Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

Title produced by software

Two-Way ANOVA

Date of computation

Wed, 30 May 2012 07:57:16 -0400

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/May/30/t13383790477edtupbacs3cds4.htm/, Retrieved Fri, 03 May 2024 22:46:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=168005, Retrieved Fri, 03 May 2024 22:46:14 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

123

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Variability] [Two-Way ANOVA] [2010-11-30 21:42:30] [74be16979710d4c4e7c6647856088456]
- RM    [Two-Way ANOVA] [Two-Way ANOVA - C...] [2011-11-28 17:22:56] [98fd0e87c3eb04e0cc2efde01dbafab6]
- R PD    [Two-Way ANOVA] [] [2012-05-25 13:07:36] [8b1b3b62029043ad14f9403dfa37ed82]
- RM          [Two-Way ANOVA] [] [2012-05-30 11:57:16] [d41d8cd98f00b204e9800998ecf8427e] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

 'f'	 'hb'	161
 'f'	 'hb'	161
 'f'	 'hb'	157
 'f'	 'hb'	168
 'f'	 'hb'	163
 'f'	 'hb'	166
 'f'	 'hb'	168
 'f'	 'hb'	175
 'f'	 'hb'	170
 'f'	 'hb'	171
 'f'	 'hb'	166
 'f'	 'hb'	169
 'f'	 'hb'	166
 'f'	 'hb'	157
 'f'	 'hb'	166
 'f'	 'hb'	164
 'f'	 'hb'	169
 'f'	 'hb'	166
 'f'	 'hb'	164
 'f'	 'hb'	163
 'f'	 'hb'	160
 'f'	 'hb'	161
 'f'	 'hb'	174
 'f'	 'hb'	162
 'f'	 'hb'	165
 'f'	 'hb'	170
 'f'	 'hb'	173
 'f'	 'hb'	162
 'f'	 'hb'	165
 'f'	 'hb'	164
 'f'	 'hb'	158
 'f'	 'hb'	175
 'f'	 'hb'	165
 'f'	 'hb'	163
 'f'	 'hb'	166
 'f'	 'hb'	171
 'f'	 'hb'	160
 'f'	 'hb'	160
 'f'	 'hb'	165
 'f'	 'hb'	169
 'f'	 'hb'	167
 'f'	 'hb'	170
 'f'	 'hb'	165
 'f'	 'hb'	163
 'f'	 'hb'	162
 'f'	 'hb'	161
 'f'	 'hb'	165
 'f'	 'hb'	169
 'f'	 'hb'	159
 'f'	 'hb'	155
 'f'	 'hb'	164
 'f'	 'hb'	163
 'f'	 'hb'	163
 'f'	 'hb'	175
 'f'	 'hb'	164
 'f'	 'hb'	152
 'f'	 'hb'	167
 'f'	 'hb'	166
 'f'	 'hb'	166
 'f'	 'hb'	174
 'f'	 'hb'	167
 'f'	 'hb'	168
 'f'	 'hb'	178
 'f'	 'hb'	165
 'f'	 'hb'	169
 'f'	 'hb'	153
 'f'	 'hb'	157
 'f'	 'hb'	171
 'f'	 'hb'	157
 'f'	 'hb'	166
 'f'	 'hb'	160
 'f'	 'hb'	148
 'f'	 'hb'	162
 'f'	 'hb'	172
 'f'	 'hb'	167
 'f'	 'hb'	163
 'f'	 'hb'	165
 'f'	 'hb'	176
 'f'	 'hb'	171
 'f'	 'hb'	160
 'f'	 'hb'	165
 'f'	 'hb'	157
 'f'	 'hb'	173
 'f'	 'hb'	168
 'f'	 'hb'	162
 'f'	 'hb'	150
 'f'	 'hb'	162
 'f'	 'hb'	163
 'f'	 'hb'	170
 'f'	 'hb'	169
 'f'	 'hb'	167
 'f'	 'hb'	163
 'f'	 'hb'	161
 'f'	 'hb'	162
 'f'	 'hb'	172
 'f'	 'hb'	159
 'f'	 'hb'	170
 'f'	 'hb'	166
 'f'	 'hb'	158
 'f'	 'hb'	163
 'f'	 'hb'	174
 'f'	 'hb'	154
 'f'	 'hb'	165
 'f'	 'hb'	162
 'f'	 'hb'	172
 'f'	 'hb'	169
 'f'	 'hb'	158
 'f'	 'hb'	164
 'f'	 'hb'	156
 'f'	 'hb'	164
 'f'	 'wb'	58
 'f'	 'wb'	53
 'f'	 'wb'	59
 'f'	 'wb'	64
 'f'	 'wb'	52
 'f'	 'wb'	65
 'f'	 'wb'	62
 'f'	 'wb'	61
 'f'	 'wb'	61
 'f'	 'wb'	54
 'f'	 'wb'	50
 'f'	 'wb'	63
 'f'	 'wb'	58
 'f'	 'wb'	39
 'f'	 'wb'	71
 'f'	 'wb'	52
 'f'	 'wb'	68
 'f'	 'wb'	56
 'f'	 'wb'	54
 'f'	 'wb'	63
 'f'	 'wb'	54
 'f'	 'wb'	49
 'f'	 'wb'	54
 'f'	 'wb'	75
 'f'	 'wb'	56
 'f'	 'wb'	66
 'f'	 'wb'	78
 'f'	 'wb'	60
 'f'	 'wb'	64
 'f'	 'wb'	64
