Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationTue, 13 Dec 2011 12:51:52 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323798741zssju527i23abzi.htm/, Retrieved Thu, 31 Oct 2024 22:50:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154584, Retrieved Thu, 31 Oct 2024 22:50:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 19:35:21] [b98453cac15ba1066b407e146608df68]
-   PD    [Recursive Partitioning (Regression Trees)] [WS 10 - Recursive...] [2011-12-13 17:51:52] [e1c4030d3eb0ab0fcc7a7b48aeaac474] [Current]
Feedback Forum

Post a new message
Dataseries X:
2	13	12	30	33	13	16
1	8	8	32	35	11	15
2	14	12	30	35	12	13
2	14	11	33	25	13	14
1	13	11	36	39	12	17
1	16	13	37	37	12	13
1	14	11	31	31	13	12
1	13	10	36	28	12	9
2	15	7	40	38	15	25
1	13	10	31	32	11	13
2	16	12	24	32	13	10
1	20	15	46	46	12	13
1	17	12	40	40	12	9
1	15	15	27	33	12	14
2	16	12	32	25	15	26
1	16	10	41	37	13	12
1	12	10	28	33	13	11
2	9	8	34	33	12	19
2	15	11	31	35	11	12
2	17	14	38	39	11	9
1	12	12	37	36	13	15
1	10	11	34	37	10	15
2	11	6	33	43	12	23
2	16	12	38	27	13	20
1	16	14	27	31	10	0
2	15	11	36	33	12	15
1	13	8	37	35	13	8
2	14	12	35	36	12	12
1	19	15	44	39	11	11
1	16	13	41	31	11	18
2	17	14	29	34	14	19
1	10	12	31	29	12	13
1	15	7	32	37	14	22
1	14	11	35	30	12	12
1	14	7	36	32	12	15
2	16	12	28	31	13	16
1	17	12	34	34	15	16
1	15	12	36	30	12	13
2	17	13	33	33	16	11
2	14	15	35	37	10	16
1	10	9	34	33	13	14
2	14	9	38	28	12	11
2	16	11	35	32	12	20
2	18	14	40	40	16	16
1	15	12	35	39	12	12
1	16	15	32	28	16	17
1	16	12	33	33	13	11
1	10	6	31	36	10	12
2	8	5	32	35	14	14
1	17	13	35	34	13	13
1	14	11	32	35	12	14
1	12	11	26	30	13	19
2	10	6	38	35	16	17
1	14	12	45	37	12	11
1	12	10	36	40	12	12
1	16	6	37	34	13	12
1	16	12	33	37	13	14
1	15	14	35	38	11	15
2	11	6	32	27	14	18
1	16	11	32	27	16	16
2	8	6	32	27	16	16
1	17	14	33	39	14	19
1	16	12	37	37	14	17
1	15	12	40	32	14	15
2	8	8	35	27	14	13
1	13	10	30	35	10	16
1	14	11	36	40	13	17
1	13	7	34	32	14	16
1	16	12	34	36	17	13
2	12	9	37	35	12	15
1	19	13	34	31	12	16
1	19	14	37	34	12	10
1	12	6	43	36	15	19
1	14	12	39	40	10	11
2	15	6	29	33	13	17
1	13	14	41	38	12	19
2	16	12	32	33	13	15
2	10	10	34	35	14	15
1	15	10	34	30	12	17
1	16	12	35	31	13	13
1	15	11	41	42	14	17
2	11	10	32	33	10	12
2	9	7	39	35	12	27
1	16	12	33	33	13	12
1	12	12	30	31	10	15
2	14	12	32	36	13	18
1	14	10	41	32	13	19
1	13	10	24	43	12	21
2	15	12	35	33	12	13
2	17	12	39	34	15	16
2	14	12	32	36	12	13
2	9	9	28	33	16	20
2	11	11	31	32	15	17
1	9	10	36	36	10	10
2	7	5	39	39	13	18
1	13	10	33	30	0	11
2	15	10	36	34	10	18
1	12	12	31	34	12	14
2	15	11	33	36	14	11
2	14	9	33	31	12	14
1	15	15	33	27	13	12
2	9	9	39	28	14	22
1	16	12	35	37	11	12
1	16	16	37	36	11	12
1	14	10	29	31	12	15
2	14	14	34	31	9	13
2	13	10	35	31	13	13
1	14	11	36	34	13	16
2	16	12	29	36	12	12
1	16	14	35	30	14	16
1	13	10	35	37	12	15
2	12	9	36	29	10	19
2	16	12	38	37	11	15
1	16	11	36	38	14	13
1	16	12	37	38	12	9
2	10	7	32	33	13	14
2	14	16	34	34	13	14
2	12	11	29	32	9	12
2	12	12	38	36	13	17
1	12	9	34	30	11	11
1	12	9	33	34	12	17
1	19	15	42	42	13	15
2	14	10	32	24	12	15
1	13	11	31	29	12	11
1	17	14	34	32	11	14
2	16	12	39	31	12	14
1	15	12	38	37	12	14
1	12	12	36	34	13	14
1	8	11	32	35	14	13
1	10	9	37	34	13	14
1	16	11	36	33	12	10
2	10	6	34	31	15	17
2	16	12	34	32	13	11
1	10	12	34	37	14	13
1	18	14	38	39	12	14
1	12	8	33	31	11	14
2	16	15	5	0	12	18
2	10	9	28	30	11	18
2	15	9	33	30	14	18
1	17	11	41	43	13	14
2	16	12	30	31	12	12
2	14	10	31	33	14	16
2	12	11	34	31	13	17
2	11	10	33	38	11	13
2	15	12	37	32	16	16
1	7	11	34	38	13	15




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154584&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154584&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154584&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Goodness of Fit
Correlation0.6568
R-squared0.4314
RMSE2.0666

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6568 \tabularnewline
R-squared & 0.4314 \tabularnewline
RMSE & 2.0666 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154584&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6568[/C][/ROW]
[ROW][C]R-squared[/C][C]0.4314[/C][/ROW]
[ROW][C]RMSE[/C][C]2.0666[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154584&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154584&T=1

As an alternative you can also use a QR Code:  

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

Goodness of Fit
Correlation0.6568
R-squared0.4314
RMSE2.0666







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
11314.780487804878-1.78048780487805
2811.2647058823529-3.26470588235294
31414.780487804878-0.780487804878049
