Cada punto de los datos, MR i , es el rango móvil de los valores de X en cada grupo. El MR i no se grafica para i < w porque es indefinido.
Término | Description |
---|---|
MR | rango móvil |
w | Número de observaciones en el rango móvil. Por opción predeterminada, w = 2. |
La línea central es la estimación sin sesgo del promedio de rango móvil.
Cuando se utiliza el rango móvil promedio para estimar la desviación estándar, la línea central = MR
Cuando se utiliza el rango móvil de la mediana para estimar la desviación estándar, la línea central = d2(w) * MR.
El LCI es el mayor de los siguientes:
o
Término | Description |
---|---|
d2() | una constante utilizada para estimar la desviación estándar |
w | número de observaciones en el rango móvil. Por opción predeterminada, w = 2. |
σ | desviación estándar del proceso |
k | parámetro de la Prueba 1 (el valor predeterminado es 3) |
d3() | Una constante utilizada para estimar el LCI y el LCS. |
El promedio de rango móvil, , de longitud w viene dado por la siguiente fórmula:
donde MRi es el rango móvil de la observación i, que se calcula de la siguiente manera:
Minitab utiliza para calcular Smr, que es una estimación sin sesgo de σ:
Término | Description |
---|---|
n | número de observaciones |
w | longitud del rango móvil. El valor predeterminado es 2. |
d2() | valor de la constante de eliminación de sesgo d2, que corresponde al valor especificado entre paréntesis. |
La mediana de rango móvil, , de longitud w viene dada por la siguiente fórmula:
donde MRi es el rango móvil de la observación i, que se calcula de la siguiente manera:
Minitab utiliza para calcular Smr, que es una estimación sin sesgo de σ:
Término | Description |
---|---|
n | número de observaciones |
w | longitud del rango móvil. El valor predeterminado es 2. |
d4() | valor de la constante de eliminación de sesgo d4, que corresponde al valor especificado entre paréntesis. |
d2(N) es el valor esperado del rango de N observaciones de una población normal con desviación estándar = 1. Por lo tanto, si r es el rango de una muestra de N observaciones de una distribución normal con desviación estándar = σ, entonces E(r) = d2(N)σ.
d3(N) es la desviación estándar del rango de N observaciones de una población normal con σ = 1. Por lo tanto, si r es el rango de una muestra de N observaciones de una distribución normal con una desviación estándar = σ, entonces desv.est.(r) = d3(N)σ.
Utilice la siguiente tabla para hallar una constante de eliminación de sesgo para un valor dado, N. (Para determinar el valor de N, consulte la fórmula del estadístico de interés.)
N | d2(N) | d3(N) | d4(N) |
---|---|---|---|
2 | 1.128 | 0.8525 | 0.954 |
3 | 1.693 | 0.8884 | 1.588 |
4 | 2.059 | 0.8798 | 1.978 |
5 | 2.326 | 0.8641 | 2.257 |
6 | 2.534 | 0.8480 | 2.472 |
7 | 2.704 | 0.8332 | 2.645 |
8 | 2.847 | 0.8198 | 2.791 |
9 | 2.970 | 0.8078 | 2.915 |
10 | 3.078 | 0.7971 | 3.024 |
11 | 3.173 | 0.7873 | 3.121 |
12 | 3.258 | 0.7785 | 3.207 |
13 | 3.336 | 0.7704 | 3.285 |
14 | 3.407 | 0.7630 | 3.356 |
15 | 3.472 | 0.7562 | 3.422 |
16 | 3.532 | 0.7499 | 3.482 |
17 | 3.588 | 0.7441 | 3.538 |
18 | 3.640 | 0.7386 | 3.591 |
19 | 3.689 | 0.7335 | 3.640 |
20 | 3.735 | 0.7287 | 3.686 |
21 | 3.778 | 0.7242 | 3.730 |
22 | 3.819 | 0.7199 | 3.771 |
23 | 3.858 | 0.7159 | 3.811 |
24 | 3.895 | 0.7121 | 3.847 |
25 | 3.931 | 0.7084 | 3.883 |
N | d2(N) |
---|---|
26 | 3.964 |
27 | 3.997 |
28 | 4.027 |
29 | 4.057 |
30 | 4.086 |
31 | 4.113 |
32 | 4.139 |
33 | 4.165 |
34 | 4.189 |
35 | 4.213 |
36 | 4.236 |
37 | 4.259 |
38 | 4.280 |
39 | 4.301 |
40 | 4.322 |
41 | 4.341 |
42 | 4.361 |
43 | 4.379 |
44 | 4.398 |
45 | 4.415 |
46 | 4.433 |
47 | 4.450 |
48 | 4.466 |
49 | 4.482 |
50 | 4.498 |
Utilice las siguientes tablas para hallar valores para la constante de eliminación de sesgo, c4'(), que se usa en las fórmulas para la raíz cuadrada del método MSSD para estimar sigma.
