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    Multivariate Statistics

    Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. The application of multivariate statistics is multivariate analysis. Multivariate statistics is concerned with understanding the different aims and background of each of the different forms of multivariate analysis and how they relate to each other. It is also concerned with multivariate probability distribution in terms of how these can be used to represent the distribution of observed data and how they can be used as part of statistical inference, where several different quantities are of interest to the same analysis.

    There is a set of probability distribution used in multivariate analyses that plays a similar role to the corresponding set of distribution that are used in univariate analysis when the normal distribution is appropriate to a dataset. These are the multivariate normal distribution, the Wishart distribution and the Multivariate Student-t distribution. 

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    BrainMass Categories within Multivariate Statistics

    Factor Analysis

    Solutions: 13

    Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.

    Cluster Analysis

    Solutions: 6

    Cluster analysis is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

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    Chocolate and BMI

    I need some help with this case study review and identify everything that is required. Thanks The readings for this week focus on various types of correlations and regressions. In this discussion we will apply those concepts to the analysis of a case study. Read the "Chocolate and Body Weight" case study presented in Chapter 20

    Performing Simple Regression and Interpretating Results

    See attached file. Expert request The following problems are to be solved by Excel and/or MegaStat. You need to find the values on the excel regression solutions and interpret them. Part 1 is a simple linear regression as done in the chapter 13 assignment. You should only include Y (fuel consumption) and X1 (temperature) f

    Multivariate Statistics

    Question 1 Regression model results can be erroneous if multicollinearity is an issue. What causes multicollinearity? a) A test of homogeneity of variance-covariance matrices is significant. b) The DV is highly intercorrelated with one or more of the IV's. c) One or more of the IV's are highly intercorrelated. d)