wie gesagt möchte ich reales Gewicht mit einem berechneten vergleichen um eine Aussage über die Genauigkeit des Schätzwerts machen zu können, mit folgenden Daten:
Code: Alles auswählen
treatment variety plant truss weight estimation
1 LOC 2 D 1 47.1 34.7
2 LOC 2 D 1 41.2 35.2
3 LOC 2 D 1 35.0 27.9
4 LOC 2 D 1 43.5 37.2
5 LOC 2 D 1 31.3 29.2
6 LOC 2 D 1 18.3 15.1
7 LOC 2 D 1 5.7 5.2
8 LOC 2 D 1 5.4 4.1
9 LOC 2 D 2 24.9 20.5
10 LOC 2 D 2 24.4 18.9
11 LOC 2 D 2 17.7 16.1
12 LOC 2 D 2 7.9 4.6
13 LOC 2 D 2 8.3 5.7
14 LOC 2 D 2 6.2 5.4
15 LOC 2 D 2 6.8 6.8
16 LOC 2 D 2 6.2 5.2
17 LOC 2 D 3 11.2 9.1
18 LOC 2 D 3 6.6 6.9
19 LOC 2 D 3 6.3 6.1
20 LOC 2 D 3 6.2 6.0
21 LOC 2 D 4 11.2 9.7
22 LOC 2 D 4 13.7 13.1
23 LOC 2 D 4 7.6 8.5
24 NOP 3 N 1 78.4 60.4
25 NOP 3 N 1 34.3 25.2
26 NOP 3 N 1 31.2 31.1
27 NOP 3 N 1 31.1 28.2
28 NOP 3 N 1 30.7 25.5
29 NOP 3 N 1 29.0 25.2
30 NOP 3 N 1 26.2 18.8
31 NOP 3 N 1 25.1 23.3
32 NOP 3 N 1 21.6 19.6
33 NOP 3 N 1 23.1 16.3
34 NOP 3 N 1 15.8 12.9
35 NOP 3 N 1 12.2 10.7
36 NOP 3 N 2 33.2 24.5
37 NOP 3 N 2 33.5 22.7
38 NOP 3 N 2 33.3 33.2
39 NOP 3 N 2 12.9 12.2
40 NOP 3 N 2 12.9 14.2
41 NOP 3 N 2 10.8 8.5
42 NOP 3 N 2 12.5 11.1
43 NOP 3 N 2 6.8 6.3
44 NOP 3 N 3 8.0 9.9
45 NOP 3 N 3 5.5 4.1
46 NOP 2 E 1 58.7 43.4
47 NOP 2 E 1 52.6 41.6
48 NOP 2 E 1 39.0 33.6
49 NOP 2 E 1 42.1 37.7
50 NOP 2 E 1 41.1 35.3
51 NOP 2 E 1 27.6 25.3
52 NOP 2 E 1 25.6 20.4
53 NOP 2 E 2 32.5 28.7
54 NOP 2 E 2 24.8 22.0
55 NOP 2 E 2 17.4 16.3
56 NOP 2 E 2 10.9 10.3
57 NOP 2 E 2 11.4 10.0
58 NOP 2 E 2 7.2 8.7
59 NOP 2 E 3 29.1 25.4
60 NOP 2 E 3 16.0 12.9
61 NOP 2 E 3 8.9 9.9
62 NOP 2 E 3 7.5 8.7
63 LOC 3 K 1 86.8 68.0
64 LOC 3 K 1 50.9 43.2
65 LOC 3 K 1 51.5 43.9
66 LOC 3 K 1 48.6 48.2
67 LOC 3 K 1 36.9 32.7
68 LOC 3 K 1 23.8 21.0
69 LOC 3 K 2 41.5 46.3
70 LOC 3 K 2 36.9 31.8
71 LOC 3 K 2 29.0 26.8
72 LOC 3 K 2 12.9 11.0
73 LOC 3 K 2 13.4 13.8
74 LOC 3 K 2 10.6 8.0
75 LOC 3 K 2 8.0 10.8
76 LOC 3 K 2 9.2 10.7
77 LOC 3 K 3 8.6 4.6
78 LOC 3 K 3 10.2 7.1
79 LOC 3 K 3 5.8 5.5
80 LOC 3 K 3 6.8 6.9
81 LOC 3 K 3 5.8 5.3
82 LOC 3 K 4 6.0 5.5
83 CLE 2 R 1 50.9 40.6
84 CLE 2 R 1 39.1 36.4
85 CLE 2 R 1 30.8 26.2
