STATISTICAL ANALYSIS

Statistical Analysis

Statistical Analysis

Part A

Non-Pair Wise Main Effect

ANOVA

Y total # gestures

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

539.000

5

107.800

7.985

.000

Within Groups

243.000

18

13.500

Total

782.000

23

From the above table, it can be observed that the significance value of the analysis of variance table is less than 0.05 which reflects that the model for the variables is significant. The analysis of variance is a statistical test to verify that several samples are from the same population. This test applies when measuring one or more categorical variables (called factors of variability then, their different modalities are sometimes called "levels") that have influence on the distribution of a continuous variable to explain.

Contrast Coefficients

 

Independent_Var_AB

Contrast

1

2

3

1

0

1

-1

2

-1

0.5

0.5

Contrast Tests

Y total # gestures

Value of contrast

Std. Error

t

df

Sig.(2-tailed

Y total # gestures Assume equal variances 1

2

5.000

4.500

1.771

1.530

2.850

2.935

24

24

.000

.015

Does not assume equal variances 1

5.000

1.788

2.795

12.07

.031

2

4.500

1.516

2.967

12.92

.018

In addition to this, it can be observed that the significance value of the gestures and the independent variables that are activity and setting shows that the model for the non-pair wise main effect is significant.

Part B

Orthogonal Interaction

ANOVA

Y total # gestures

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

547.000

10

103.431

8.647

.038

Within Groups

235.000

13

19.684

Total

782.000

23

Moreover, it can be seen that the significance value of the analysis of variance table for the orthogonal interaction is less than 0.05 which reflects that the model for the variables is significant. Analysis of variance allows us to study the behavior of a variable to be explained in terms of continuous or categorical explanatory variables. When one wishes to study the behavior of several variables to explain the same time, we use a multiple analysis of variance. If a model contains categorical and continuous variables, and we want to study the laws binding the continuous explanatory variables with the dependent variable according to each category of categorical variables, we then use an analysis of covariance.

It is a set of statistical models (statistical model) with procedural accompaniment to these models was able to compare the ...