 'f'	 'wb'	52
 'f'	 'wb'	62
 'f'	 'wb'	55
 'f'	 'wb'	56
 'f'	 'wb'	50
 'f'	 'wb'	50
 'f'	 'wb'	50
 'f'	 'wb'	63
 'f'	 'wb'	61
 'f'	 'wb'	53
 'f'	 'wb'	60
 'f'	 'wb'	56
 'f'	 'wb'	53
 'f'	 'wb'	57
 'f'	 'wb'	57
 'f'	 'wb'	56
 'f'	 'wb'	56
 'f'	 'wb'	50
 'f'	 'wb'	52
 'f'	 'wb'	55
 'f'	 'wb'	55
 'f'	 'wb'	47
 'f'	 'wb'	45
 'f'	 'wb'	62
 'f'	 'wb'	53
 'f'	 'wb'	52
 'f'	 'wb'	57
 'f'	 'wb'	64
 'f'	 'wb'	59
 'f'	 'wb'	55
 'f'	 'wb'	76
 'f'	 'wb'	62
 'f'	 'wb'	68
 'f'	 'wb'	55
 'f'	 'wb'	52
 'f'	 'wb'	47
 'f'	 'wb'	45
 'f'	 'wb'	68
 'f'	 'wb'	44
 'f'	 'wb'	62
 'f'	 'wb'	56
 'f'	 'wb'	50
 'f'	 'wb'	53
 'f'	 'wb'	64
 'f'	 'wb'	62
 'f'	 'wb'	52
 'f'	 'wb'	53
 'f'	 'wb'	54
 'f'	 'wb'	64
 'f'	 'wb'	55
 'f'	 'wb'	55
 'f'	 'wb'	59
 'f'	 'wb'	70
 'f'	 'wb'	57
 'f'	 'wb'	47
 'f'	 'wb'	47
 'f'	 'wb'	55
 'f'	 'wb'	48
 'f'	 'wb'	59
 'f'	 'wb'	58
 'f'	 'wb'	57
 'f'	 'wb'	51
 'f'	 'wb'	54
 'f'	 'wb'	53
 'f'	 'wb'	59
 'f'	 'wb'	59
 'f'	 'wb'	63
 'f'	 'wb'	66
 'f'	 'wb'	53
 'f'	 'wb'	54
 'f'	 'wb'	60
 'f'	 'wb'	43
 'f'	 'wb'	63
 'f'	 'wb'	56
 'f'	 'wb'	60
 'f'	 'wb'	58
 'f'	 'wb'	50
 'f'	 'wb'	59
 'f'	 'wb'	51
 'f'	 'wb'	62
 'm'	 'hb'	182
 'm'	 'hb'	177
 'm'	 'hb'	170
 'm'	 'hb'	167
 'm'	 'hb'	186
 'm'	 'hb'	178
 'm'	 'hb'	171
 'm'	 'hb'	175
 'm'	 'hb'	187
 'm'	 'hb'	197
 'm'	 'hb'	180
 'm'	 'hb'	175
 'm'	 'hb'	173
 'm'	 'hb'	183
 'm'	 'hb'	178
 'm'	 'hb'	173
 'm'	 'hb'	176
 'm'	 'hb'	174
 'm'	 'hb'	178
 'm'	 'hb'	187
 'm'	 'hb'	178
 'm'	 'hb'	183
 'm'	 'hb'	179
 'm'	 'hb'	180
 'm'	 'hb'	182
 'm'	 'hb'	169
 'm'	 'hb'	185
 'm'	 'hb'	177
 'm'	 'hb'	176
 'm'	 'hb'	183
 'm'	 'hb'	172
 'm'	 'hb'	173
 'm'	 'hb'	165
 'm'	 'hb'	177
 'm'	 'hb'	180
 'm'	 'hb'	189
 'm'	 'hb'	178
 'm'	 'hb'	173
 'm'	 'hb'	182
 'm'	 'hb'	183
 'm'	 'hb'	168
 'm'	 'hb'	182
 'm'	 'hb'	178
 'm'	 'hb'	173
 'm'	 'hb'	184
 'm'	 'hb'	180
 'm'	 'hb'	189
 'm'	 'hb'	185
 'm'	 'hb'	178
 'm'	 'hb'	183
 'm'	 'hb'	179
 'm'	 'hb'	179
 'm'	 'hb'	184
 'm'	 'hb'	184
 'm'	 'hb'	169
 'm'	 'hb'	178
 'm'	 'hb'	178
 'm'	 'hb'	167
 'm'	 'hb'	179
 'm'	 'hb'	185
 'm'	 'hb'	177
 'm'	 'hb'	188
 'm'	 'hb'	191
 'm'	 'hb'	175
 'm'	 'hb'	184
 'm'	 'hb'	169
 'm'	 'hb'	172
 'm'	 'hb'	163
 'm'	 'hb'	191
 'm'	 'hb'	169
 'm'	 'hb'	170
 'm'	 'hb'	176
 'm'	 'hb'	168
 'm'	 'hb'	178
 'm'	 'hb'	170
 'm'	 'hb'	178
 'm'	 'hb'	174
 'm'	 'hb'	176
 'm'	 'hb'	181
 'm'	 'hb'	173
 'm'	 'hb'	183
 'm'	 'hb'	185
 'm'	 'hb'	173
 'm'	 'hb'	175
 'm'	 'hb'	180
 'm'	 'hb'	175
 'm'	 'hb'	181
 'm'	 'hb'	177
 'm'	 'wb'	77
 'm'	 'wb'	68
 'm'	 'wb'	76
 'm'	 'wb'	76
 'm'	 'wb'	69
 'm'	 'wb'	71
 'm'	 'wb'	65
 'm'	 'wb'	70
 'm'	 'wb'	92
 'm'	 'wb'	76
 'm'	 'wb'	119
 'm'	 'wb'	65
 'm'	 'wb'	66
 'm'	 'wb'	101
 'm'	 'wb'	75
 'm'	 'wb'	79
 'm'	 'wb'	64
 'm'	 'wb'	69
 'm'	 'wb'	88
 'm'	 'wb'	65
 'm'	 'wb'	80
 'm'	 'wb'	78
 'm'	 'wb'	85
 'm'	 'wb'	73
 'm'	 'wb'	82
 'm'	 'wb'	74
 'm'	 'wb'	102
 'm'	 'wb'	64
 'm'	 'wb'	65
 'm'	 'wb'	73
 'm'	 'wb'	75
 'm'	 'wb'	57
 'm'	 'wb'	68
 'm'	 'wb'	71
 'm'	 'wb'	71
 'm'	 'wb'	97
 'm'	 'wb'	80
 'm'	 'wb'	66
