Data analysis and visualization are crucial skills for engineers. They help make sense of complex information and communicate findings effectively. MATLAB offers powerful tools for these tasks, from importing and cleaning data to creating stunning visualizations.

This section covers essential techniques like statistical analysis, hypothesis testing, and advanced plotting. You'll learn to manipulate data, perform regressions, and design interactive user interfaces. These skills are vital for interpreting experimental results and presenting engineering solutions.

Data Manipulation in MATLAB

Importing and Organizing Data

• MATLAB's data import functions (importdata, xlsread, csvread) load various file formats into the workspace efficiently
• table data type organizes heterogeneous data with named variables and observations facilitating structured analysis
• Data manipulation techniques involve indexing, slicing, and reshaping arrays to prepare datasets
  • Use colon operator for slicing (array(1:5, :) selects first 5 rows of all columns)
  • Reshape function transforms array dimensions (reshape(A, 3, []) creates 3-row matrix)

Data Cleaning and Preprocessing

• Handle missing values using fillmissing function with methods like 'constant', 'previous', or 'linear'
• Remove outliers with isoutlier function based on various methods (median absolute deviation, mean ± 3 standard deviations)
• Normalize data using normalize function to scale variables to comparable ranges
  • Z-score normalization: normalize(data, 'zscore')
  • Min-max scaling: normalize(data, 'range')

Basic Statistical Analysis

• Utilize built-in functions for descriptive statistics
  • mean calculates average (mean([1 2 3 4 5]) returns 3)
  • median finds middle value (median([1 2 3 4 5]) returns 3)
  • std computes standard deviation
  • var determines variance
• Advanced analysis employs Statistics and Machine Learning Toolbox for complex methods (clustering, classification, regression)

Statistical Analysis of Engineering Data

Hypothesis Testing and Regression

• Conduct hypothesis tests using functions like ttest, anova1, and chi2gof to assess experimental result significance
  • Two-sample t-test: [h, p] = ttest2(group1, group2) compares means of two groups
• Perform regression analysis to model relationships between variables
  • Linear regression: mdl = fitlm(X, y) fits linear model to data
  • Non-linear regression: mdl = fitnlm(X, y, model) fits specified non-linear model

Time Series and Dimensionality Reduction

• Analyze temporal patterns using time series functions
  • autocorr evaluates self-correlation (autocorr(data) plots autocorrelation function)
  • crosscorr assesses correlation between two time series
• Apply Principal Component Analysis (PCA) to reduce dimensionality and identify key features
  • [coeff, score, latent] = pca(data) performs PCA on input data

Advanced Statistical Techniques

• Implement Bayesian inference techniques for probabilistic interpretations of data
  • Use bayesopt function for Bayesian optimization of model parameters
• Conduct reliability analysis to assess system performance and failure rates
  • Weibull analysis: wblfit function fits Weibull distribution to failure data
  • Monte Carlo simulations: Generate random samples to estimate system reliability

Advanced Visualization Techniques

3D Plotting and Animation

• Create three-dimensional plots using functions like plot3, surf, and contour3
  • plot3(x, y, z) generates 3D line plot
  • surf(X, Y, Z) creates 3D surface plot
• Develop animations to represent time-varying phenomena
  • Use getframe to capture plot frames (F = getframe(gcf))
  • Combine frames with movie function to create animation (movie(F))

Plot Customization and Enhancement

• Customize plots with advanced properties
  • Adjust lighting: lighting gouraud for smooth shading
  • Set transparency: alpha(0.5) makes surface 50% transparent
  • Modify camera viewpoint: view(45, 30) changes azimuth and elevation
• Integrate multiple plot types for comprehensive representation
  • Create subplots: subplot(2,2,1) divides figure into 2x2 grid, selects first position
  • Overlay plots: hold on allows multiple plots on same axes

Color Mapping and Output

• Apply color mapping techniques to communicate quantitative information
  • Create custom colormaps: colormap(customMap)
  • Adjust color scaling: caxis([min max]) sets color limits
• Export high-quality visualizations for reports and presentations
  • Save as vector graphics: print('-vector', 'filename.pdf')
  • Specify resolution: print('-r300', 'filename.png') for 300 dpi output

Interactive User Interface Design

GUI Development Approaches

• Utilize App Designer for drag-and-drop GUI creation with minimal coding
  • Add components like buttons, sliders, and axes from component library
  • Set component properties and callbacks in property inspector
• Develop GUIs programmatically using functions like figure, uicontrol, and uimenu
  • Create main window: fig = figure('Name', 'My GUI')
  • Add button: uicontrol('Style', 'pushbutton', 'String', 'Click me')

Event-Driven Programming and Interactivity

• Implement event-driven programming techniques for responsive interfaces
  • Define callbacks: set(button, 'Callback', @buttonCallback)
  • Use listeners for custom events: addlistener(obj, 'EventName', @eventCallback)
• Integrate MATLAB's plotting capabilities within GUIs for real-time visualization
  • Update plots based on user input: plot(axes, newData) within callback function

Layout Management and Deployment

• Manage GUI layout using tools like uigridlayout and uiflowcontainer
  • Create grid layout: g = uigridlayout(fig, [3 2]) for 3x2 grid
  • Add components to specific grid positions: uibutton(g, 'Position', [1 1])
• Deploy MATLAB GUIs as standalone applications using MATLAB Compiler
  • Package app with required files: mcc -m myApp.m
  • Distribute executable to users without MATLAB installations