MATLAB Statistics Assignment Help

Statistics and Machine Learning Toolbox™ contains functions that are used to describe, analyze, and model the data. The descriptive statistics and plots can be used for

• exploratory data analysis,
• fit probability distributions to data,
• generate random numbers for Monte Carlo simulations,
• and perform hypothesis tests.

Inferences are drawn from data tables and a predictive model is build accordingly with the help of Regression and classification algorithms.

We also have a function in the Statistics and Machine Learning Toolbox for multidimensional data analysis that provides feature selection, stepwise regression, principal component analysis (PCA), regularization, and other dimensionality reduction methods which helps in the recognition of variables and features that acts as the rudimentary aspect of  the predictive model.  

The toolbox provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Sometimes even the memory fall short of storing some data sets, the computations on these data sets can only be achieved by the algorithms of the statistics and machine learning.