86 CLE 2 R 1 27.3 22.4
87 CLE 2 R 1 26.1 22.3
88 CLE 2 R 1 18.9 15.2
89 CLE 2 R 1 19.0 14.8
90 CLE 2 R 1 17.3 14.7
91 CLE 2 R 1 14.8 16.9
92 CLE 2 R 1 10.5 8.4
93 CLE 2 R 1 9.9 6.6
94 CLE 2 R 1 8.6 12.4
95 CLE 2 R 1 9.1 11.9
96 CLE 2 R 1 8.9 13.2
97 CLE 2 R 1 9.4 11.5
98 CLE 2 R 1 7.2 6.6
99 CLE 2 R 1 6.8 6.6
100 CLE 2 R 1 6.7 7.1
101 CLE 2 R 1 6.2 6.0
102 CLE 2 R 2 39.2 34.4
103 CLE 2 R 2 12.0 9.0
104 CLE 2 R 2 9.9 7.4
105 CLE 2 R 2 7.8 9.9
106 CLE 2 R 2 9.5 14.1
107 CLE 2 R 2 6.6 6.6
108 CLE 2 R 3 14.6 16.2
109 CLE 2 R 3 12.0 10.2
110 CLE 2 R 3 8.7 10.6
111 CLE 2 R 3 8.1 9.8
112 CLE 2 R 3 8.2 10.4
113 CLE 2 R 3 7.1 7.9
114 NOP 1 M 1 43.9 34.2
115 NOP 1 M 1 33.7 35.3
116 NOP 1 M 1 40.6 40.9
117 NOP 1 M 1 37.9 32.1
118 NOP 1 M 1 39.9 41.3
119 NOP 1 M 1 25.6 22.2
120 NOP 1 M 2 45.2 38.8
121 NOP 1 M 2 30.9 28.5
122 NOP 1 M 2 22.9 23.5
123 NOP 1 M 2 23.3 23.3
124 NOP 1 M 2 22.9 23.5
125 NOP 1 M 2 18.3 14.0
126 NOP 1 M 2 18.2 18.2
127 NOP 1 M 2 12.4 22.7
128 NOP 1 M 3 21.0 16.9
129 NOP 1 M 3 16.6 14.1
130 NOP 1 M 3 10.5 7.9
131 NOP 1 M 3 13.1 12.0
132 NOP 1 M 3 6.9 6.6
133 NOP 1 M 3 7.8 8.6
134 NOP 1 M 3 6.3 6.3
135 NOP 1 M 4 21.3 16.1
136 NOP 1 M 4 19.4 20.8
137 DIF 2 Q 1 48.2 37.4
138 DIF 2 Q 1 50.1 43.3
139 DIF 2 Q 1 51.7 44.0
140 DIF 2 Q 1 47.0 39.4
141 DIF 2 Q 1 31.9 27.3
142 DIF 2 Q 1 25.4 26.9
143 DIF 2 Q 1 24.6 23.1
144 DIF 2 Q 1 8.9 13.2
145 DIF 2 Q 2 49.7 38.3
146 DIF 2 Q 2 37.5 32.4
147 DIF 2 Q 2 30.3 26.0
148 DIF 2 Q 2 26.4 23.7
149 DIF 2 Q 2 16.6 14.6
150 DIF 2 Q 2 14.7 12.0
151 DIF 2 Q 3 58.0 48.8
152 DIF 2 Q 3 28.4 22.7
153 DIF 2 Q 3 26.5 19.9
154 DIF 2 Q 3 10.7 8.1
155 DIF 2 Q 3 7.2 6.0
156 DIF 2 Q 4 15.7 12.0
157 CLE 1 R 1 75.6 63.3
158 CLE 1 R 1 43.6 39.8
159 CLE 1 R 1 43.4 38.1
160 CLE 1 R 1 45.2 37.9
161 CLE 1 R 1 41.6 34.8
162 CLE 1 R 1 36.5 28.9
163 CLE 1 R 1 26.7 21.9
164 CLE 1 R 1 20.1 14.5
165 CLE 1 R 2 26.5 22.3
166 CLE 1 R 2 26.9 20.8
Wie folgt habe ich versucht es darzustellen:
Code: Alles auswählen
ggplot(d, aes(x=d$estimation, y=d$weight))+
geom_point(shape=19, alpha=1/4)+
geom_smooth(method=lm,se=FALSE)
Beziehungsweise habt ihr eine Idee welcher plot der geeignete sein könnte?