Understanding Data and Principal Component Algorithm (PCA) - 40 points (Adapted from: Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, John Wiley &Sons 3RD EDITION (2016), Galit Shmueli, Peter C. Bruce, Nitin R. Patel) Dataset: Cereals.xls Problem description: Breakfast Cereals . Use the data for the breakfast cereals to explore and summarize the data as follows: Note: Few records contain missing values since there are just a few, a simple solution is to remove them first. However, to understand how a software can do this task, use the "Missing Data Handling" utility in XLMiner) http://www.solver.com/xlminer/help/missing-data-handling-examples Variables carbo, sugar and potassium have some missing values. Later, to draw histograms for these variables, delete the missing values from each of the columns separately using XLMiner's missing data handling utility. Another approach (and the one more commonly used when analyzing the data set as a whole) is to delete or impute missing values in all variables for all records at the same time To avoid analysis errors, fix the missing values, clean the dataset and use the cleaned data for the following tasks. Answer the following questions in your analysis. 1. Which variables are quantitative/numerical? Which are ordinal? Which are nominal? (create a Table for easy display of various types) (5 points) 2. Create a table with the average, median, min, max