20. Measuring and Modeling Relationships between Variables Flashcards Preview

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1

Sample Correlation Coefficient (r) P.393

A statistics that measures the degree of linear relationship between two sets of numbers.

r= Sxy / √Sxx Syy

2

Coefficient of Determinations r^2 P.395

Amount of the variation in Y that is explained by the fitted simple linear equation.

3

Adjusted Coefficient of Determination r^2adj

Used with multiple regression. Adjusted coefficient will increase when variables are added to the model that act to decrease the mean square error.

4

Regression analysis P.397

A technique that typically uses continuous predictor variables to predict the variation in a continuous response variable.

5

Linear regression coefficients P.398

Numbers associated with each predictor variable in a linear regression equation that tells how the response variable changes with each unit increase in the predictor variable.

6

Simple Linear Regression P.398

Y=b0+b1X+E

7

Multicollinearity P.406

Two or more predictor variables in a multiple regression model are correlated.

8

Multivariate Tools P406

-Principal components
-Factor analysis
-Discriminant analysis
-Multiple ANOVA

9

Principal Components

Used to form a smaller number of uncorrelated variables from a large set of data. The goal is to explain the maximum amount of variance with the fewest number of principal components.

10

Factor analysis

Used to determine the underlying factors responsible for correlations in the data.

11

Discriminant analysis

Used to classify observation into two or more groups if you have a sample with known groups.

12

MANOVA

Used to analyze both balanced and unbalanced experimental designs.

13

Multi-vari P.417

Graphical technique for viewing multiple sources of process variation.

14

Positional variation P.417

Within-part variation

15

Cyclical variation P.417

Part-to-part variation

16

Temporal variabtion P.417

Variation occurs as change over time.