41413.20454545454550.795454545454545
51313.2045454545455-0.204545454545455
61616.5185185185185-0.518518518518519
71413.20454545454550.795454545454545
81313.2045454545455-0.204545454545455
91511.26470588235293.73529411764706
101313.2045454545455-0.204545454545455
111614.7804878048781.21951219512195
122016.51851851851853.48148148148148
131714.7804878048782.21951219512195
141516.5185185185185-1.51851851851852
151614.7804878048781.21951219512195
161613.20454545454552.79545454545454
171213.2045454545455-1.20454545454546
18911.2647058823529-2.26470588235294
191513.20454545454551.79545454545454
201716.51851851851850.481481481481481
211214.780487804878-2.78048780487805
221013.2045454545455-3.20454545454546
231111.2647058823529-0.264705882352942
241614.7804878048781.21951219512195
251616.5185185185185-0.518518518518519
261513.20454545454551.79545454545454
271311.26470588235291.73529411764706
281414.780487804878-0.780487804878049
291916.51851851851852.48148148148148
301616.5185185185185-0.518518518518519
311716.51851851851850.481481481481481
321014.780487804878-4.78048780487805
331511.26470588235293.73529411764706
341413.20454545454550.795454545454545
351411.26470588235292.73529411764706
361614.7804878048781.21951219512195
371714.7804878048782.21951219512195
381514.7804878048780.219512195121951
391716.51851851851850.481481481481481
401416.5185185185185-2.51851851851852
411011.2647058823529-1.26470588235294
421411.26470588235292.73529411764706
431613.20454545454552.79545454545454
441816.51851851851851.48148148148148
451514.7804878048780.219512195121951
461616.5185185185185-0.518518518518519
471614.7804878048781.21951219512195
481011.2647058823529-1.26470588235294
49811.2647058823529-3.26470588235294
501716.51851851851850.481481481481481
511413.20454545454550.795454545454545
521213.2045454545455-1.20454545454546
531011.2647058823529-1.26470588235294
541414.780487804878-0.780487804878049
551213.2045454545455-1.20454545454546
561611.26470588235294.73529411764706
571614.7804878048781.21951219512195
581516.5185185185185-1.51851851851852
591111.2647058823529-0.264705882352942
601613.20454545454552.79545454545454
61811.2647058823529-3.26470588235294
621716.51851851851850.481481481481481
631614.7804878048781.21951219512195
641514.7804878048780.219512195121951
65811.2647058823529-3.26470588235294
661313.2045454545455-0.204545454545455
671413.20454545454550.795454545454545
681311.26470588235291.73529411764706
691614.7804878048781.21951219512195
701211.26470588235290.735294117647058
711916.51851851851852.48148148148148
721916.51851851851852.48148148148148
731211.26470588235290.735294117647058
741414.780487804878-0.780487804878049
751511.26470588235293.73529411764706
761316.5185185185185-3.51851851851852
771614.7804878048781.21951219512195
781013.2045454545455-3.20454545454546
791513.20454545454551.79545454545454
801614.7804878048781.21951219512195
811513.20454545454551.79545454545454
821113.2045454545455-2.20454545454546
83911.2647058823529-2.26470588235294
841614.7804878048781.21951219512195
851214.780487804878-2.78048780487805
861414.780487804878-0.780487804878049
871413.20454545454550.795454545454545
881313.2045454545455-0.204545454545455
891514.7804878048780.219512195121951
901714.7804878048782.21951219512195
911414.780487804878-0.780487804878049
92911.2647058823529-2.26470588235294
931113.2045454545455-2.20454545454546
94913.2045454545455-4.20454545454546
95711.2647058823529-4.26470588235294
961313.2045454545455-0.204545454545455
971513.20454545454551.79545454545454
981214.780487804878-2.78048780487805
991513.20454545454551.79545454545454
1001411.26470588235292.73529411764706
1011516.5185185185185-1.51851851851852
102911.2647058823529-2.26470588235294
1031614.7804878048781.21951219512195
1041616.5185185185185-0.518518518518519
1051413.20454545454550.795454545454545
1061416.5185185185185-2.51851851851852
1071313.2045454545455-0.204545454545455
1081413.20454545454550.795454545454545
1091614.7804878048781.21951219512195
1101616.5185185185185-0.518518518518519
1111313.2045454545455-0.204545454545455
1121211.26470588235290.735294117647058
1131614.7804878048781.21951219512195
1141613.20454545454552.79545454545454
1151614.7804878048781.21951219512195
1161011.2647058823529-1.26470588235294
1171416.5185185185185-2.51851851851852
1181213.2045454545455-1.20454545454546
1191214.780487804878-2.78048780487805
1201211.26470588235290.735294117647058
1211211.26470588235290.735294117647058
1221916.51851851851852.48148148148148
1231413.20454545454550.795454545454545
