PLEASE WRITE THE ANSWERS BY COMPUTER

WRITE THE RIGHT DATA

Setup: We have data on England and Wales mortality rates from lung cancer from 1936 - 1980. The years correspond to various calendar periods: 1936: 1936-1940 1941: 19411945 1946: 1946-1950 The lung information is the lung cancer mortality rate per 1 million person-years. The data are shown below. etc. Lung Year 1936 1941 1946 1951 1956 1961 1966 1971 1976 575 720 1450 2500 3900 5200 6400 7700 8900 Residual 881.556 -87.444 -471.444-535.444-249.444 -63.444 22.556 208.556 294.556 Questions: 1. Calculate the lag 1 autocorrelation of the residuals. Interpret your findings. 2. Calculate the Durbin-Watson statistic and the p-value for a 2-sided test. Interpret your findings. Use a = 0.05. (Note: SAS gives 1-sided p-values. To get 2-sided p-values, multiply the smaller 1-sided p-value by 2.) 3. Run a regression analysis and determine if the equal variance condition is met or not. Provide appropriate plots and/or test results. 4. Run a regression analysis and determine if the residuals are normal or not. Provide appropriate plots and/or test results. 5. Suppose the normality condition is not met (even if it is). Should you transform the 2- or the y-variable? Setup: We have data on England and Wales mortality rates from lung cancer from 1936 - 1980. The years correspond to various calendar periods: 1936: 1936-1940 1941: 19411945 1946: 1946-1950 The lung information is the lung cancer mortality rate per 1 million person-years. The data are shown below. etc. Lung Year 1936 1941 1946 1951 1956 1961 1966 1971 1976 575 720 1450 2500 3900 5200 6400 7700 8900 Residual 881.556 -87.444 -471.444-535.444-249.444 -63.444 22.556 208.556 294.556 Questions: 1. Calculate the lag 1 autocorrelation of the residuals. Interpret your findings. 2. Calculate the Durbin-Watson statistic and the p-value for a 2-sided test. Interpret your findings. Use a = 0.05. (Note: SAS gives 1-sided p-values. To get 2-sided p-values, multiply the smaller 1-sided p-value by 2.) 3. Run a regression analysis and determine if the equal variance condition is met or not. Provide appropriate plots and/or test results. 4. Run a regression analysis and determine if the residuals are normal or not. Provide appropriate plots and/or test results. 5. Suppose the normality condition is not met (even if it is). Should you transform the 2- or the y-variable