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Find an example of a study that shows a statistical significance but not a clinical significance.
Explain each sampling technique discussed in the "Visual Learner: Statistics" in your own words,
Fit a regression model with happy as the response and the other four variables as predictors. Give an interpretation for the meaning of the love coefficient.
Using the sat dataset, fit a model with the total SAT score as the response and expend, salary, ratio and takers as predictors.
For the prostate data, fit a model with lpsa as the response and the other variables as predictors. Compute and comment on the correlations.
Consider all possible models that also include all, some or none of the other two predictors. Report the coefficient for temperature, its standard error.
Thirty samples of cheddar cheese were analyzed for their content of acetic acid, hydrogen sulfide and lactic acid
Fit a model with just income as a predictor and use an F-test to compare it to the full model.
Test the hypothesis that ßsalary = ßratio = ßexpend = 0. Do any of these predictors have an effect on the response?
The snail dataset contains percentage water content of the tissues of snails grown under three different levels of relative humidity and two different.
Fit an autoregressive model of the same form used for the airline data. Are all the predictors statistically significant?
Compute a 95% prediction interval for median predictor values and compare to the results to the interval for the full model.
The dataset teengambs concerns a study of teenage gambling in Britain. Fit a regression model with the expenditure on gambling.
In data mining the candidate model should be applied to a data set that was not used in the estimation process in order to find out the accuracy.
Why do you think communication between the person preparing a forecast and the forecast user is important?
You need to have the forecast done and the presentation ready in just a few days. What method(s) would you consider using and why?
Comment on the pattern of misclassifications. How much better did this data mining technique do as compared to a naive model?
Evaluate the following statement: “If an ARIMA model is properly constructed, it has residual autocorrelations that are all equal to zero.”
Assume you would like to use a Winters’ model and combine the forecast results with a multiple-regression model.
Combine the two methods (i.e., the Winters’ and the multiple-regression models) with the alculated weighting scheme, and construct a combined forecast model.
Could there be cases in which a combined model would show no gain in forecast accuracy over the original models?
Outline the different methods for combining forecast models. Can more than two forecasting models be combined into a single model?
What percentage error would result if you forecast sales for the year by simply multiplying the 13,000 units for the first quarter by 4?
Explain what dummy variables are and how they can be used to account for seasonality. Give an example of how you might use dummy variables to measure.
Review the three quick checks that should be used in evaluating a multiple-regression model.