Intercept 58.87 15.33 3.84 0.00 28.59 89.15 of X -1.64 0.85 -1.93 0.06 -3.31 0.04 Income (‘000s) 1.11 0.24 4.64 0.00 0.63 1.56 3-1 . Only the coefficients for the Intercept and Income are statistically significant at the 5 percent level or better. The is quite low, indicating that the model explains only 14 percent of R-square the total variation in demand for X. The adjusted is only marginally R-square lower (13 percent), suggesting that the is not the result of an excessive R-square number of estimated coefficients relative to the sample size. The F-statistic, however, suggests that the overall regression is statistically significant at better than the 5 percent level. The approximate 95 percent confidence interval for is . Thus, you can be 95 percent a confident that is within the range of 17 and 27. The approximate 95 percent a confidence interval for is . Thus, you can be 95 percent confident that is within b b the range of and –3.2 –0.4. The statistics are as follows: ; ; and . t the coefficient estimate, , is not statistically different from zero. Since, the coefficient estimate, , is statistically different from zero. Finally, since the coefficient estimate, , is not statistically different from zero. The R-square tells us that 35 percent of the variability in the dependent variable is explained by price and income. The own-price elasticity of demand is -2.18, so demand is elastic. The income elasticity of demand is 0.34, so X is a normal good. The result is not surprising. Given the available information, the own price elasticity of demand for major cellular telephone manufacturer is . Since this number is greater than one in absolute value, demand is elastic. By the total revenue test, this means that a reduction in price will increase revenues. The regression output is as follows: Table 3-2
Intercept 58.87 15.33 3.84 0.00 28.59 89.15 of X -1.64 0.85 -1.93 0.06 -3.31 0.04 Income (‘000s) 1.11 0.24 4.64 0.00 0.63 1.56 3-1 . Only the coefficients for the Intercept and Income are statistically significant at the 5 percent level or better. The R-square is quite low, indicating that the model explains only 14 percent of the total variation in demand for X. The adjusted R-square is only marginally lower (13 percent), suggesting that the R-square is not the result of an excessive number of estimated coefficients relative to the sample size. The F-statistic, however, suggests that the overall regression is statistically significant at better than the 5 percent level. The approximate 95 percent confidence interval for a is . Thus, you can be 95 percent confident that a is within the range of 17 and 27. The approximate 95 percent confidence interval for b is . Thus, you can be 95 percent confident that b is within the range of –3.2 and –0.4. The t statistics are as follows: ; ; and . the coefficient estimate, , is not statistically different from zero. Since, the coefficient estimate, , is statistically different from zero. Finally, since the coefficient estimate, , is not statistically different from zero. The R-square tells us that 35 percent of the variability in the dependent variable is explained by price and income. The own-price elasticity of demand is -2.18, so demand is elastic. The income elasticity of demand is 0.34, so X is a normal good. The result is not surprising. Given the available information, the own price elasticity of demand for major cellular telephone manufacturer is . Since this number is greater than one in absolute value, demand is elastic. By the total revenue test, this means that a reduction in price will increase revenues. The regression output is as follows: Table 3-2
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