nastwitter.blogg.se - Principal component analysis xlstat

13.4 - Obtain Estimates of Canonical Correlation.Test for Relationship Between Canonical Variate Pairs 13.1 - Setting the Stage for Canonical Correlation Analysis.Lesson 13: Canonical Correlation Analysis.

12.7 - Maximum Likelihood Estimation Method.

12.6 - Final Notes about the Principal Component Method.

12.4 - Example: Places Rated Data - Principal Component Method.

11.7 - Once the Components Are Calculated.

11.6 - Example: Places Rated after Standardization.

11.5 - Alternative: Standardize the Variables.

11.4 - Interpretation of the Principal Components.

11.2 - How do we find the coefficients?.

11.1 - Principal Component Analysis (PCA) Procedure.

Lesson 11: Principal Components Analysis (PCA).

10.5 - Estimating Misclassification Probabilities.

10.1 - Bayes Rule and Classification Problem.

9.6 - Step 3: Test for the main effects of treatments.9.5 - Step 2: Test for treatment by time interactions.9.3 - Some Criticisms about the Split-ANOVA Approach.8.10 - Two-way MANOVA Additive Model and Assumptions.8.9 - Randomized Block Design: Two-way MANOVA.8.7 - Constructing Orthogonal Contrasts.8.4 - Example: Pottery Data - Checking Model Assumptions.8.2 - The Multivariate Approach: One-way Multivariate Analysis of Variance (One-way MANOVA).8.1 - The Univariate Approach: Analysis of Variance (ANOVA).Lesson 8: Multivariate Analysis of Variance (MANOVA).7.2.8 - Simultaneous (1 - α) x 100% Confidence Intervals.7.2.7 - Testing for Equality of Mean Vectors when \(Σ_1 ≠ Σ_2\).7.2.6 - Model Assumptions and Diagnostics Assumptions.7.2.4 - Bonferroni Corrected (1 - α) x 100% Confidence Intervals.7.2.2 - Upon Which Variable do the Swiss Bank Notes Differ? - Two Sample Mean Problem.7.2.1 - Profile Analysis for One Sample Hotelling's T-Square.7.1.15 - The Two-Sample Hotelling's T-Square Test Statistic.7.1.12 - Two-Sample Hotelling's T-Square.7.1.11 - Question 2: Matching Perceptions.7.1.8 - Multivariate Paired Hotelling's T-Square.7.1.7 - Question 1: The Univariate Case.7.1.4 - Example: Women’s Survey Data and Associated Confidence Intervals.7.1.1 - An Application of One-Sample Hotelling’s T-Square.Lesson 7: Inferences Regarding Multivariate Population Mean.6.2 - Example: Wechsler Adult Intelligence Scale.Lesson 6: Multivariate Conditional Distribution and Partial Correlation.5.2 - Interval Estimate of Population Mean.5.1 - Distribution of Sample Mean Vector.Lesson 5: Sample Mean Vector and Sample Correlation and Related Inference Problems.4.7 - Example: Wechsler Adult Intelligence Scale.4.6 - Geometry of the Multivariate Normal Distribution.4.4 - Multivariate Normality and Outliers.4.3 - Exponent of Multivariate Normal Distribution.Lesson 4: Multivariate Normal Distribution.Lesson 3: Graphical Display of Multivariate Data.

Lesson 2: Linear Combinations of Random Variables.

1.5 - Additional Measures of Dispersion.

Lesson 1: Measures of Central Tendency, Dispersion and Association.

(There is another very useful data reduction technique called Factor Analysis discussed in a subsequent lesson.) Each linear combination will correspond to a principal component. To interpret the data in a more meaningful form, it is necessary to reduce the number of variables to a few, interpretable linear combinations of the data. With 12 variables, for example, there will be more than 200 three-dimensional scatterplots. Graphical displays may also not be particularly helpful when the data set is very large. There would be too many pairwise correlations between the variables to consider. With a large number of variables, the dispersion matrix may be too large to study and interpret properly. Where some communities might rate better in the arts, other communities might rate better in other areas such as having a lower crime rate and good educational opportunities. For housing and crime, the lower the score the better. Note! Within the dataset, except for housing and crime, the higher the score the better.