1241313.2045454545455-0.204545454545455
1251716.51851851851850.481481481481481
1261614.7804878048781.21951219512195
1271514.7804878048780.219512195121951
1281214.780487804878-2.78048780487805
129813.2045454545455-5.20454545454546
1301011.2647058823529-1.26470588235294
1311613.20454545454552.79545454545454
1321011.2647058823529-1.26470588235294
1331614.7804878048781.21951219512195
1341014.780487804878-4.78048780487805
1351816.51851851851851.48148148148148
1361211.26470588235290.735294117647058
1371616.5185185185185-0.518518518518519
1381011.2647058823529-1.26470588235294
1391511.26470588235293.73529411764706
1401713.20454545454553.79545454545454
1411614.7804878048781.21951219512195
1421413.20454545454550.795454545454545
1431213.2045454545455-1.20454545454546
1441113.2045454545455-2.20454545454546
1451514.7804878048780.219512195121951
146713.2045454545455-6.20454545454546

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 13 & 14.780487804878 & -1.78048780487805 \tabularnewline
2 & 8 & 11.2647058823529 & -3.26470588235294 \tabularnewline
3 & 14 & 14.780487804878 & -0.780487804878049 \tabularnewline
4 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
5 & 13 & 13.2045454545455 & -0.204545454545455 \tabularnewline
6 & 16 & 16.5185185185185 & -0.518518518518519 \tabularnewline
7 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
8 & 13 & 13.2045454545455 & -0.204545454545455 \tabularnewline
9 & 15 & 11.2647058823529 & 3.73529411764706 \tabularnewline
10 & 13 & 13.2045454545455 & -0.204545454545455 \tabularnewline
11 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
12 & 20 & 16.5185185185185 & 3.48148148148148 \tabularnewline
13 & 17 & 14.780487804878 & 2.21951219512195 \tabularnewline
14 & 15 & 16.5185185185185 & -1.51851851851852 \tabularnewline
15 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
16 & 16 & 13.2045454545455 & 2.79545454545454 \tabularnewline
17 & 12 & 13.2045454545455 & -1.20454545454546 \tabularnewline
18 & 9 & 11.2647058823529 & -2.26470588235294 \tabularnewline
19 & 15 & 13.2045454545455 & 1.79545454545454 \tabularnewline
20 & 17 & 16.5185185185185 & 0.481481481481481 \tabularnewline
21 & 12 & 14.780487804878 & -2.78048780487805 \tabularnewline
22 & 10 & 13.2045454545455 & -3.20454545454546 \tabularnewline
23 & 11 & 11.2647058823529 & -0.264705882352942 \tabularnewline
24 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
25 & 16 & 16.5185185185185 & -0.518518518518519 \tabularnewline
26 & 15 & 13.2045454545455 & 1.79545454545454 \tabularnewline
27 & 13 & 11.2647058823529 & 1.73529411764706 \tabularnewline
28 & 14 & 14.780487804878 & -0.780487804878049 \tabularnewline
29 & 19 & 16.5185185185185 & 2.48148148148148 \tabularnewline
30 & 16 & 16.5185185185185 & -0.518518518518519 \tabularnewline
31 & 17 & 16.5185185185185 & 0.481481481481481 \tabularnewline
32 & 10 & 14.780487804878 & -4.78048780487805 \tabularnewline
33 & 15 & 11.2647058823529 & 3.73529411764706 \tabularnewline
34 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
35 & 14 & 11.2647058823529 & 2.73529411764706 \tabularnewline
36 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
37 & 17 & 14.780487804878 & 2.21951219512195 \tabularnewline
38 & 15 & 14.780487804878 & 0.219512195121951 \tabularnewline
39 & 17 & 16.5185185185185 & 0.481481481481481 \tabularnewline
40 & 14 & 16.5185185185185 & -2.51851851851852 \tabularnewline
41 & 10 & 11.2647058823529 & -1.26470588235294 \tabularnewline
42 & 14 & 11.2647058823529 & 2.73529411764706 \tabularnewline
43 & 16 & 13.2045454545455 & 2.79545454545454 \tabularnewline
44 & 18 & 16.5185185185185 & 1.48148148148148 \tabularnewline
45 & 15 & 14.780487804878 & 0.219512195121951 \tabularnewline
46 & 16 & 16.5185185185185 & -0.518518518518519 \tabularnewline
47 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
48 & 10 & 11.2647058823529 & -1.26470588235294 \tabularnewline
49 & 8 & 11.2647058823529 & -3.26470588235294 \tabularnewline
50 & 17 & 16.5185185185185 & 0.481481481481481 \tabularnewline
51 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
52 & 12 & 13.2045454545455 & -1.20454545454546 \tabularnewline
53 & 10 & 11.2647058823529 & -1.26470588235294 \tabularnewline
54 & 14 & 14.780487804878 & -0.780487804878049 \tabularnewline
55 & 12 & 13.2045454545455 & -1.20454545454546 \tabularnewline
56 & 16 & 11.2647058823529 & 4.73529411764706 \tabularnewline
57 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
58 & 15 & 16.5185185185185 & -1.51851851851852 \tabularnewline
