In estimating the regression in problem #2, you are also concerned that the t-statistics may be inflated because of the presence of conditional heteroscedasticity. You conduct a regression of the squared residuals against the dummy variables X1, X2, and X3 and find that for the squared residuals regression: Multiple R 0.4145 R Square 0.1718 Adjusted R Square 0.1600 SEE 92.3760 Conduct a test at the level to see if conditional heteroskedasticity is present

In estimating the regression in problem #2, you are also concerned that the t-statistics may be inflated because of the presence of conditional heteroscedasticity. You conduct a regression of the squared residuals against the dummy variables X1, X2, and X3 and find that for the squared residuals regression: Multiple R 0.4145 R Square 0.1718 Adjusted R Square 0.1600 SEE 92.3760 Conduct a test at the level to see if conditional heteroskedasticity is present

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.6: Higher-degree Polynomials And Rational Functions

Problem 1TU: The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t=...

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The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
) In estimating the regression in problem #2, you are also concerned that the t-statistics may be inflated because of the presence of conditional heteroscedasticity. You conduct a regression of the squared residuals against the dummy variables X1, X2, and X3 and find that for the squared residuals regression: Multiple R 0.4145 R Square 0.1718 Adjusted R Square 0.1600 SEE 92.3760 Conduct a test at the level to see if conditional heteroskedasticity is present In view of your answer for a), what needs to be done?
) In estimating the regression in problem #2, you are also concerned that the t-statistics may be inflated because of the presence of conditional heteroscedasticity. You conduct a regression of the squared residuals against the dummy variables X1, X2, and X3 and find that for the squared residuals regression: Multiple R 0.4145 R Square 0.1718 Adjusted R Square 0.1600 SEE 92.3760 Conduct a test at the level to see if conditional heteroskedasticity is present and from your results, what needs to be done?
) In estimating the regression in problem #2, you are also concerned that the t-statistics may be inflated because of the presence of conditional heteroscedasticity. There are 214 observations and 3 independent variables. You conduct a regression of the squared residuals against the dummy variables X1, X2, and X3 and find that for the squared residuals regression: Multiple R 0.4145 R Square 0.1718 Adjusted R Square 0.1600 SEE 92.3760 Conduct a Breusch–Pagan test at the 0.05 level to see if conditional heteroskedasticity is present and from your results, what needs to be done?
Cascade Pharmaceuticals Company developed the following regression model, using time-series data from the past 33 quarters, for one of its nonprescription cold remedies: Y=−1.04+0.24X1−0.27X2�=−1.04+0.24�1−0.27�2 where Y� = quarterly sales (in thousands of cases) of the cold remedy X1�1 = Cascade's quarterly advertising (× $1,000) for the cold remedy X2�2 = competitors' advertising for similar products (× $10,000) Here is additional information concerning the regression model: sb1=0.042�b1=0.042, sb2=0.070�b2=0.070, R2=0.720�2=0.720, se=1.63��=1.63, F-statistic=31.402F-statistic=31.402, and Durbin-Watson (d) statistic=0.499Durbin-Watson (d) statistic=0.499. Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the cold remedy? (Hint: t0.05/2,33 - 3=2.042�0.05/2,33 - 3=2.042.) Check all that apply. X1�1 X2�2 What proportion of the total variation in sales is explained by the regression…
Consider the following sample regression equation yˆ = 150 − 20x, where y is the demand for Product A (in 1,000s) and x is the price of the product (in $). If the price of Product A is $5, then we expect demand to be ________.
You have obtained a sub-sample of 1744 individuals from the Current Population Survey (CPS) and are interested in the relationship between weekly earnings and age. The regression, using heteroskedasticity-robust standard errors, yielded the following result: = 239.16 + 3.75× Age, R2 = 0.15, SER = 287.21., where Earn and Age are measured in dollars and years respectively. Interpret the intercept? Interpret the slope coefficient b) Is the effect of age on earnings large? The average age in this sample is 37.5 years. What is annual income in the sample? (e) Interpret the measures of fit.
Suppose that you perform a hypothesis test for the slope of the population regression line with the null hypothesis H0: ß1 = 0 and the alternative hypothesis Ha: ß1 ? 0. If you reject the null hypothesis, what can you say about the utility of the regression equation for making predictions?
Cascade Pharmaceuticals Company developed the following regression model, using time-series data from the past 33 quarters, for one of its nonprescription cold remedies:Y = -1:04 + 0:24X1 - 0:27X2where Y = quarterly sales (in thousands of cases) of the cold remedyX1 = Cascade’s quarterly advertising (× $1,000) for the cold remedyX2 = competitors’ advertising for similar products (× $10,000)Here is additional information concerning the regression model:sb1 = 0:032 sb2 = 0:070R2 = 0:64 se = 1:63 F-statistic = 31:402Durbin-Watson (d) statistic = 0.4995a. Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the cold remedy?b. What proportion of the total variation in sales is explained by the regression equation?c. Perform an F-test (at the 0.05 level) of the overall explanatory power of the model.d. What additional statistical information (if any) would you find useful in the evaluation of this model?
You conduct a regression of the squared residuals against the dummy variables X1, X2, and X3 and find that for the squared residuals regression: Multiple R 0.4145 R Square 0.1718 Adjusted R Square 0.1600 SEE 92.3760 Conduct a test at the level to see if conditional heteroskedasticity is present In view of your answer for a), what needs to be done?
Why the following assumptions must be met in order for us to use linear regression? The errors e1,…,en are random and independent. In particular, the magnitude of any error ei does not influence the value of the next error ei + 1. The errors e1,…,en all have mean 0. The errors e1,…,en all have the same variance, which we denote by o2. The errors e1,…,en are normally distributed