 'm'	 'wb'	69
 'm'	 'wb'	69
 'm'	 'wb'	55
 'm'	 'wb'	59
 'm'	 'wb'	62
 'm'	 'wb'	70
 'm'	 'wb'	84
 'm'	 'wb'	69
 'm'	 'wb'	88
 'm'	 'wb'	103
 'm'	 'wb'	63
 'm'	 'wb'	84
 'm'	 'wb'	79
 'm'	 'wb'	67
 'm'	 'wb'	83
 'm'	 'wb'	96
 'm'	 'wb'	75
 'm'	 'wb'	65
 'm'	 'wb'	78
 'm'	 'wb'	69
 'm'	 'wb'	67
 'm'	 'wb'	87
 'm'	 'wb'	83
 'm'	 'wb'	90
 'm'	 'wb'	85
 'm'	 'wb'	66
 'm'	 'wb'	88
 'm'	 'wb'	54
 'm'	 'wb'	69
 'm'	 'wb'	56
 'm'	 'wb'	96
 'm'	 'wb'	76
 'm'	 'wb'	61
 'm'	 'wb'	82
 'm'	 'wb'	62
 'm'	 'wb'	71
 'm'	 'wb'	66
 'm'	 'wb'	81
 'm'	 'wb'	68
 'm'	 'wb'	80
 'm'	 'wb'	82
 'm'	 'wb'	70
 'm'	 'wb'	76
 'm'	 'wb'	88
 'm'	 'wb'	89
 'm'	 'wb'	74
 'm'	 'wb'	83
 'm'	 'wb'	81
 'm'	 'wb'	90
 'm'	 'wb'	79
 'f'	 'rh'	159
 'f'	 'rh'	158
 'f'	 'rh'	155
 'f'	 'rh'	165
 'f'	 'rh'	160
 'f'	 'rh'	165
 'f'	 'rh'	165
 'f'	 'rh'	171
 'f'	 'rh'	170
 'f'	 'rh'	168
 'f'	 'rh'	165
 'f'	 'rh'	168
 'f'	 'rh'	160
 'f'	 'rh'	153
 'f'	 'rh'	165
 'f'	 'rh'	161
 'f'	 'rh'	170
 'f'	 'rh'	165
 'f'	 'rh'	160
 'f'	 'rh'	159
 'f'	 'rh'	158
 'f'	 'rh'	173
 'f'	 'rh'	158
 'f'	 'rh'	163
 'f'	 'rh'	169
 'f'	 'rh'	160
 'f'	 'rh'	163
 'f'	 'rh'	161
 'f'	 'rh'	155
 'f'	 'rh'	171
 'f'	 'rh'	163
 'f'	 'rh'	159
 'f'	 'rh'	161
 'f'	 'rh'	150
 'f'	 'rh'	158
 'f'	 'rh'	163
 'f'	 'rh'	175
 'f'	 'rh'	163
 'f'	 'rh'	170
 'f'	 'rh'	165
 'f'	 'rh'	160
 'f'	 'rh'	160
 'f'	 'rh'	161
 'f'	 'rh'	160
 'f'	 'rh'	165
 'f'	 'rh'	153
 'f'	 'rh'	154
 'f'	 'rh'	163
 'f'	 'rh'	160
 'f'	 'rh'	160
 'f'	 'rh'	173
 'f'	 'rh'	160
 'f'	 'rh'	150
 'f'	 'rh'	164
 'f'	 'rh'	165
 'f'	 'rh'	163
 'f'	 'rh'	171
 'f'	 'rh'	165
 'f'	 'rh'	163
 'f'	 'rh'	175
 'f'	 'rh'	163
 'f'	 'rh'	154
 'f'	 'rh'	153
 'f'	 'rh'	169
 'f'	 'rh'	155
 'f'	 'rh'	163
 'f'	 'rh'	158
 'f'	 'rh'	148
 'f'	 'rh'	160
 'f'	 'rh'	168
 'f'	 'rh'	160
 'f'	 'rh'	163
 'f'	 'rh'	176
 'f'	 'rh'	171
 'f'	 'rh'	155
 'f'	 'rh'	165
 'f'	 'rh'	158
 'f'	 'rh'	170
 'f'	 'rh'	165
 'f'	 'rh'	160
 'f'	 'rh'	152
 'f'	 'rh'	160
 'f'	 'rh'	165
 'f'	 'rh'	160
 'f'	 'rh'	160
 'f'	 'rh'	158
 'f'	 'rh'	171
 'f'	 'rh'	155
 'f'	 'rh'	168
 'f'	 'rh'	165
 'f'	 'rh'	155
 'f'	 'rh'	160
 'f'	 'rh'	160
 'f'	 'rh'	168
 'f'	 'rh'	166
 'f'	 'rh'	155
 'f'	 'rh'	165
 'f'	 'rh'	158
 'f'	 'rh'	161
 'f'	 'rw'	51
 'f'	 'rw'	54
 'f'	 'rw'	59
 'f'	 'rw'	64
 'f'	 'rw'	57
 'f'	 'rw'	66
 'f'	 'rw'	62
 'f'	 'rw'	61
 'f'	 'rw'	61
 'f'	 'rw'	59
 'f'	 'rw'	50
 'f'	 'rw'	61
 'f'	 'rw'	60
 'f'	 'rw'	41
 'f'	 'rw'	71
 'f'	 'rw'	52
 'f'	 'rw'	63
 'f'	 'rw'	54
 'f'	 'rw'	53
 'f'	 'rw'	59
 'f'	 'rw'	55
 'f'	 'rw'	56
 'f'	 'rw'	75
 'f'	 'rw'	57
 'f'	 'rw'	65
 'f'	 'rw'	75
 'f'	 'rw'	59
 'f'	 'rw'	63
 'f'	 'rw'	62
 'f'	 'rw'	51
 'f'	 'rw'	61
 'f'	 'rw'	54
 'f'	 'rw'	57
 'f'	 'rw'	50
 'f'	 'rw'	55
 'f'	 'rw'	64
 'f'	 'rw'	60
 'f'	 'rw'	52
 'f'	 'rw'	55
 'f'	 'rw'	56
 'f'	 'rw'	53
 'f'	 'rw'	59
 'f'	 'rw'	56
 'f'	 'rw'	56
 'f'	 'rw'	57
 'f'	 'rw'	50
 'f'	 'rw'	52
 'f'	 'rw'	55
 'f'	 'rw'	47
 'f'	 'rw'	45
 'f'	 'rw'	63
 'f'	 'rw'	51
 'f'	 'rw'	51
 'f'	 'rw'	55
 'f'	 'rw'	64
 'f'	 'rw'	55
 'f'	 'rw'	57