N | c4'(N) | N | c4'(N) | N | c4'(N) |
---|---|---|---|---|---|
2 | 0.797850 | 41 | 0.990797 | 80 | 0.995215 |
3 | 0.871530 | 42 | 0.991013 | 81 | 0.995272 |
4 | 0.905763 | 43 | 0.991218 | 82 | 0.995328 |
5 | 0.925222 | 44 | 0.991415 | 83 | 0.995383 |
6 | 0.937892 | 45 | 0.991602 | 84 | 0.995436 |
7 | 0.946837 | 46 | 0.991782 | 85 | 0.995489 |
8 | 0.953503 | 47 | 0.991953 | 86 | 0.995539 |
9 | 0.958669 | 48 | 0.992118 | 87 | 0.995589 |
10 | 0.962793 | 49 | 0.992276 | 88 | 0.995638 |
11 | 0.966163 | 50 | 0.992427 | 89 | 0.995685 |
12 | 0.968968 | 51 | 0.992573 | 90 | 0.995732 |
13 | 0.971341 | 52 | 0.992713 | 91 | 0.995777 |
14 | 0.973375 | 53 | 0.992848 | 92 | 0.995822 |
15 | 0.975137 | 54 | 0.992978 | 93 | 0.995865 |
16 | 0.976679 | 55 | 0.993103 | 94 | 0.995908 |
17 | 0.978039 | 56 | 0.993224 | 95 | 0.995949 |
18 | 0.979249 | 57 | 0.993340 | 96 | 0.995990 |
19 | 0.980331 | 58 | 0.993452 | 97 | 0.996030 |
20 | 0.981305 | 59 | 0.993561 | 98 | 0.996069 |
21 | 0.982187 | 60 | 0.993666 | 99 | 0.996108 |
22 | 0.982988 | 61 | 0.993767 | 100 | 0.996145 |
23 | 0.983720 | 62 | 0.993866 | 101 | 0.996182 |
24 | 0.984391 | 63 | 0.993961 | 102 | 0.996218 |
25 | 0.985009 | 64 | 0.994053 | 103 | 0.996253 |
26 | 0.985579 | 65 | 0.994142 | 104 | 0.996288 |
27 | 0.986107 | 66 | 0.994229 | 105 | 0.996322 |
28 | 0.986597 | 67 | 0.994313 | 106 | 0.996356 |
29 | 0.987054 | 68 | 0.994395 | 107 | 0.996389 |
30 | 0.987480 | 69 | 0.994474 | 108 | 0.996421 |
31 | 0.987878 | 70 | 0.994551 | 109 | 0.996452 |
32 | 0.988252 | 71 | 0.994626 | 110 | 0.996483 |
33 | 0.988603 | 72 | 0.994699 | 111 | 0.996514 |
34 | 0.988934 | 73 | 0.994769 | 112 | 0.996544 |
35 | 0.989246 | 74 | 0.994838 | 113 | 0.996573 |
36 | 0.989540 | 75 | 0.994905 | 114 | 0.996602 |
37 | 0.989819 | 76 | 0.994970 | 115 | 0.996631 |
38 | 0.990083 | 77 | 0.995034 | 116 | 0.996658 |
39 | 0.990333 | 78 | 0.995096 | 117 | 0.996686 |
40 | 0.990571 | 79 | 0.995156 | 118 | 0.996713 |
N | c4'(N) | N | c4'(N) | N | c4'(N) |
---|---|---|---|---|---|
119 | 0.996739 | 160 | 0.997541 | 201 | 0.998016 |
120 | 0.996765 | 161 | 0.997555 | 202 | 0.998025 |
121 | 0.996791 | 162 | 0.997570 | 203 | 0.998034 |
122 | 0.996816 | 163 | 0.997584 | 204 | 0.998043 |
123 | 0.996841 | 164 | 0.997598 | 205 | 0.998052 |
