Statistical Data Analysis using MATLAB: A Beginners Guide

Explore data science applications in science and engineering and why Matlab is chosen, then build Matlab fundamentals, environment, help commands, and basic commands for statistical data analysis.

Explore Matlab as a matrix laboratory that enables numerical computation, data visualization, and programming in an interactive environment, with toolboxes, GUI, and easy hardware interfacing.

Explore the MATLAB development environment and its five main parts, and navigate the four core windows—command window, command history, current directory, and workspace—while learning start and quit procedures.

Master MATLAB help commands, demonstrations, and documentation to access function details, find functions with look for, and explore 2D/3D plotting examples and tutorials.

Learn general MATLAB commands for managing the workspace and directories, including who, whos, clear all, clc, clf, pwd, cd, dir, and mkdir.

Explore the fundamentals of statistical data analysis using MATLAB, building on MATLAB basics, the development environment, and the data science workflow, including why it matters, time series, and trend analysis.

Explore how statistical data analysis using Matlab supports engineering design, from data collection and presentation to descriptive and inferential statistics, hypothesis testing, and making data-driven decisions.

Define the data science workflow from observation to decision and design, detailing data acquisition, organization, exploratory analysis, and four analytics types—descriptive, diagnostic, predictive, and prescriptive—within MATLAB.

Investigate time series and trend analysis within Matlab-based statistical data analysis for beginners. Explore applications in engineering and business, with data observed at specified or equal time intervals.

Learn the basic data analysis flow for time series in MATLAB, from data acquisition and preprocessing to visualization, filtering and smoothing, and both parametric and nonparametric modeling.

Explore MATLAB-based data preprocessing for statistical analysis, covering loading data, handling missing values, identifying outliers, and smoothing filters, as a foundation for summarizing, visualizing, and modeling time-series data.

Load data into MATLAB from various file formats using dm read, specify file name, delimiter, and data range, skip headers, and note zero-padding for uneven matrices.

Learn to load data from a Word document into MATLAB and write mph to km/h and pounds to newtons tables to Word files using f open, fprintf, and f close.

Learn how to transfer data between MATLAB and Excel using readtable and writetable, and control writing with xlswrite for specific sheets and ranges.

Learn to use MATLAB's data import tool to import text, Excel, Word files, configure delimiters, handle missing values, and generate a script or function to import data as a table.

Learn to import large data collections in Matlab using data store. Read from multiple files or sheets in chunks without loading entire files into memory.

Identify messy and missing data in MATLAB tables by locating nan and nat. Replace or remove missing data using nearest values, standardized missing, and read table.

Identify outliers as data deviations using matlab's isoutlier, applicable to vectors, matrices, or tables. Fill outliers with filloutliers and set thresholds by median absolute deviation or three times the mean.

Learn to smooth and filter noisy time series to reveal underlying patterns before modeling. Use convolution with a window and built-in filters, like Butterworth, to reduce randomness.

Learn to summarize preprocessed data in MATLAB by analyzing measure of location (mean, median, mode), measure of scale, and distribution shape, using histograms and curve fitting.

Explore measure of location as central tendency in data using MATLAB, including mean, median, mode, and range. Understand how built-in functions compute these statistics for vectors, matrices, and n-dimensional arrays.

Explore the measure of scale in data analysis with Matlab, focusing on data spread and variability. Learn to compute max, mean, variance, and standard deviation using Matlab functions.

Explore categorical data in MATLAB, including ordinal, nominal, and binary variables, and learn to create, convert, summarize, and manipulate categorical arrays using built-in functions.

Master MATLAB categorical data by converting arrays and identifying categories like blue, green, red. Use gallery generation, concatenation, unions, reordering, and multiplication to analyze color and beverage data.

Explore data visualization in MATLAB to understand patterns, trends, and outliers using two- and three-dimensional plots such as line graphs, bar graphs, histograms, scatter plots, and box plots.

Learn to create two-dimensional plots in Matlab, using plot with vectors or matrices, add x and y labels, titles, legends, and plot multiple data sets in one figure.

Learn to create two dimensional plots in MATLAB, including bar graphs with x/y data, stacked and colored bars, semi-log x and semi-log y plots, and stem charts for sampled data.

Explore how histograms reveal the frequency distribution of continuous data, and learn to plot and customize them in MATLAB using the histogram function, including bin width, normalization, and display options.

Explore MATLAB's statistical plotting techniques for univariate, bivariate, and multivariate data, including box plots, scatter plots, and Andrews plots, with practical examples and outlier handling.

Learn to visualize tall arrays in MATLAB by creating a data store, defining tall arrays, and using pie charts, histograms, scatter plots, and bin scatter for large datasets.

Load wind data.mat, compute mean, moving mean, min, max, and deviations for wind speed and humidity, then visualize results with single and two-axis plots in MATLAB.

Explore statistical data analysis in MATLAB using the wind data.mat, load and convert variables, compute mean, min, max, and std, and illustrate the results with visualization, including dual y-axes.

Explore wind flow in three-dimensional visualization using Matlab: load wind.mat, compute velocity, and visualize via velocity slices, contour slices, streamlines, and three-dimensional stream tube views in a rectangular channel.

Demonstrate wind flow three-dimensional visualization in MATLAB using wind.mat, calculate velocity, and render horizontal slices, contour slices, streamlines, 3D slices, and stream tubes in a rectangular channel.

Understand statistical data modeling as applying statistical analysis to data sets, observe and visualize data, and fit mathematical relationships to predict future outcomes.

Explore parametric modeling with linear regression in MATLAB, fitting data via y = w0 + w1 x and evaluating coefficients, p-values, t-stats, and ANOVA.

Learn polynomial regression in Matlab by transforming data into polynomial features and fitting a linear model. Use polyfit and polyval to estimate coefficients and predict values.

Apply statistical data analysis in MATLAB to real-time Covid-19 data across US states, covering data loading, preprocessing, visualization, regression modeling, moving mean, and animated progression.

Learn data preparation and visualization in MATLAB by loading US states data, organizing dates and states, and plotting horizontal bars of COVID-19 cases and deaths with moving means.

Develop MATLAB data preparation, visualization, and plotting skills for Covid-19 trends, using unique dates, horizontal and vertical bar plots, and seven-day moving means.

Preprocess US Covid 19 data in MATLAB by generating missing values and replacing them with previous, linear, and spline regression, then visualize confirmed and death cases with subplots.

Master data pre-processing and visualization in MATLAB by handling missing data with previous, linear, and spline methods, comparing their effects on California covid-19 cases and deaths.

Apply MATLAB statistical data modeling to California covid data, using polyfit for first to fourth order and quadratic and spline regression, then compare curve fitting options for best fit.

Explore Matlab data modeling by fitting polynomial orders one to four to California covid-19 case data, then compare quadratic, exponential, and smoothing spline fits, with smoothing spline best matching.

Learn to create a matlab data animation of US covid-19 data by date, using us-states and us-population csv files, with two animations: total confirmed cases and normalized by population.

Create a MATLAB data animation that visualizes COVID-19 state-wise confirmed cases and population-normalized percentages across 155 dates using horizontal bar charts and GIF outputs.