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本文档包括了对软件中用到的多元统计分析中判别分析与聚类分析的主要方法(包括距离判别分析,费希尔判别分析,贝叶斯判别分析,逐步判别分析及聚类分析)原理及在本软件中使用的基本方法与设计流程图进行了详尽的阐释,在通过本文档的阅读对软件有一个总体的了解后再确定您要使用的分析方法-This document includes the software used in multivariate statistical analysis of discriminant analysis and cluster
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多元统计分析引论，一本很好的高等数理统计的书籍。-Introduction multivariate statistical analysis, a higher number of good books on mathematical statistics.
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Applied multivariate statistical analysis
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A Guide to Multivariate Techniques
Preparation for Statistical Analysis
Review: ANOVA
Review: ANCOVA
MANOVA
MANCOVA
Repeated Measure Analysis
Factor Analysis
Discriminant Analysis
Cluster Analysis
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Principal Component Analysis (PCA) is the general name for a technique which uses sophisticated
underlying mathematical principles to transforms a number of possibly correlated
variables into a smaller number of variables called principal compone
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HBAT. pls use this in your assignment in multivariate analysis. credits to the owner thank you
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多元统计分析方法在上市公司绩效评价体系建立中的应用_谢明旸 多元统计分析方法在上市公司绩效评价体系建立中的应用_谢明旸 -Multivariate statistical analysis method in the Performance of Listed Companies Evaluation System of _ Xie Ming Yang multivariate statistical analysis method in the Performance of Listed Co
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Independent component analysis (ICA) is a method for finding underlying factors or
components multivariate (multidimensional) statistical data. What distinguishes
ICA other methods is that it looks for components that are both statistically
ind
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利用多因素小波变换和多变量时间序列模型的日前电价预测-Prediction of a day ahead electricity price using multivariate wavelet transform and multivariate time series model
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基于欧几里得距离的聚类分析，基于人工神经网络的常用数字信号调制，利用最小二乘法进行拟合多元非线性方程。- Clustering analysis based on Euclidean distance, The commonly used digital signal modulation based on artificial neural network, Multivariate least squares fitting method of nonlinear equations.
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现代信号处理中谱估计在matlab中的使用，最大信噪比的独立分量分析算法，多元数据分析的主分量分析投影。- Modern signal processing used in the spectral estimation in matlab, SNR largest independent component analysis algorithm, Principal component analysis of multivariate data analysis projection.
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多元数据分析的主分量分析投影，通过虚拟阵元进行DOA估计，光纤无线通信系统中传输性能的研究。- Principal component analysis of multivariate data analysis projection, Conducted through virtual array DOA estimation, Fiber Transmission wireless communication system performance.
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有小波分析的盲信号处理，多元数据分析的主分量分析投影，做视觉测量的上位机代码。- There Wavelet Analysis Blind Signal Processing, Principal component analysis of multivariate data analysis projection, Do Vision Measurement PC code.
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利用最小二乘法进行拟合多元非线性方程，处理信号的时频分析，DC-DC部分采用定功率单环控制。- Multivariate least squares fitting method of nonlinear equations, When processing a signal frequency analysis, DC-DC power single-part set-loop control.
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利用最小二乘法进行拟合多元非线性方程，三相光伏逆变并网的仿真，最小二乘回归分析算法。- Multivariate least squares fitting method of nonlinear equations, Three-phase photovoltaic inverter and network simulation, Least-squares regression analysis algorithm.
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比较了软阈值，硬阈值及当今各种阈值计算方法,多元数据分析的主分量分析投影，实现了图像的灰度化并进一步用于视频监视控。- Comparison of soft threshold and hard threshold and today various threshold calculation method, Principal component analysis of multivariate data analysis projection, Achieve a grayscale ima
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已经调试成功.内含m文件，可直接运行，实现了对10个数字音的识别程序多元数据分析的主分量分析投影。- Has been successful debugging. M contains files can be directly run, Realization of 10 digital audio recognition program Principal component analysis of multivariate data analysis projection.
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多元数据分析的主分量分析投影，有PMUSIC 校正前和校正后的比较，这个有中文注释，看得明白。- Principal component analysis of multivariate data analysis projection, A relatively before correction and after correction PMUSIC, The Chinese have a comment, understand it.
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这是一个好用的频偏估计算法的matlab仿真程序,多元数据分析的主分量分析投影，重要参数的提取。- This is a useful frequency estimation algorithm matlab simulation program, Principal component analysis of multivariate data analysis projection, Extract important parameters.
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自写曲率计算函数 ,多元数据分析的主分量分析投影，实现六自由度运动学逆解算法。- Since writing the curvature calculation function, Principal component analysis of multivariate data analysis projection, Six degrees of freedom to achieve inverse kinematics algorithm.
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