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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?What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?1) 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?
- 24) If the R-square value for a simple linear regression model is 0.80 and the regression line has anegative slope, the correlation coefficient describing the relationship between the two variables is_____________.Are boys, on average, heavier than girls at birth, all other factors beingequal? If so, by how much? Is the di§erence between boys and girlsestimated from this regression statistically significant at the 5% level?The following monthly sales of chocolate boxes (in thousands of AUS dollars) have been recorded for January, February, March, and April, respectively: 8.5, 8, 8, 9. Examining the forecasting accuracy for the month of April only, explain which of the following forecasting method would you recommend: the Naïve method, the Average method, or the Simple exponential smoothing method (assuming alpha=0.85 and initial state of 8)?
- 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?Whenever the slope of a regression line is zero, the correlation coefficient will also be zero. True FalseThe following monthly sales of chocolate boxes (in thousands of AUS dollars) have been recorded for January, February, March, and April, respectively: 9.5, 8, 9, 9. Examining sales forecast accuracy for the month of April only, explain which of the following forecasting method would you recommend: the Naïve method, the Average method, or the Simple exponential smoothing method (assuming alpha=0.8 and initial state of 8.5)?
- Suppose an appliance manufacturer is doing a regression analysis, using quarterly time-series data, of the factors affecting its sales of appliances. A regression equation was estimated between appliance sales (in dollars) as the dependent variable and disposable personal income and new housing starts as the independent variables. The statistical tests of the model showed large t-values for both independent variables, along with a high r2 value. However, analysis of the residuals indicated that substantial autocorrelation was present.a. What are some of the possible causes of this autocorrelation?b. How does this autocorrelation affect the conclusions concerning the significance of the individual explanatory variables and the overall explanatory power of the regression model?c. Given that a person uses the model for forecasting future appliance sales, how does this autocorrelation affect the accuracy of these forecasts?d. What techniques might be used to remove this autocorrelation…1) Indicate whether the following statements are true or false. Explain why and show your work.a) In a simple regression Yi = B1 + B2 Xi + ui where var(ui) = o^2.X^2, weighting the model by X would solve the problem of non-common (heteroskedastic) variance of the error term.If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that y and x are positively correlated.
