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ANOVA-nSTAT

An Online Computational Tool For ANOVA in The Paradigm of New Statistics

By W. J. Zhang and Y. H. Qi



The user manual guide and suggested citation of this page:
Zhang W.J., Qi Y.H. 2024. ANOVA-nSTAT: ANOVA methodology and computational tool in the paradigm of new statistics. Computational Ecology and Software, 14(1): 48-67
Also, click here to download the corresponding offline tool (the standalone executable software).


Randomized Complete Design

Number of Treatments (n):

Number of Replications (m):

Acceptable Least Effect Size: Small effect Intermediate effect Large effect

Acceptable Least p-value in Significance Testing: 0.01 0.001 0.0001

ANOVA Data (Reset and enter or copy your ANOVA data (space delimited) into this area):




Results:


ANOVA Demo Data for Randomized Complete Design (n = 6, m = 5) (No. treatments (total rows) = 6, No. replications (total columns) = 5):

2 3 2 3 2
8 6 4 5 7
7 8 9 7 8
8 7 5 8 9
2 4 2 1 2
9 6 9 8 9

Randomized Block Design

Number of Treatments (n):

Number of Blocks (m):

Acceptable Least Effect Size: Small effect Intermediate effect Large effect

Acceptable Least p-value in Significance Testing: 0.01 0.001 0.0001

ANOVA Data (Reset and enter or copy your ANOVA data (space delimited) into this area):




Results:


ANOVA Demo Data for Randomized Block Design (n = 6, m = 5) (No. treatments (total rows) = 6, No. replications (total columns) = 5):

2 1 2 3 3
5 4 4 3 4
10 11 9 10 9
9 8 8 7 7
6 7 6 9 8
1 4 1 2 2

Randomized Block Design with Subsamples

Number of Treatments (n):

Number of Blocks (m):

Number of Subsample (h):

Acceptable Least Effect Size: Small effect Intermediate effect Large effect

Acceptable Least p-value in Significance Testing: 0.01 0.001 0.0001

ANOVA Data (Reset and enter or copy your ANOVA data (space delimited) into this area):




Results:


ANOVA Demo Data for Randomized Block Design with Subsamples (n = 3, m = 5, h = 6) (No. treatments (total matrices) = 3, No. blocks (total columns) = 5, No. sub-samples (total rows in a matrix) = 6):

2 1 2 3 3
9 8 8 7 7
6 7 7 5 6
4 5 4 4 3
1 0 1 2 1
3 5 4 4 3
2 1 2 3 3
4 5 4 4 3
6 7 7 8 8
7 9 8 7 7
1 0 1 2 1
6 7 6 5 6
2 1 2 3 3
4 5 4 4 3
9 8 8 7 7
6 7 7 8 8
1 0 1 2 1
2 2 3 2 2

Nested Design

Number of Treatments (n):

Number of Blocks (h):

Number of Subsample (m):

Acceptable Least Effect Size: Small effect Intermediate effect Large effect

Acceptable Least p-value in Significance Testing: 0.01 0.001 0.0001

ANOVA Data (Reset and enter or copy your ANOVA data (space delimited) into this area):




Results:


ANOVA Demo Data for Nested Design (n = 3, h = 6, m = 5) (No. treatments (total matrices) = 3, No. blocks (total rows in a matrix) = 6, No. sub-samples (total columns) = 5):

3 1 3 2 3
7 7 8 7 8
7 6 7 5 6
3 5 5 4 3
2 1 1 2 1
4 5 3 4 3
2 1 2 3 3
3 5 4 4 3
8 7 7 6 8
8 9 8 7 7
2 1 1 0 1
5 6 7 5 6
2 1 2 3 3
4 5 4 4 3
7 7 8 9 8
6 7 7 8 8
2 1 1 2 1
3 2 3 1 2

Two Factor Classification

Number of Levels of Factor A (n):

Number of Levels of Factor B (m):

Number of Replications (r):

