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Please use R Studio in this question. Consider USairpollution data set in MVA package. Then, please answer the following question. a) Obtain mean vector and Var-Cov and Correlation matrix of the dataset. Please make a comment. b) Calculate 5 number summaries of the variables in the dataset with a one line code. Interpret them. c) Construct a scatterplot matrix of the data and comment on the result. d) Please generate a research question and answer it with one appropriate visual tool. e) Use the bivariate boxplot on the scatterplot of each pair of variables in the air pollution data to identify any outliers. Calculate the correlation between each pair of variables using all the data and the data with any identified outliers removed. Comment on the results. f) Please check multivariate normality of the data by using both visual and formal ways. Don't forget to state your hypothesis. g) Compare the chi-plots with the corresponding scatterplots for each pair of variables in the air pollution data. Do you think that there is any advantage in the former? h) Identify the outliers via adjusted mahalanobis distance and compare the result with part e.
Sri K.
When analyzing large data sets with many variables, researchers often encounter the problem of missing data (e.g., non-response). Typically, an imputation method will be used to substitute in reasonable values (e.g., the mean of the variable) for the missing data. An imputation method that uses "nearest neighbors" as substitutes for the missing data was evaluated in Data \& Knowledge Engineering (Mar. 2013). Two quantitative assessment measures of the imputation algorithm are normalized root mean square error (NRMSE) and classification bias. The researchers applied the imputation method to a sample of 3600 data sets with missing values and determined the NRMSE and classification bias for each data set. The correlation coefficient between the two variables was reported as $r=.2838$. a. Conduct a test to determine if the true population correlation coefficient relating NRMSE and bias is positive. Interpret this result practically. b. A scatterplot for the data (extracted from the journal article) is shown below. Based on the graph, would you recommend using NRMSE as a linear predictor of bias? Explain why your answer does not contradict the result in part a.
Stat: Need someone to explain this to me please?
Paul A.
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