59 & 11 & 11.2647058823529 & -0.264705882352942 \tabularnewline
60 & 16 & 13.2045454545455 & 2.79545454545454 \tabularnewline
61 & 8 & 11.2647058823529 & -3.26470588235294 \tabularnewline
62 & 17 & 16.5185185185185 & 0.481481481481481 \tabularnewline
63 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
64 & 15 & 14.780487804878 & 0.219512195121951 \tabularnewline
65 & 8 & 11.2647058823529 & -3.26470588235294 \tabularnewline
66 & 13 & 13.2045454545455 & -0.204545454545455 \tabularnewline
67 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
68 & 13 & 11.2647058823529 & 1.73529411764706 \tabularnewline
69 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
70 & 12 & 11.2647058823529 & 0.735294117647058 \tabularnewline
71 & 19 & 16.5185185185185 & 2.48148148148148 \tabularnewline
72 & 19 & 16.5185185185185 & 2.48148148148148 \tabularnewline
73 & 12 & 11.2647058823529 & 0.735294117647058 \tabularnewline
74 & 14 & 14.780487804878 & -0.780487804878049 \tabularnewline
75 & 15 & 11.2647058823529 & 3.73529411764706 \tabularnewline
76 & 13 & 16.5185185185185 & -3.51851851851852 \tabularnewline
77 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
78 & 10 & 13.2045454545455 & -3.20454545454546 \tabularnewline
79 & 15 & 13.2045454545455 & 1.79545454545454 \tabularnewline
80 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
81 & 15 & 13.2045454545455 & 1.79545454545454 \tabularnewline
82 & 11 & 13.2045454545455 & -2.20454545454546 \tabularnewline
83 & 9 & 11.2647058823529 & -2.26470588235294 \tabularnewline
84 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
85 & 12 & 14.780487804878 & -2.78048780487805 \tabularnewline
86 & 14 & 14.780487804878 & -0.780487804878049 \tabularnewline
87 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
88 & 13 & 13.2045454545455 & -0.204545454545455 \tabularnewline
89 & 15 & 14.780487804878 & 0.219512195121951 \tabularnewline
90 & 17 & 14.780487804878 & 2.21951219512195 \tabularnewline
91 & 14 & 14.780487804878 & -0.780487804878049 \tabularnewline
92 & 9 & 11.2647058823529 & -2.26470588235294 \tabularnewline
93 & 11 & 13.2045454545455 & -2.20454545454546 \tabularnewline
94 & 9 & 13.2045454545455 & -4.20454545454546 \tabularnewline
95 & 7 & 11.2647058823529 & -4.26470588235294 \tabularnewline
96 & 13 & 13.2045454545455 & -0.204545454545455 \tabularnewline
97 & 15 & 13.2045454545455 & 1.79545454545454 \tabularnewline
98 & 12 & 14.780487804878 & -2.78048780487805 \tabularnewline
99 & 15 & 13.2045454545455 & 1.79545454545454 \tabularnewline
100 & 14 & 11.2647058823529 & 2.73529411764706 \tabularnewline
101 & 15 & 16.5185185185185 & -1.51851851851852 \tabularnewline
102 & 9 & 11.2647058823529 & -2.26470588235294 \tabularnewline
103 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
104 & 16 & 16.5185185185185 & -0.518518518518519 \tabularnewline
105 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
106 & 14 & 16.5185185185185 & -2.51851851851852 \tabularnewline
107 & 13 & 13.2045454545455 & -0.204545454545455 \tabularnewline
108 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
109 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
110 & 16 & 16.5185185185185 & -0.518518518518519 \tabularnewline
111 & 13 & 13.2045454545455 & -0.204545454545455 \tabularnewline
112 & 12 & 11.2647058823529 & 0.735294117647058 \tabularnewline
113 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
114 & 16 & 13.2045454545455 & 2.79545454545454 \tabularnewline
115 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
116 & 10 & 11.2647058823529 & -1.26470588235294 \tabularnewline
117 & 14 & 16.5185185185185 & -2.51851851851852 \tabularnewline
118 & 12 & 13.2045454545455 & -1.20454545454546 \tabularnewline
119 & 12 & 14.780487804878 & -2.78048780487805 \tabularnewline
120 & 12 & 11.2647058823529 & 0.735294117647058 \tabularnewline
121 & 12 & 11.2647058823529 & 0.735294117647058 \tabularnewline
122 & 19 & 16.5185185185185 & 2.48148148148148 \tabularnewline
123 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
124 & 13 & 13.2045454545455 & -0.204545454545455 \tabularnewline
125 & 17 & 16.5185185185185 & 0.481481481481481 \tabularnewline
126 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
127 & 15 & 14.780487804878 & 0.219512195121951 \tabularnewline
128 & 12 & 14.780487804878 & -2.78048780487805 \tabularnewline
129 & 8 & 13.2045454545455 & -5.20454545454546 \tabularnewline
130 & 10 & 11.2647058823529 & -1.26470588235294 \tabularnewline
131 & 16 & 13.2045454545455 & 2.79545454545454 \tabularnewline