 'f'	 'rw'	77
 'f'	 'rw'	62
 'f'	 'rw'	68
 'f'	 'rw'	55
 'f'	 'rw'	56
 'f'	 'rw'	45
 'f'	 'rw'	68
 'f'	 'rw'	44
 'f'	 'rw'	61
 'f'	 'rw'	53
 'f'	 'rw'	47
 'f'	 'rw'	53
 'f'	 'rw'	62
 'f'	 'rw'	53
 'f'	 'rw'	55
 'f'	 'rw'	55
 'f'	 'rw'	66
 'f'	 'rw'	55
 'f'	 'rw'	55
 'f'	 'rw'	55
 'f'	 'rw'	67
 'f'	 'rw'	58
 'f'	 'rw'	47
 'f'	 'rw'	45
 'f'	 'rw'	44
 'f'	 'rw'	56
 'f'	 'rw'	50
 'f'	 'rw'	54
 'f'	 'rw'	52
 'f'	 'rw'	58
 'f'	 'rw'	59
 'f'	 'rw'	62
 'f'	 'rw'	66
 'f'	 'rw'	50
 'f'	 'rw'	59
 'f'	 'rw'	56
 'f'	 'rw'	55
 'f'	 'rw'	54
 'f'	 'rw'	49
 'f'	 'rw'	59
 'f'	 'rw'	51
 'f'	 'rw'	61
 'm'	 'rh'	180
 'm'	 'rh'	175
 'm'	 'rh'	165
 'm'	 'rh'	165
 'm'	 'rh'	180
 'm'	 'rh'	175
 'm'	 'rh'	170
 'm'	 'rh'	174
 'm'	 'rh'	185
 'm'	 'rh'	200
 'm'	 'rh'	178
 'm'	 'rh'	173
 'm'	 'rh'	170
 'm'	 'rh'	180
 'm'	 'rh'	175
 'm'	 'rh'	173
 'm'	 'rh'	175
 'm'	 'rh'	171
 'm'	 'rh'	175
 'm'	 'rh'	188
 'm'	 'rh'	178
 'm'	 'rh'	180
 'm'	 'rh'	175
 'm'	 'rh'	183
 'm'	 'rh'	170
 'm'	 'rh'	185
 'm'	 'rh'	172
 'm'	 'rh'	180
 'm'	 'rh'	169
 'm'	 'rh'	170
 'm'	 'rh'	165
 'm'	 'rh'	170
 'm'	 'rh'	175
 'm'	 'rh'	185
 'm'	 'rh'	175
 'm'	 'rh'	175
 'm'	 'rh'	180
 'm'	 'rh'	183
 'm'	 'rh'	170
 'm'	 'rh'	183
 'm'	 'rh'	175
 'm'	 'rh'	170
 'm'	 'rh'	183
 'm'	 'rh'	180
 'm'	 'rh'	185
 'm'	 'rh'	182
 'm'	 'rh'	175
 'm'	 'rh'	183
 'm'	 'rh'	171
 'm'	 'rh'	179
 'm'	 'rh'	181
 'm'	 'rh'	183
 'm'	 'rh'	165
 'm'	 'rh'	178
 'm'	 'rh'	175
 'm'	 'rh'	165
 'm'	 'rh'	185
 'm'	 'rh'	175
 'm'	 'rh'	185
 'm'	 'rh'	188
 'm'	 'rh'	175
 'm'	 'rh'	183
 'm'	 'rh'	165
 'm'	 'rh'	174
 'm'	 'rh'	161
 'm'	 'rh'	188
 'm'	 'rh'	165
 'm'	 'rh'	170
 'm'	 'rh'	168
 'm'	 'rh'	178
 'm'	 'rh'	165
 'm'	 'rh'	175
 'm'	 'rh'	173
 'm'	 'rh'	175
 'm'	 'rh'	173
 'm'	 'rh'	180
 'm'	 'rh'	188
 'm'	 'rh'	173
 'm'	 'rh'	175
 'm'	 'rh'	180
 'm'	 'rh'	178
 'm'	 'rh'	178
 'm'	 'rw'	77
 'm'	 'rw'	70
 'm'	 'rw'	76
 'm'	 'rw'	77
 'm'	 'rw'	73
 'm'	 'rw'	71
 'm'	 'rw'	64
 'm'	 'rw'	75
 'm'	 'rw'	101
 'm'	 'rw'	75
 'm'	 'rw'	124
 'm'	 'rw'	66
 'm'	 'rw'	70
 'm'	 'rw'	100
 'm'	 'rw'	73
 'm'	 'rw'	76
 'm'	 'rw'	65
 'm'	 'rw'	69
 'm'	 'rw'	86
 'm'	 'rw'	67
 'm'	 'rw'	80
 'm'	 'rw'	80
 'm'	 'rw'	82
 'm'	 'rw'	85
 'm'	 'rw'	73
 'm'	 'rw'	107
 'm'	 'rw'	64
 'm'	 'rw'	74
 'm'	 'rw'	70
 'm'	 'rw'	58
 'm'	 'rw'	69
 'm'	 'rw'	71
 'm'	 'rw'	76
 'm'	 'rw'	98
 'm'	 'rw'	76
 'm'	 'rw'	66
 'm'	 'rw'	70
 'm'	 'rw'	70
 'm'	 'rw'	56
 'm'	 'rw'	61
 'm'	 'rw'	66
 'm'	 'rw'	68
 'm'	 'rw'	86
 'm'	 'rw'	71
 'm'	 'rw'	87
 'm'	 'rw'	101
 'm'	 'rw'	63
 'm'	 'rw'	90
 'm'	 'rw'	79
 'm'	 'rw'	67
 'm'	 'rw'	83
 'm'	 'rw'	94
 'm'	 'rw'	76
 'm'	 'rw'	66
 'm'	 'rw'	77
 'm'	 'rw'	73
 'm'	 'rw'	89
 'm'	 'rw'	84
 'm'	 'rw'	91
 'm'	 'rw'	83
 'm'	 'rw'	68
 'm'	 'rw'	86
 'm'	 'rw'	58
 'm'	 'rw'	68
 'm'	 'rw'	58
 'm'	 'rw'	95
 'm'	 'rw'	75
 'm'	 'rw'	61
 'm'	 'rw'	64
 'm'	 'rw'	68
 'm'	 'rw'	67
 'm'	 'rw'	82
 'm'	 'rw'	68
 'm'	 'rw'	78
 'm'	 'rw'	70
 'm'	 'rw'	75
 'm'	 'rw'	93
 'm'	 'rw'	86
 'm'	 'rw'	71
 'm'	 'rw'	80
 'm'	 'rw'	91
 'm'	 'rw'	81