124 | 0.996865 | 165 | 0.997612 | 206 | 0.998061 |
125 | 0.996889 | 166 | 0.997625 | 207 | 0.998070 |
126 | 0.996913 | 167 | 0.997639 | 208 | 0.998078 |
127 | 0.996936 | 168 | 0.997652 | 209 | 0.998087 |
128 | 0.996959 | 169 | 0.997665 | 210 | 0.998095 |
129 | 0.996982 | 170 | 0.997678 | 211 | 0.998104 |
130 | 0.997004 | 171 | 0.997691 | 212 | 0.998112 |
131 | 0.997026 | 172 | 0.997703 | 213 | 0.998120 |
132 | 0.997047 | 173 | 0.997716 | 214 | 0.998128 |
133 | 0.997069 | 174 | 0.997728 | 215 | 0.998137 |
134 | 0.997089 | 175 | 0.997741 | 216 | 0.998145 |
135 | 0.997110 | 176 | 0.997753 | 217 | 0.998152 |
136 | 0.997130 | 177 | 0.997765 | 218 | 0.998160 |
137 | 0.997150 | 178 | 0.997776 | 219 | 0.998168 |
138 | 0.997170 | 179 | 0.997788 | 220 | 0.998176 |
139 | 0.997189 | 180 | 0.997800 | 221 | 0.998184 |
140 | 0.997209 | 181 | 0.997811 | 222 | 0.998191 |
141 | 0.997227 | 182 | 0.997822 | 223 | 0.998199 |
142 | 0.997246 | 183 | 0.997834 | 224 | 0.998206 |
143 | 0.997264 | 184 | 0.997845 | 225 | 0.998214 |
144 | 0.997282 | 185 | 0.997856 | 226 | 0.998221 |
145 | 0.997300 | 186 | 0.997866 | 227 | 0.998228 |
146 | 0.997318 | 187 | 0.997877 | 228 | 0.998235 |
147 | 0.997335 | 188 | 0.997888 | 229 | 0.998242 |
148 | 0.997352 | 189 | 0.997898 | 230 | 0.998250 |
149 | 0.997369 | 190 | 0.997909 | 231 | 0.998257 |
150 | 0.997386 | 191 | 0.997919 | 232 | 0.998263 |
151 | 0.997402 | 192 | 0.997929 | 233 | 0.998270 |
152 | 0.997419 | 193 | 0.997939 | 234 | 0.998277 |
153 | 0.997435 | 194 | 0.997949 | 235 | 0.998284 |
154 | 0.997450 | 195 | 0.997959 | 236 | 0.998291 |
155 | 0.997466 | 196 | 0.997969 | 237 | 0.998297 |
156 | 0.997481 | 197 | 0.997978 | 238 | 0.998304 |
157 | 0.997497 | 198 | 0.997988 | 239 | 0.998311 |
158 | 0.997512 | 199 | 0.997997 | 240 | 0.998317 |
159 | 0.997526 | 200 | 0.998007 | 241 | 0.998323 |
N | c4'(N) | N | c4'(N) | N | c4'(N) |
---|---|---|---|---|---|
242 | 0.998330 | 283 | 0.998553 | 324 | 0.998720 |
243 | 0.998336 | 284 | 0.998558 | 325 | 0.998723 |
244 | 0.998342 | 285 | 0.998562 | 326 | 0.998727 |
245 | 0.998349 | 286 | 0.998567 | 327 | 0.998730 |
246 | 0.998355 | 287 | 0.998571 | 328 | 0.998734 |
247 | 0.998361 | 288 | 0.998576 | 329 | 0.998737 |
248 | 0.998367 | 289 | 0.998580 | 330 | 0.998740 |
249 | 0.998373 | 290 | 0.998585 | 331 | 0.998744 |