132 & 10 & 11.2647058823529 & -1.26470588235294 \tabularnewline
133 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
134 & 10 & 14.780487804878 & -4.78048780487805 \tabularnewline
135 & 18 & 16.5185185185185 & 1.48148148148148 \tabularnewline
136 & 12 & 11.2647058823529 & 0.735294117647058 \tabularnewline
137 & 16 & 16.5185185185185 & -0.518518518518519 \tabularnewline
138 & 10 & 11.2647058823529 & -1.26470588235294 \tabularnewline
139 & 15 & 11.2647058823529 & 3.73529411764706 \tabularnewline
140 & 17 & 13.2045454545455 & 3.79545454545454 \tabularnewline
141 & 16 & 14.780487804878 & 1.21951219512195 \tabularnewline
142 & 14 & 13.2045454545455 & 0.795454545454545 \tabularnewline
143 & 12 & 13.2045454545455 & -1.20454545454546 \tabularnewline
144 & 11 & 13.2045454545455 & -2.20454545454546 \tabularnewline
145 & 15 & 14.780487804878 & 0.219512195121951 \tabularnewline
146 & 7 & 13.2045454545455 & -6.20454545454546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154584&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]14.780487804878[/C][C]-1.78048780487805[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]11.2647058823529[/C][C]-3.26470588235294[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]14.780487804878[/C][C]-0.780487804878049[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]13.2045454545455[/C][C]-0.204545454545455[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]16.5185185185185[/C][C]-0.518518518518519[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]13.2045454545455[/C][C]-0.204545454545455[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]11.2647058823529[/C][C]3.73529411764706[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]13.2045454545455[/C][C]-0.204545454545455[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]12[/C][C]20[/C][C]16.5185185185185[/C][C]3.48148148148148[/C][/ROW]
[ROW][C]13[/C][C]17[/C][C]14.780487804878[/C][C]2.21951219512195[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]16.5185185185185[/C][C]-1.51851851851852[/C][/ROW]
[ROW][C]15[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]13.2045454545455[/C][C]2.79545454545454[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]13.2045454545455[/C][C]-1.20454545454546[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]11.2647058823529[/C][C]-2.26470588235294[/C][/ROW]
[ROW][C]19[/C][C]15[/C][C]13.2045454545455[/C][C]1.79545454545454[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]16.5185185185185[/C][C]0.481481481481481[/C][/ROW]
[ROW][C]21[/C][C]12[/C][C]14.780487804878[/C][C]-2.78048780487805[/C][/ROW]
[ROW][C]22[/C][C]10[/C][C]13.2045454545455[/C][C]-3.20454545454546[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.2647058823529[/C][C]-0.264705882352942[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]25[/C][C]16[/C][C]16.5185185185185[/C][C]-0.518518518518519[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]13.2045454545455[/C][C]1.79545454545454[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]11.2647058823529[/C][C]1.73529411764706[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]14.780487804878[/C][C]-0.780487804878049[/C][/ROW]
[ROW][C]29[/C][C]19[/C][C]16.5185185185185[/C][C]2.48148148148148[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]16.5185185185185[/C][C]-0.518518518518519[/C][/ROW]
[ROW][C]31[/C][C]17[/C][C]16.5185185185185[/C][C]0.481481481481481[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]14.780487804878[/C][C]-4.78048780487805[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]11.2647058823529[/C][C]3.73529411764706[/C][/ROW]
[ROW][C]34[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]11.2647058823529[/C][C]2.73529411764706[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]37[/C][C]17[/C][C]14.780487804878[/C][C]2.21951219512195[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.780487804878[/C][C]0.219512195121951[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]16.5185185185185[/C][C]0.481481481481481[/C][/ROW]
[ROW][C]40[/C][C]14[/C][C]16.5185185185185[/C][C]-2.51851851851852[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.2647058823529[/C][C]-1.26470588235294[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.2647058823529[/C][C]2.73529411764706[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]13.2045454545455[/C][C]2.79545454545454[/C][/ROW]