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168005&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168005&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168005&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

ANOVA Model
Response ~ Treatment_A * Treatment_B
means	164.736	13.275	-2.504	-107.938	-107.791	0.749	6.488	5.677

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 164.736 & 13.275 & -2.504 & -107.938 & -107.791 & 0.749 & 6.488 & 5.677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168005&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]164.736[/C][C]13.275[/C][C]-2.504[/C][C]-107.938[/C][C]-107.791[/C][C]0.749[/C][C]6.488[/C][C]5.677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168005&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168005&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

ANOVA Model
Response ~ Treatment_A * Treatment_B
means	164.736	13.275	-2.504	-107.938	-107.791	0.749	6.488	5.677

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
	1
Treatment_A	1	50910.643	50910.643	783.57	0
Treatment_B	1	2054254.108	684751.369	10539.062	0
Treatment_A:Treatment_B	1	1554.987	518.329	7.978	0
Residuals	750	48729.529	64.973

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 50910.643 & 50910.643 & 783.57 & 0 \tabularnewline
Treatment_B & 1 & 2054254.108 & 684751.369 & 10539.062 & 0 \tabularnewline
Treatment_A:Treatment_B & 1 & 1554.987 & 518.329 & 7.978 & 0 \tabularnewline
Residuals & 750 & 48729.529 & 64.973 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168005&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C][/C][C]1[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]1[/C][C]50910.643[/C][C]50910.643[/C][C]783.57[/C][C]0[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]2054254.108[/C][C]684751.369[/C][C]10539.062[/C][C]0[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]1554.987[/C][C]518.329[/C][C]7.978[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]750[/C][C]48729.529[/C][C]64.973[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168005&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168005&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
	1
Treatment_A	1	50910.643	50910.643	783.57	0
Treatment_B	1	2054254.108	684751.369	10539.062	0
Treatment_A:Treatment_B	1	1554.987	518.329	7.978	0
Residuals	750	48729.529	64.973