250 | 0.998379 | 291 | 0.998589 | 332 | 0.998747 |
251 | 0.998385 | 292 | 0.998593 | 333 | 0.998751 |
252 | 0.998391 | 293 | 0.998598 | 334 | 0.998754 |
253 | 0.998397 | 294 | 0.998602 | 335 | 0.998757 |
254 | 0.998403 | 295 | 0.998606 | 336 | 0.998761 |
255 | 0.998408 | 296 | 0.998611 | 337 | 0.998764 |
256 | 0.998414 | 297 | 0.998615 | 338 | 0.998767 |
257 | 0.998420 | 298 | 0.998619 | 339 | 0.998770 |
258 | 0.998425 | 299 | 0.998623 | 340 | 0.998774 |
259 | 0.998431 | 300 | 0.998627 | 341 | 0.998777 |
260 | 0.998436 | 301 | 0.998632 | 342 | 0.998780 |
261 | 0.998442 | 302 | 0.998636 | 343 | 0.998783 |
262 | 0.998447 | 303 | 0.998640 | 344 | 0.998786 |
263 | 0.998453 | 304 | 0.998644 | 345 | 0.998790 |
264 | 0.998458 | 305 | 0.998648 | 346 | 0.998793 |
265 | 0.998463 | 306 | 0.998652 | 347 | 0.998796 |
266 | 0.998469 | 307 | 0.998656 | 348 | 0.998799 |
267 | 0.998474 | 308 | 0.998660 | 349 | 0.998802 |
268 | 0.998479 | 309 | 0.998664 | 350 | 0.998805 |
269 | 0.998484 | 310 | 0.998668 | 351 | 0.998808 |
270 | 0.998489 | 311 | 0.998671 | 352 | 0.998811 |
271 | 0.998495 | 312 | 0.998675 | 353 | 0.998814 |
272 | 0.998500 | 313 | 0.998679 | 354 | 0.998817 |
273 | 0.998505 | 314 | 0.998683 | 355 | 0.998820 |
274 | 0.998510 | 315 | 0.998687 | 356 | 0.998823 |
275 | 0.998515 | 316 | 0.998690 | 357 | 0.998826 |
276 | 0.998519 | 317 | 0.998694 | 358 | 0.998829 |
277 | 0.998524 | 318 | 0.998698 | 359 | 0.998832 |
278 | 0.998529 | 319 | 0.998701 | 360 | 0.998835 |
279 | 0.998534 | 320 | 0.998705 | 361 | 0.998837 |
280 | 0.998539 | 321 | 0.998709 | 362 | 0.998840 |
281 | 0.998544 | 322 | 0.998712 | 363 | 0.998843 |
282 | 0.998548 | 323 | 0.998716 | 364 | 0.998846 |
k | c4'(k) | k | c4'(k) | k | c4'(k) |
---|---|---|---|---|---|
365 | 0.998849 | 411 | 0.998963 | 457 | 0.999054 |
366 | 0.998851 | 412 | 0.998965 | 458 | 0.999056 |
367 | 0.998854 | 413 | 0.998967 | 459 | 0.999058 |
368 | 0.998857 | 414 | 0.998970 | 460 | 0.999060 |
369 | 0.998860 | 415 | 0.998972 | 461 | 0.999061 |
370 | 0.998862 | 416 | 0.998974 | 462 | 0.999063 |
371 | 0.998865 | 417 | 0.998976 | 463 | 0.999065 |
372 | 0.998868 | 418 | 0.998978 | 464 | 0.999067 |
373 | 0.998871 | 419 | 0.998980 | 465 | 0.999068 |
374 | 0.998873 | 420 | 0.998982 | 466 | 0.999070 |