[ROW][C]44[/C][C]18[/C][C]16.5185185185185[/C][C]1.48148148148148[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]14.780487804878[/C][C]0.219512195121951[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]16.5185185185185[/C][C]-0.518518518518519[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]48[/C][C]10[/C][C]11.2647058823529[/C][C]-1.26470588235294[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]11.2647058823529[/C][C]-3.26470588235294[/C][/ROW]
[ROW][C]50[/C][C]17[/C][C]16.5185185185185[/C][C]0.481481481481481[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]13.2045454545455[/C][C]-1.20454545454546[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]11.2647058823529[/C][C]-1.26470588235294[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.780487804878[/C][C]-0.780487804878049[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.2045454545455[/C][C]-1.20454545454546[/C][/ROW]
[ROW][C]56[/C][C]16[/C][C]11.2647058823529[/C][C]4.73529411764706[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]16.5185185185185[/C][C]-1.51851851851852[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.2647058823529[/C][C]-0.264705882352942[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]13.2045454545455[/C][C]2.79545454545454[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]11.2647058823529[/C][C]-3.26470588235294[/C][/ROW]
[ROW][C]62[/C][C]17[/C][C]16.5185185185185[/C][C]0.481481481481481[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]14.780487804878[/C][C]0.219512195121951[/C][/ROW]
[ROW][C]65[/C][C]8[/C][C]11.2647058823529[/C][C]-3.26470588235294[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]13.2045454545455[/C][C]-0.204545454545455[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.2647058823529[/C][C]1.73529411764706[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.2647058823529[/C][C]0.735294117647058[/C][/ROW]
[ROW][C]71[/C][C]19[/C][C]16.5185185185185[/C][C]2.48148148148148[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]16.5185185185185[/C][C]2.48148148148148[/C][/ROW]
[ROW][C]73[/C][C]12[/C][C]11.2647058823529[/C][C]0.735294117647058[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]14.780487804878[/C][C]-0.780487804878049[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]11.2647058823529[/C][C]3.73529411764706[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]16.5185185185185[/C][C]-3.51851851851852[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]78[/C][C]10[/C][C]13.2045454545455[/C][C]-3.20454545454546[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.2045454545455[/C][C]1.79545454545454[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]13.2045454545455[/C][C]1.79545454545454[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]13.2045454545455[/C][C]-2.20454545454546[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]11.2647058823529[/C][C]-2.26470588235294[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]14.780487804878[/C][C]-2.78048780487805[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.780487804878[/C][C]-0.780487804878049[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]13.2045454545455[/C][C]-0.204545454545455[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.780487804878[/C][C]0.219512195121951[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]14.780487804878[/C][C]2.21951219512195[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]14.780487804878[/C][C]-0.780487804878049[/C][/ROW]
[ROW][C]92[/C][C]9[/C][C]11.2647058823529[/C][C]-2.26470588235294[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]13.2045454545455[/C][C]-2.20454545454546[/C][/ROW]
[ROW][C]94[/C][C]9[/C][C]13.2045454545455[/C][C]-4.20454545454546[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]11.2647058823529[/C][C]-4.26470588235294[/C][/ROW]
[ROW][C]96[/C][C]13[/C][C]13.2045454545455[/C][C]-0.204545454545455[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]13.2045454545455[/C][C]1.79545454545454[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]14.780487804878[/C][C]-2.78048780487805[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]13.2045454545455[/C][C]1.79545454545454[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]11.2647058823529[/C][C]2.73529411764706[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]16.5185185185185[/C][C]-1.51851851851852[/C][/ROW]
[ROW][C]102[/C][C]9[/C][C]11.2647058823529[/C][C]-2.26470588235294[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]16.5185185185185[/C][C]-0.518518518518519[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]16.5185185185185[/C][C]-2.51851851851852[/C][/ROW]
