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- Explain what autocorrelation indicates . What are the main problems that autocorrelation creates for OLS estimation results ? Give two ways to detect autocorrelation problem and the hypothesis that are tested ?A high bias model has a high unpredictable error, while a high variance model has a high systematic error. Select: True or False?Can We use ARDL model for the following information? If not, what isyour suggestion for the model? If yes, why?Table 1. Unit Root TestVariable Level First Difference Second DifferenceUN 0.0000*GR 0.0022INF 0.3720 0.0000INT 0.2682 0.0059WAGE 0.0226TAXIN 0.8565 0.0016EDU 0.1404 0.2333 0.0000FEMALE 0.0435URBAN 0.3330 0.3471 0.0188 *These numbers show the P-Value. Table2. Cointegration TestHypothesized Trace 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**None * 0.998292 580.3047 159.5297 0.0000At most 1 * 0.991600 408.2568 125.6154 0.0000At most 2 * 0.977966 279.2106 95.75366 0.0000At most 3 * 0.960113 176.2016 69.81889 0.0000At most 4 * 0.748764 89.21584 47.85613 0.0000At most 5 * 0.624056 51.91909 29.79707 0.0000At most 6 * 0.528664 25.50456 15.49471 0.0011At most 7 * 0.175048 5.195616 3.841466 0.0226
- Define Soft-Margin explaining how much Bias and Variance each tend to have. You could contrasted to Maximal-Margin-Classifier, and its trend on Bias and Variance.What is the sample linear correlation coefficient?Q.10 Explain advantages and disadvantages of robust regression with random sample consensus.
- As a benchmark, use a normalized histogram. Is the absence of a non-normalized histogram in a normalized histogram acceptable?Exhibit what we mean by coding norms.Name the concept: The variance of e i is the same for every observation. A. Heteroscedasticity B. Bias C. Homoscedasticity D. Consistency
- Explain how Logistic Regression works.The difficulty in performing variance analysis is the calculation of BCWP because one must predict the percent complete. Discuss the various methods to eliminate this problemExplain the benefits of using all-subsets regression versus stepwise regression in terms of data analysis.