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
m-f	16.478	15.323	17.634	0
rh-hb	-2.192	-4.327	-0.058	0.041
rw-hb	-105.027	-107.161	-102.892	0
wb-hb	-105.268	-107.354	-103.182	0
rw-rh	-102.834	-105.016	-100.653	0
wb-rh	-103.075	-105.21	-100.941	0
wb-rw	-0.241	-2.375	1.893	0.991
m:hb-f:hb	13.275	9.771	16.779	0
f:rh-f:hb	-2.504	-5.898	0.89	0.328
m:rh-f:hb	11.52	7.945	15.094	0
f:rw-f:hb	-107.938	-111.332	-104.544	0
m:rw-f:hb	-88.175	-91.75	-84.601	0
f:wb-f:hb	-107.791	-111.094	-104.487	0
m:wb-f:hb	-88.839	-92.343	-85.335	0
f:rh-m:hb	-15.779	-19.368	-12.19	0
m:rh-m:hb	-1.755	-5.516	2.005	0.849
f:rw-m:hb	-121.213	-124.803	-117.624	0
m:rw-m:hb	-101.45	-105.211	-97.69	0
f:wb-m:hb	-121.066	-124.57	-117.562	0
m:wb-m:hb	-102.114	-105.807	-98.42	0
m:rh-f:rh	14.024	10.365	17.682	0
f:rw-f:rh	-105.434	-108.917	-101.952	0
m:rw-f:rh	-85.671	-89.33	-82.013	0
f:wb-f:rh	-105.287	-108.681	-101.893	0
m:wb-f:rh	-86.335	-89.924	-82.745	0
f:rw-m:rh	-119.458	-123.116	-115.8	0
m:rw-m:rh	-99.695	-103.521	-95.869	0
f:wb-m:rh	-119.311	-122.885	-115.736	0
m:wb-m:rh	-100.358	-104.119	-96.598	0
m:rw-f:rw	19.763	16.105	23.421	0
f:wb-f:rw	0.147	-3.247	3.542	1
m:wb-f:rw	19.1	15.51	22.689	0
f:wb-m:rw	-19.616	-23.19	-16.041	0
m:wb-m:rw	-0.663	-4.424	3.097	0.999
m:wb-f:wb	18.952	15.448	22.456	0

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
m-f & 16.478 & 15.323 & 17.634 & 0 \tabularnewline
rh-hb & -2.192 & -4.327 & -0.058 & 0.041 \tabularnewline
rw-hb & -105.027 & -107.161 & -102.892 & 0 \tabularnewline
wb-hb & -105.268 & -107.354 & -103.182 & 0 \tabularnewline
rw-rh & -102.834 & -105.016 & -100.653 & 0 \tabularnewline
wb-rh & -103.075 & -105.21 & -100.941 & 0 \tabularnewline
wb-rw & -0.241 & -2.375 & 1.893 & 0.991 \tabularnewline
m:hb-f:hb & 13.275 & 9.771 & 16.779 & 0 \tabularnewline
f:rh-f:hb & -2.504 & -5.898 & 0.89 & 0.328 \tabularnewline
m:rh-f:hb & 11.52 & 7.945 & 15.094 & 0 \tabularnewline
f:rw-f:hb & -107.938 & -111.332 & -104.544 & 0 \tabularnewline
m:rw-f:hb & -88.175 & -91.75 & -84.601 & 0 \tabularnewline
f:wb-f:hb & -107.791 & -111.094 & -104.487 & 0 \tabularnewline
m:wb-f:hb & -88.839 & -92.343 & -85.335 & 0 \tabularnewline
f:rh-m:hb & -15.779 & -19.368 & -12.19 & 0 \tabularnewline
m:rh-m:hb & -1.755 & -5.516 & 2.005 & 0.849 \tabularnewline
f:rw-m:hb & -121.213 & -124.803 & -117.624 & 0 \tabularnewline
m:rw-m:hb & -101.45 & -105.211 & -97.69 & 0 \tabularnewline
f:wb-m:hb & -121.066 & -124.57 & -117.562 & 0 \tabularnewline
m:wb-m:hb & -102.114 & -105.807 & -98.42 & 0 \tabularnewline
m:rh-f:rh & 14.024 & 10.365 & 17.682 & 0 \tabularnewline
f:rw-f:rh & -105.434 & -108.917 & -101.952 & 0 \tabularnewline
m:rw-f:rh & -85.671 & -89.33 & -82.013 & 0 \tabularnewline
f:wb-f:rh & -105.287 & -108.681 & -101.893 & 0 \tabularnewline
m:wb-f:rh & -86.335 & -89.924 & -82.745 & 0 \tabularnewline
f:rw-m:rh & -119.458 & -123.116 & -115.8 & 0 \tabularnewline
m:rw-m:rh & -99.695 & -103.521 & -95.869 & 0 \tabularnewline
f:wb-m:rh & -119.311 & -122.885 & -115.736 & 0 \tabularnewline
m:wb-m:rh & -100.358 & -104.119 & -96.598 & 0 \tabularnewline
m:rw-f:rw & 19.763 & 16.105 & 23.421 & 0 \tabularnewline
f:wb-f:rw & 0.147 & -3.247 & 3.542 & 1 \tabularnewline
m:wb-f:rw & 19.1 & 15.51 & 22.689 & 0 \tabularnewline
f:wb-m:rw & -19.616 & -23.19 & -16.041 & 0 \tabularnewline
m:wb-m:rw & -0.663 & -4.424 & 3.097 & 0.999 \tabularnewline
m:wb-f:wb & 18.952 & 15.448 & 22.456 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168005&T=3