[ROW][C]107[/C][C]13[/C][C]13.2045454545455[/C][C]-0.204545454545455[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]16.5185185185185[/C][C]-0.518518518518519[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]13.2045454545455[/C][C]-0.204545454545455[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]11.2647058823529[/C][C]0.735294117647058[/C][/ROW]
[ROW][C]113[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]114[/C][C]16[/C][C]13.2045454545455[/C][C]2.79545454545454[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]11.2647058823529[/C][C]-1.26470588235294[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]16.5185185185185[/C][C]-2.51851851851852[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]13.2045454545455[/C][C]-1.20454545454546[/C][/ROW]
[ROW][C]119[/C][C]12[/C][C]14.780487804878[/C][C]-2.78048780487805[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.2647058823529[/C][C]0.735294117647058[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.2647058823529[/C][C]0.735294117647058[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]16.5185185185185[/C][C]2.48148148148148[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.2045454545455[/C][C]-0.204545454545455[/C][/ROW]
[ROW][C]125[/C][C]17[/C][C]16.5185185185185[/C][C]0.481481481481481[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]14.780487804878[/C][C]0.219512195121951[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]14.780487804878[/C][C]-2.78048780487805[/C][/ROW]
[ROW][C]129[/C][C]8[/C][C]13.2045454545455[/C][C]-5.20454545454546[/C][/ROW]
[ROW][C]130[/C][C]10[/C][C]11.2647058823529[/C][C]-1.26470588235294[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]13.2045454545455[/C][C]2.79545454545454[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]11.2647058823529[/C][C]-1.26470588235294[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]134[/C][C]10[/C][C]14.780487804878[/C][C]-4.78048780487805[/C][/ROW]
[ROW][C]135[/C][C]18[/C][C]16.5185185185185[/C][C]1.48148148148148[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]11.2647058823529[/C][C]0.735294117647058[/C][/ROW]
[ROW][C]137[/C][C]16[/C][C]16.5185185185185[/C][C]-0.518518518518519[/C][/ROW]
[ROW][C]138[/C][C]10[/C][C]11.2647058823529[/C][C]-1.26470588235294[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]11.2647058823529[/C][C]3.73529411764706[/C][/ROW]
[ROW][C]140[/C][C]17[/C][C]13.2045454545455[/C][C]3.79545454545454[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]14.780487804878[/C][C]1.21951219512195[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]13.2045454545455[/C][C]0.795454545454545[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]13.2045454545455[/C][C]-1.20454545454546[/C][/ROW]
[ROW][C]144[/C][C]11[/C][C]13.2045454545455[/C][C]-2.20454545454546[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]14.780487804878[/C][C]0.219512195121951[/C][/ROW]
[ROW][C]146[/C][C]7[/C][C]13.2045454545455[/C][C]-6.20454545454546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154584&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154584&T=2

As an alternative you can also use a QR Code:  

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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
11314.780487804878-1.78048780487805
2811.2647058823529-3.26470588235294
31414.780487804878-0.780487804878049
41413.20454545454550.795454545454545
51313.2045454545455-0.204545454545455
61616.5185185185185-0.518518518518519
71413.20454545454550.795454545454545
81313.2045454545455-0.204545454545455
91511.26470588235293.73529411764706
101313.2045454545455-0.204545454545455
111614.7804878048781.21951219512195
122016.51851851851853.48148148148148
131714.7804878048782.21951219512195
141516.5185185185185-1.51851851851852
151614.7804878048781.21951219512195
161613.20454545454552.79545454545454
171213.2045454545455-1.20454545454546
18911.2647058823529-2.26470588235294
191513.20454545454551.79545454545454
201716.51851851851850.481481481481481
211214.780487804878-2.78048780487805
221013.2045454545455-3.20454545454546
231111.2647058823529-0.264705882352942
241614.7804878048781.21951219512195
251616.5185185185185-0.518518518518519
261513.20454545454551.79545454545454
271311.26470588235291.73529411764706
281414.780487804878-0.780487804878049
291916.51851851851852.48148148148148
301616.5185185185185-0.518518518518519
311716.51851851851850.481481481481481
321014.780487804878-4.78048780487805
331511.26470588235293.73529411764706
