentage change in quantity divided by the percentage change in = ε . ε= From the demand equation, is so –1.438, . ε= = ε –1.146. 3.4 According to Excel, the price coefficient’s standard error is 0.0549, and the corresponding t-statistic is Statistically,an explanatory variable’s –12.477. coefficient is significantly different than zero at the 0.05 level if its is t-statistic larger than 1.96 in absolute value. In this example, the is 12.477, so the t-statistic price coefficient is statistically significant. REGRESSION SPECIFICATION 4.1 An explanatory variable’s coefficient is statistically significantly different than zero at the 0.05 level if its is larger than 1.96 in absolute value. In this t-statistic example, the is 4.25, so the price coefficient is statistically significant. t-statistic A year of experience raises CEO compensation by 36.10 thousand dollars, which seems economically significant. 4.2 To find the log-linear demand equation, take the log of both sides and simplify: . You can estimate this in Excel using an OLS regression of the form by converting your quantity and price data to logs. In the resulting regression, will α be an estimate of ln(A), and will be an estimate of β ε.
Q = 12.140 – 5.8546p. Using Excel, the equation for the demand function with 2.7 as the quantity in the second row is Q = 12.493 – 6.1545p. 3.3 From the demand equation, is –1.438, so the effect of an increase in on the quantity of Camry’s demanded is ΔQ = –1.438(Δp) ΔQ = –1.438(1) ΔQ = –1.438 thousand. From the demand equation, if the price is $20 thousand, then demand is Q = 53.857–1.438p Q = 53.857–1.438(20) Q = 25.097 thousand The elasticity of demand is the percentage change in quantity divided by the percentage change in ε = ε= . From the demand equation, is –1.438, so ε= . ε = –1.146. 3.4 According to Excel, the price coefficient’s standard error is 0.0549, and the corresponding t-statistic is –12.477. Statistically,an explanatory variable’s coefficient is significantly different than zero at the 0.05 level if its t-statistic is larger than 1.96 in absolute value. In this example, the t-statistic is 12.477, so the price coefficient is statistically significant. REGRESSION SPECIFICATION 4.1 An explanatory variable’s coefficient is statistically significantly different than zero at the 0.05 level if its t-statistic is larger than 1.96 in absolute value. In this example, the t-statistic is 4.25, so the price coefficient is statistically significant. A year of experience raises CEO compensation by 36.10 thousand dollars, which seems economically significant. 4.2 To find the log-linear demand equation, take the log of both sides and simplify: . You can estimate this in Excel using an OLS regression of the form by converting your quantity and price data to logs. In the resulting regression, α will be an estimate of ln(A), and β will be an estimate of ε.
Solution-Manual-for-Managerial-Economics-and-Strategy-2nd-Edition-by-Jeffrey-M-Perloff.docx