For the sake of dealing with datasets, create a dataset using the following M-code: clear all; The..

For the sake of dealing with datasets, create a dataset using the following M-code: clear all;The generated dataset DS2 will have 100 entries and 7 columns; the first column is the color category, which has four colors; the second column is the [0, 1] interval; and the other five columns each represent a randomly generated point density function. Try the following MATLAB Statistics Toolbox functions: 1. mle using beta, gamma, normal, generalized extreme value, and Weibull distribution. 2. Kstest2 with _ = 0.05 to compare between columns of DS2 (columns 3–7). 3. Convert Your DS dataset into

»For the sake of dealing with datasets, create a dataset using the following M-code: clear all;The generated dataset DS2 will have 100 entries and 7 columns; the first column is the color category, which has four colors; the second column is the [0, 1] interval; and the other five columns each represent a randomly generated point density function. Try the following MATLAB Statistics Toolbox functions: 1. mle using beta, gamma, normal, generalized extreme value, and Weibull distribution. 2. Kstest2 with _ = 0.05 to compare between columns of DS2 (columns 3–7). 3. Convert Your DS dataset into double type and apply [p,t,stats] = anova1(DDS), where DDS is the double version of Your DS dataset. 4. Use mulcompare(stats); where stats structure is the output of MATLAB anova1 function, used in Step 3. multcompare generates an interactive plot where you can tell how many groups are compared with the group of interest. 5. Use [d,p,stats2]=manova1(DDS,DS2.Color,0.05), where DS2.Color is the categorical group. Do all groups have the same mean? 6. Use manovacluster(stats2) to generate the dendrogram, where stats2 structure is the output of MATLAB manova1 function, used in Step 5. 

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