Applied Multivariate Statistical Analysis by Richard A. JohnsonHow this book can write more than eight hundred pages? Its very verbose and fail to hit the point. From theoretical aspect, it leave so many things that need to explain in a clear and logical way,only tell you to consult books that list in the bibliography. From practical aspect, it doesnt contain any interesting example, all of which give me a full taste of what is trivial.
I dont read the whole book, because I find that if I dont programming on my computer, I will require few useful things. The authors treat many trivial things with details,which make me disgust. I think this book can reduce to at least four hundred pages.
How can my teacher recommend this nonsensical book? I DO NOT RECOMMEND IT AT ALL!
Introduction to Multivariate Data Analysis
What is Multivariate Statistical Analysis?
Multivariate statistical analysis refers to multiple advanced techniques for examining relationships among multiple variables at the same time. Researchers use multivariate procedures in studies that involve more than one dependent variable also known as the outcome or phenomenon of interest , more than one independent variable also known as a predictor or both. Upper-level undergraduate courses and graduate courses in statistics teach multivariate statistical analysis. This type of analysis is desirable because researchers often hypothesize that a given outcome of interest is effected or influenced by more than one thing. There are many statistical techniques for conducting multivariate analysis, and the most appropriate technique for a given study varies with the type of study and the key research questions. Four of the most common multivariate techniques are multiple regression analysis, factor analysis, path analysis and multiple analysis of variance, or MANOVA.
Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. The application of multivariate statistics is multivariate analysis. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the problem being studied. In addition, multivariate statistics is concerned with multivariate probability distributions , in terms of both. Certain types of problems involving multivariate data, for example simple linear regression and multiple regression , are not usually considered to be special cases of multivariate statistics because the analysis is dealt with by considering the univariate conditional distribution of a single outcome variable given the other variables.
Multivariate statistical methods are used to analyze the joint behavior of more than one random variable. There are a wide range of mulitvariate techniques available, as may be seen from the different statistical method examples below. These techniques can be done using Statgraphics Centurion 18's multivariate statistical analysis. More: Matrix Plot. It calculates summary statistics for each variable, as well as correlations and covariances between the variables. The graphs include a scatterplot matrix, star plots, and sunray plots.