Acceptable Least Effect Size: Small effect Intermediate effect Large effect

Acceptable Least p-value in Significance Testing: 0.01 0.001 0.0001

ANOVA Data (Reset and enter or copy your ANOVA data (space delimited) into this area):




Results:


ANOVA Demo Data for Two-Factor Classification (n = 3, m = 5, r = 6) (No. levels of factor A (total matrices) = 3, No. levels of factor B (total columns) = 5, No. replications (total rows in a matrix) = 6):

2 4 2 5 3
9 8 8 7 7
6 7 7 10 6
4 5 8 4 3
6 4 5 2 3
5 5 7 8 3
5 4 6 3 3
4 8 4 4 3
11 7 7 8 8
7 9 8 7 7
4 6 7 2 9
6 9 6 5 6
10 6 11 8 12
14 15 13 12 11
13 10 8 11 9
6 10 7 8 8
10 11 16 8 13
9 6 13 10 8

Two-Factor Randomized Complete Block Design

Number of Levels of Factor A (n):

Number of Levels of Factor B (m):

Number of Replications (r):

Acceptable Least Effect Size: Small effect Intermediate effect Large effect

Acceptable Least p-value in Significance Testing: 0.01 0.001 0.0001

ANOVA Data (Reset and enter or copy your ANOVA data (space delimited) into this area):




Results:


ANOVA Demo Data for Two-Factor Randomized Complete Block Design (n = 3, m = 6, r = 5) (No. levels of factor A (total matrices) = 3, No. levels of factor B (total rows in a matrix) = 6, No. replications (total columns) = 5):

2 4 2 5 3
9 8 8 7 7
6 7 7 8 6
4 5 8 4 3
6 4 5 2 3
5 5 7 10 3
11 7 12 8 10
12 15 11 12 14
13 10 8 11 9
6 10 7 8 8
13 11 16 8 12
8 7 13 10 8
5 4 6 3 3
4 8 4 4 3
11 7 7 8 8
7 9 8 7 7
4 6 7 2 9
6 9 6 5 6

Split Design

Number of Levels of Main Treatments (n):

Number of Levels of Sub-Treatments (m):

Number of Blocks (r):

Acceptable Least p-value in Significance Testing: 0.01 0.001 0.0001

ANOVA Data (Reset and enter or copy your ANOVA data (space delimited) into this area):




Results:


ANOVA Demo Data for Split Design (n = 3, m = 6, r = 5) (No. main treatments (total matrices) = 3, No. sub-treatments (total rows in a matrix) = 6, No. blocks (total columns) = 5):

4 5 4 4 3
1 0 1 2 1
2 1 2 3 3
9 8 8 7 7
6 7 7 5 6
3 5 4 4 3
6 7 7 8 8
7 9 8 7 7
2 1 2 3 3
4 5 4 4 3
1 0 1 2 1
6 7 6 5 6
6 7 7 8 8
1 0 1 2 1
2 1 2 3 3
4 5 4 4 3
9 8 8 7 7
2 2 3 2 2

Orthogonal Design

Number of Levels of Treatments (n):

Number of Levels of Factors (w):

Number of Factor's Levels (q):

Number of Columns (m):

Number of Replications (r):

Acceptable Least p-value in Significance Testing: 0.01 0.001 0.0001

ANOVA Data (Reset and enter or copy your ANOVA data (space delimited) into this area):




Results:


ANOVA Demo Data for Orthogonalt Design (n = 9, w = 3, q = 3, m = 4, r = 2) (No. treatments (total rows) = 9, No. factors (IDs 1, 2 and 3 in the first 4 columns) = 3, No. factor levels (the first 3 columns) = 3, No. columns (the first 4 columns) = 4, No. replications (the last 2 columns) = 2):

1 1 1 1 195 189
1 2 2 2 161 178
1 3 3 3 196 186
2 1 2 3 160 155
2 2 3 1 176 176
2 3 1 2 176 172
3 1 3 2 167 177
3 2 1 3 171 181
3 3 2 1 159 160


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International Academy of Ecology and Environmental Sciences. E-mail: office@iaees.org
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