[TABLE]
[ROW][C]Tukey Honest Significant Difference Comparisons[/C][/ROW]
[ROW][C] [/C][C]diff[/C][C]lwr[/C][C]upr[/C][C]p adj[/C][/ROW]
[ROW][C]m-f[/C][C]16.478[/C][C]15.323[/C][C]17.634[/C][C]0[/C][/ROW]
[ROW][C]rh-hb[/C][C]-2.192[/C][C]-4.327[/C][C]-0.058[/C][C]0.041[/C][/ROW]
[ROW][C]rw-hb[/C][C]-105.027[/C][C]-107.161[/C][C]-102.892[/C][C]0[/C][/ROW]
[ROW][C]wb-hb[/C][C]-105.268[/C][C]-107.354[/C][C]-103.182[/C][C]0[/C][/ROW]
[ROW][C]rw-rh[/C][C]-102.834[/C][C]-105.016[/C][C]-100.653[/C][C]0[/C][/ROW]
[ROW][C]wb-rh[/C][C]-103.075[/C][C]-105.21[/C][C]-100.941[/C][C]0[/C][/ROW]
[ROW][C]wb-rw[/C][C]-0.241[/C][C]-2.375[/C][C]1.893[/C][C]0.991[/C][/ROW]
[ROW][C]m:hb-f:hb[/C][C]13.275[/C][C]9.771[/C][C]16.779[/C][C]0[/C][/ROW]
[ROW][C]f:rh-f:hb[/C][C]-2.504[/C][C]-5.898[/C][C]0.89[/C][C]0.328[/C][/ROW]
[ROW][C]m:rh-f:hb[/C][C]11.52[/C][C]7.945[/C][C]15.094[/C][C]0[/C][/ROW]
[ROW][C]f:rw-f:hb[/C][C]-107.938[/C][C]-111.332[/C][C]-104.544[/C][C]0[/C][/ROW]
[ROW][C]m:rw-f:hb[/C][C]-88.175[/C][C]-91.75[/C][C]-84.601[/C][C]0[/C][/ROW]
[ROW][C]f:wb-f:hb[/C][C]-107.791[/C][C]-111.094[/C][C]-104.487[/C][C]0[/C][/ROW]
[ROW][C]m:wb-f:hb[/C][C]-88.839[/C][C]-92.343[/C][C]-85.335[/C][C]0[/C][/ROW]
[ROW][C]f:rh-m:hb[/C][C]-15.779[/C][C]-19.368[/C][C]-12.19[/C][C]0[/C][/ROW]
[ROW][C]m:rh-m:hb[/C][C]-1.755[/C][C]-5.516[/C][C]2.005[/C][C]0.849[/C][/ROW]
[ROW][C]f:rw-m:hb[/C][C]-121.213[/C][C]-124.803[/C][C]-117.624[/C][C]0[/C][/ROW]
[ROW][C]m:rw-m:hb[/C][C]-101.45[/C][C]-105.211[/C][C]-97.69[/C][C]0[/C][/ROW]
[ROW][C]f:wb-m:hb[/C][C]-121.066[/C][C]-124.57[/C][C]-117.562[/C][C]0[/C][/ROW]
[ROW][C]m:wb-m:hb[/C][C]-102.114[/C][C]-105.807[/C][C]-98.42[/C][C]0[/C][/ROW]
[ROW][C]m:rh-f:rh[/C][C]14.024[/C][C]10.365[/C][C]17.682[/C][C]0[/C][/ROW]
[ROW][C]f:rw-f:rh[/C][C]-105.434[/C][C]-108.917[/C][C]-101.952[/C][C]0[/C][/ROW]
[ROW][C]m:rw-f:rh[/C][C]-85.671[/C][C]-89.33[/C][C]-82.013[/C][C]0[/C][/ROW]
[ROW][C]f:wb-f:rh[/C][C]-105.287[/C][C]-108.681[/C][C]-101.893[/C][C]0[/C][/ROW]
[ROW][C]m:wb-f:rh[/C][C]-86.335[/C][C]-89.924[/C][C]-82.745[/C][C]0[/C][/ROW]
[ROW][C]f:rw-m:rh[/C][C]-119.458[/C][C]-123.116[/C][C]-115.8[/C][C]0[/C][/ROW]
[ROW][C]m:rw-m:rh[/C][C]-99.695[/C][C]-103.521[/C][C]-95.869[/C][C]0[/C][/ROW]
[ROW][C]f:wb-m:rh[/C][C]-119.311[/C][C]-122.885[/C][C]-115.736[/C][C]0[/C][/ROW]
[ROW][C]m:wb-m:rh[/C][C]-100.358[/C][C]-104.119[/C][C]-96.598[/C][C]0[/C][/ROW]
[ROW][C]m:rw-f:rw[/C][C]19.763[/C][C]16.105[/C][C]23.421[/C][C]0[/C][/ROW]
[ROW][C]f:wb-f:rw[/C][C]0.147[/C][C]-3.247[/C][C]3.542[/C][C]1[/C][/ROW]
[ROW][C]m:wb-f:rw[/C][C]19.1[/C][C]15.51[/C][C]22.689[/C][C]0[/C][/ROW]
[ROW][C]f:wb-m:rw[/C][C]-19.616[/C][C]-23.19[/C][C]-16.041[/C][C]0[/C][/ROW]
[ROW][C]m:wb-m:rw[/C][C]-0.663[/C][C]-4.424[/C][C]3.097[/C][C]0.999[/C][/ROW]
[ROW][C]m:wb-f:wb[/C][C]18.952[/C][C]15.448[/C][C]22.456[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168005&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168005&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Tukey Honest Significant Difference Comparisons
	diff	lwr	upr	p adj
m-f	16.478	15.323	17.634	0
rh-hb	-2.192	-4.327	-0.058	0.041
rw-hb	-105.027	-107.161	-102.892	0
wb-hb	-105.268	-107.354	-103.182	0
rw-rh	-102.834	-105.016	-100.653	0
wb-rh	-103.075	-105.21	-100.941	0
wb-rw	-0.241	-2.375	1.893	0.991
m:hb-f:hb	13.275	9.771	16.779	0
f:rh-f:hb	-2.504	-5.898	0.89	0.328
m:rh-f:hb	11.52	7.945	15.094	0
f:rw-f:hb	-107.938	-111.332	-104.544	0
m:rw-f:hb	-88.175	-91.75	-84.601	0
f:wb-f:hb	-107.791	-111.094	-104.487	0
m:wb-f:hb	-88.839	-92.343	-85.335	0
f:rh-m:hb	-15.779	-19.368	-12.19	0
m:rh-m:hb	-1.755	-5.516	2.005	0.849
f:rw-m:hb	-121.213	-124.803	-117.624	0
m:rw-m:hb	-101.45	-105.211	-97.69	0
f:wb-m:hb	-121.066	-124.57	-117.562	0
m:wb-m:hb	-102.114	-105.807	-98.42	0
m:rh-f:rh	14.024	10.365	17.682	0
f:rw-f:rh	-105.434	-108.917	-101.952	0
m:rw-f:rh	-85.671	-89.33	-82.013	0
f:wb-f:rh	-105.287	-108.681	-101.893	0
m:wb-f:rh	-86.335	-89.924	-82.745	0
f:rw-m:rh	-119.458	-123.116	-115.8	0
m:rw-m:rh	-99.695	-103.521	-95.869	0
f:wb-m:rh	-119.311	-122.885	-115.736	0
m:wb-m:rh	-100.358	-104.119	-96.598	0
m:rw-f:rw	19.763	16.105	23.421	0
f:wb-f:rw	0.147	-3.247	3.542	1
m:wb-f:rw	19.1	15.51	22.689	0
f:wb-m:rw	-19.616	-23.19	-16.041	0
m:wb-m:rw	-0.663	-4.424	3.097	0.999
m:wb-f:wb	18.952	15.448	22.456	0