375 | 0.998876 | 421 | 0.998985 | 467 | 0.999072 |
376 | 0.998879 | 422 | 0.998987 | 468 | 0.999073 |
377 | 0.998881 | 423 | 0.998989 | 469 | 0.999075 |
378 | 0.998884 | 424 | 0.998991 | 470 | 0.999077 |
379 | 0.998886 | 425 | 0.998993 | 471 | 0.999078 |
380 | 0.998889 | 426 | 0.998995 | 472 | 0.999080 |
381 | 0.998892 | 427 | 0.998997 | 473 | 0.999082 |
382 | 0.998894 | 428 | 0.998999 | 474 | 0.999084 |
383 | 0.998897 | 429 | 0.999001 | 475 | 0.999085 |
384 | 0.998899 | 430 | 0.999003 | 476 | 0.999087 |
385 | 0.998902 | 431 | 0.999005 | 477 | 0.999088 |
386 | 0.998904 | 432 | 0.999007 | 478 | 0.999090 |
387 | 0.998907 | 433 | 0.999009 | 479 | 0.999092 |
388 | 0.998909 | 434 | 0.999011 | 480 | 0.999093 |
389 | 0.998912 | 435 | 0.999013 | 481 | 0.999095 |
390 | 0.998914 | 436 | 0.999015 | 482 | 0.999097 |
391 | 0.998917 | 437 | 0.999017 | 483 | 0.999098 |
392 | 0.998919 | 438 | 0.999019 | 484 | 0.999100 |
393 | 0.998921 | 439 | 0.999021 | 485 | 0.999101 |
394 | 0.998924 | 440 | 0.999023 | 486 | 0.999103 |
395 | 0.998926 | 441 | 0.999025 | 487 | 0.999104 |
396 | 0.998929 | 442 | 0.999027 | 488 | 0.999106 |
397 | 0.998931 | 443 | 0.999028 | 489 | 0.999108 |
398 | 0.998933 | 444 | 0.999030 | 490 | 0.999109 |
399 | 0.998936 | 445 | 0.999032 | 491 | 0.999111 |
400 | 0.998938 | 446 | 0.999034 | 492 | 0.999112 |
401 | 0.998940 | 447 | 0.999036 | 493 | 0.999114 |
402 | 0.998943 | 448 | 0.999038 | 494 | 0.999115 |
403 | 0.998945 | 449 | 0.999040 | 495 | 0.999117 |
404 | 0.998947 | 450 | 0.999042 | 496 | 0.999118 |
405 | 0.998950 | 451 | 0.999043 | 497 | 0.999120 |
406 | 0.998952 | 452 | 0.999045 | 498 | 0.999121 |
407 | 0.998954 | 453 | 0.999047 | 499 | 0.999123 |
408 | 0.998956 | 454 | 0.999049 | 500 | 0.999124 |
409 | 0.998959 | 455 | 0.999051 | ||
410 | 0.998961 | 456 | 0.999052 |
Si usted utiliza una transformación de Box-Cox, Minitab transforma los valores de los datos originales (Yi) de acuerdo con la siguiente fórmula:
donde λ es el parámetro de la transformación. Luego Minitab crea una gráfica de control con los valores de los datos transformados (Wi). Para enterarse de cómo Minitab elige el valor óptimo para λ, vaya a Métodos y fórmulas para Transformación de Box-Cox.
λ | Transformación |
---|---|
2 | |
0.5 | |
0 | |
−0.5 | |
−1 |