Time series regression with ARMA errors always provides narrower prediction intervals than seasonal arima models (differencing). True False
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- 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?3- Heteroskedasticity refers to the residuals from a regression model Select one:Being linearBeing independentHaving a constant varianceHaving time-dependent varianceClear my choice1) What is the probability of a stroke over the next 10 years for John Smith, a 68-year-old smoker who has blood pressure of 175? What action might the physician recommend for John to reduce the risk of stroke? 2) Is there any multicollinearity problem in the above multiple regression model? How do you know?
- 32. What effect on the results of a regression does data that exhibits heteroscedasticity cause? Higher risk of type I error Lower risk of type I error Higher risk of type II errorIn simple linear regression, most often we perform a two-tail test of the population slope 1 to determine whether there is sufficient evidence to infer that a linear relationship exists. The null hypothesis is stated as:Which of the following would you expect to be a problem associated with adding lagged regressors into a regression equation in order to “cure” autocorrelation? Select one: a. The assumption that the regressors are non-stochastic is violated. b. Adding lags may induce multicollinearity with current values of variables. c. The standard errors of the coefficients will fall as a result of adding more explanatory variables. d. A model with many lags may lead to residual non-normality.
- If in a multivariate regression model with significant F-value, all the estimated parameters have computed-t values less than 1, then the model is likely to have _____. Select one: a. partial correlation for each regressor is insignificant b. some or all the regressors are linearly dependent c. a problem of multicollinearity d. all of the aboveWhich of the following is not a formal condition for inference about a regression estimate? Sampling independence of bivariate observations Linearity between variables X and Y Normality in the distribution of residuals Heteroscedasticity between X and Y variablesDoes the sugar cane model suffer from heteroscedasticity? Perform a Breusch-Pegan test as well as a Whitetest to verify what the residual plots suggests, based on the following regression results:
- method that uses a weighted average of past values for arriving at smoothed time series values is known as ______. a. decomposition b. regression analysis c. exponential smoothing d. deseasonalizationIn multiple linear regression, the Variance Inflation Factor (VIF) measures multicollinearity among the X-variables. What is multicollinearity, and why do we want to avoid it?Can logistic regression be used in both prospective and retrospective study? Will the odds ratios of the effect of the predictor on the response are different under these two study designs? What formula can we use to solve maximum likelihood estimate of regression coefficient beta in Poisson regression (with log link)? can ridge regression be applied if sample size is smaller than the number of predictors? if any 2 variables in X1, X2 AND Y have a positive correlation, then in the linear regression Y = b0 + b1X1 +b2X2 +e, will the sign of b1 and b2 both be positive? will the residuals that we get from linear regression will always be uncorrelated given X?