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	7	13.343	0
	750

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 7 & 13.343 & 0 \tabularnewline
  & 750 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168005&T=4

[TABLE]
[ROW][C]Levenes Test for Homogeneity of Variance[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]Group[/C][C]7[/C][C]13.343[/C][C]0[/C][/ROW]
[ROW][C] [/C][C]750[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168005&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168005&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Levenes Test for Homogeneity of Variance
	Df	F value	Pr(>F)
Group	7	13.343	0
	750

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 3 ; par2 = 1 ; par3 = 2 ; par4 = TRUE ;

Parameters (R input):

par1 = 3 ; par2 = 1 ; par3 = 2 ; par4 = TRUE ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;

R code (references can be found in the software module):

cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
cat3 <- as.numeric(par3)
intercept<-as.logical(par4)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
f2 <- as.character(x[,cat3])
xdf<-data.frame(x1,f1, f2)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
(V3 <-dimnames(y)[[1]][cat3])
names(xdf)<-c('Response', 'Treatment_A', 'Treatment_B')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment_A * Treatment_B- 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment_A * Treatment_B, data = xdf) )
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Model', length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'means',,TRUE)
for(i in 1:length(lmxdf$coefficients)){
a<-table.element(a, round(lmxdf$coefficients[i], digits=3),,FALSE)
}
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',,TRUE)
a<-table.element(a, 'Df',,FALSE)
a<-table.element(a, 'Sum Sq',,FALSE)
a<-table.element(a, 'Mean Sq',,FALSE)
a<-table.element(a, 'F value',,FALSE)
a<-table.element(a, 'Pr(>F)',,FALSE)
a<-table.row.end(a)
for(i in 1 : length(rownames(anova.xdf))-1){
a<-table.row.start(a)
a<-table.element(a,rownames(anova.xdf)[i] ,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[i], digits=3),,FALSE)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$'Df'[i+1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='anovaplot.png')
boxplot(Response ~ Treatment_A + Treatment_B, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups')
dev.off()
bitmap(file='designplot.png')
xdf2 <- xdf # to preserve xdf make copy for function
names(xdf2) <- c(V1, V2, V3)
plot.design(xdf2, main='Design Plot of Group Means')
dev.off()
bitmap(file='interactionplot.png')
interaction.plot(xdf$Treatment_A, xdf$Treatment_B, xdf$Response, xlab=V2, ylab=V1, trace.label=V3, main='Possible Interactions Between Anova Groups')
dev.off()
if(intercept==TRUE){
thsd<-TukeyHSD(aov.xdf)
names(thsd) <- c(V2, V3, paste(V2, ':', V3, sep=''))
bitmap(file='TukeyHSDPlot.png')
layout(matrix(c(1,2,3,3), 2,2))
plot(thsd, las=1)
dev.off()
}
if(intercept==TRUE){
ntables<-length(names(thsd))
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Tukey Honest Significant Difference Comparisons', 5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ', 1, TRUE)
for(i in 1:4){
a<-table.element(a,colnames(thsd[[1]])[i], 1, TRUE)
}
a<-table.row.end(a)
for(nt in 1:ntables){
for(i in 1:length(rownames(thsd[[nt]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[nt]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[nt]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
} # end nt
a<-table.end(a)
table.save(a,file='hsdtable.tab')
}#end if hsd tables
if(intercept==FALSE){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'TukeyHSD Message', 1,TRUE)
a<-table.row.end(a)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Must Include Intercept to use Tukey Test ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
library(car)
lt.lmxdf<-levene.test(lmxdf)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Levenes Test for Homogeneity of Variance', 4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
for (i in 1:3){
a<-table.element(a,names(lt.lmxdf)[i], 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Group', 1, TRUE)
for (i in 1:3){
a<-table.element(a,round(lt.lmxdf[[i]][1], digits=3), 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
a<-table.element(a,lt.lmxdf[[1]][2], 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code