341413.20454545454550.795454545454545
351411.26470588235292.73529411764706
361614.7804878048781.21951219512195
371714.7804878048782.21951219512195
381514.7804878048780.219512195121951
391716.51851851851850.481481481481481
401416.5185185185185-2.51851851851852
411011.2647058823529-1.26470588235294
421411.26470588235292.73529411764706
431613.20454545454552.79545454545454
441816.51851851851851.48148148148148
451514.7804878048780.219512195121951
461616.5185185185185-0.518518518518519
471614.7804878048781.21951219512195
481011.2647058823529-1.26470588235294
49811.2647058823529-3.26470588235294
501716.51851851851850.481481481481481
511413.20454545454550.795454545454545
521213.2045454545455-1.20454545454546
531011.2647058823529-1.26470588235294
541414.780487804878-0.780487804878049
551213.2045454545455-1.20454545454546
561611.26470588235294.73529411764706
571614.7804878048781.21951219512195
581516.5185185185185-1.51851851851852
591111.2647058823529-0.264705882352942
601613.20454545454552.79545454545454
61811.2647058823529-3.26470588235294
621716.51851851851850.481481481481481
631614.7804878048781.21951219512195
641514.7804878048780.219512195121951
65811.2647058823529-3.26470588235294
661313.2045454545455-0.204545454545455
671413.20454545454550.795454545454545
681311.26470588235291.73529411764706
691614.7804878048781.21951219512195
701211.26470588235290.735294117647058
711916.51851851851852.48148148148148
721916.51851851851852.48148148148148
731211.26470588235290.735294117647058
741414.780487804878-0.780487804878049
751511.26470588235293.73529411764706
761316.5185185185185-3.51851851851852
771614.7804878048781.21951219512195
781013.2045454545455-3.20454545454546
791513.20454545454551.79545454545454
801614.7804878048781.21951219512195
811513.20454545454551.79545454545454
821113.2045454545455-2.20454545454546
83911.2647058823529-2.26470588235294
841614.7804878048781.21951219512195
851214.780487804878-2.78048780487805
861414.780487804878-0.780487804878049
871413.20454545454550.795454545454545
881313.2045454545455-0.204545454545455
891514.7804878048780.219512195121951
901714.7804878048782.21951219512195
911414.780487804878-0.780487804878049
92911.2647058823529-2.26470588235294
931113.2045454545455-2.20454545454546
94913.2045454545455-4.20454545454546
95711.2647058823529-4.26470588235294
961313.2045454545455-0.204545454545455
971513.20454545454551.79545454545454
981214.780487804878-2.78048780487805
991513.20454545454551.79545454545454
1001411.26470588235292.73529411764706
1011516.5185185185185-1.51851851851852
102911.2647058823529-2.26470588235294
1031614.7804878048781.21951219512195
1041616.5185185185185-0.518518518518519
1051413.20454545454550.795454545454545
1061416.5185185185185-2.51851851851852
1071313.2045454545455-0.204545454545455
1081413.20454545454550.795454545454545
1091614.7804878048781.21951219512195
1101616.5185185185185-0.518518518518519
1111313.2045454545455-0.204545454545455
1121211.26470588235290.735294117647058
1131614.7804878048781.21951219512195
1141613.20454545454552.79545454545454
1151614.7804878048781.21951219512195
1161011.2647058823529-1.26470588235294
1171416.5185185185185-2.51851851851852
1181213.2045454545455-1.20454545454546
1191214.780487804878-2.78048780487805
1201211.26470588235290.735294117647058
1211211.26470588235290.735294117647058
1221916.51851851851852.48148148148148
1231413.20454545454550.795454545454545
1241313.2045454545455-0.204545454545455
1251716.51851851851850.481481481481481
1261614.7804878048781.21951219512195
1271514.7804878048780.219512195121951
1281214.780487804878-2.78048780487805
129813.2045454545455-5.20454545454546
1301011.2647058823529-1.26470588235294
1311613.20454545454552.79545454545454
1321011.2647058823529-1.26470588235294
1331614.7804878048781.21951219512195
1341014.780487804878-4.78048780487805
1351816.51851851851851.48148148148148
1361211.26470588235290.735294117647058
1371616.5185185185185-0.518518518518519
1381011.2647058823529-1.26470588235294
1391511.26470588235293.73529411764706
1401713.20454545454553.79545454545454
1411614.7804878048781.21951219512195
1421413.20454545454550.795454545454545
1431213.2045454545455-1.20454545454546
1441113.2045454545455-2.20454545454546
1451514.7804878048780.219512195121951
146713.2045454545455-6.20454545454546



Parameters (Session):
par1 = 2 ; par2 = none ; par3 = 4 ; par4 = no ;
Parameters (R input):
par1 = 2 ; par2 = none ; par3 = 4 ; par4 = no ;
R code (references can be found in the software module):
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}