1 Zakład Bioinformatyki, Instytut Informatyki, Uniwersytet w Białymstoku
✉ Correspondence: Jarosław Kotowicz <j.kotowicz@uwb.edu.pl>
[1] 2.1249874 -10.2865847 1.7913608 1.9451335 -5.5892037 0.1287322 -1.5224232 3.6882780 -3.8030355
[10] 3.5524538 [1] 2.0598291 1.0320158 -0.9580062 -0.7314746 1.9001470 3.2782507 1.9737342 2.6156499 -0.6128363 1.3471175Inny sposób wywołania
[1] 2.1249874 -10.2865847 1.7913608 1.9451335 -5.5892037 0.1287322[1] -5.2829695 0.7300313 8.9150630 7.4593185 -2.4671868 13.0699565
Cramer-von Mises normality test
data: x
W = 0.058536, p-value = 0.3952Interpretacja wyniku!
W
0.05853586 [1] 0.3951641[1] "Cramer-von Mises normality test"[1] "x"Aproksymacja chi-kwadrat mo戼㹦e by攼㸶 niepoprawna
Chi-squared test for given probabilities
data: v
X-squared = 167.99, df = 999, p-value = 1Interpretacja wyniku!
[1] -13.48324[1] 17.11177[1] -13.48324 17.11177Błąd w poleceniu 'chisq.test(y)':
wszystkie wpisy 'x' muszą być nieujemne oraz skończoneAproksymacja chi-kwadrat mo戼㹦e by攼㸶 niepoprawna
Chi-squared test for given probabilities
data: y - min(y)
X-squared = 1009.9, df = 999, p-value = 0.3979Interpretacja wyniku!
[1] -0.95800624 -0.73147460 -0.61283634 -0.26259484 -0.77301464 -0.50261229 -0.93218663 -0.16600981 -0.67371737
[10] -0.75644257 -0.81003315 -0.65999794 -0.75958694 -0.37211472 -0.20222113 -0.89404429 -0.06799252 -0.38338615
[19] -0.66683780 -0.49019173 -0.38805568 -0.74434469 -0.97263808 -0.11684305 -0.94581750 -0.14191842 -0.36905811
[28] -0.69450349 -0.01473395 -0.19964384 -0.45496207 -0.23851467 -0.09463141 -0.72448216 -0.26153626 -0.36264080
[37] -0.10229570 -0.36607777 -0.09141630 -0.32872861 -0.97090753 -0.34199329 -0.16551923 -0.68151346 -0.87377524
[46] -0.52569026 -0.64925711 -0.83881919 -0.82314761 -0.08549429 -0.73140699 -0.45307140 -0.25339035 -0.68834832
[55] -0.47735923 -0.77829048 -0.32139343 -0.79568583 -0.38602023 -0.53205535 -0.61340768 -0.26170468 -0.98069078
[64] -0.60638089 -0.15911746 -0.48566788 -0.35561190 -0.79563512 -0.42337023 -0.37548184 -0.58462850 -0.82951841
[73] -0.77496934 -0.52477507 -0.17227290 -0.36950605 -0.36234337 -0.82798795 -0.33728772 -0.79382131 -0.19574776
[82] -0.35778896 -0.79959051 -0.31995309 -0.15032912 -0.96685557 -0.89311734 -0.60936618 -0.88826419 -0.17153404
[91] -0.67411027 -0.36960852 -0.19024623 -0.06509306 -0.10393386 -0.17197220 -0.80091542 -0.40873708 -0.85647391
[100] -0.99412833 -0.94622227 -0.86527903 -0.94529888 -0.80214489 -0.22202381 -0.63103740 -0.80186290 -0.46176165
[109] -0.25725595 -0.25815562 -0.88425588 -0.30024511 -0.81757783 -0.68620628 -0.75661401 -0.79866321 -0.85138138
[118] -0.78717002 -0.81789560 -0.58657005 -0.14679690 -0.18831857 -0.95190122 -0.77608847 -0.68858983 -0.42548730
[127] -0.38342125 -0.71377264 -0.69549763 -0.16481542 -0.51893462 -0.20418006 -0.78671444 -0.74021722 -0.90043103
[136] -0.28708037 -0.29597974 -0.81989008 -0.69879078 -0.84335086 -0.64482531 -0.41073430 -0.42575010 -0.06573560
[145] -0.10751313 -0.41918188 -0.40335221 -0.78606690 -0.17088896 -0.18281418 -0.37985539 -0.24526034 -0.38019913
[154] -0.82241759 -0.11671112 -0.98866375 -0.51578829 -0.91530054 -0.62414241 -0.52262317 -0.45940923 -0.63344993
[163] -0.86202492 -0.65678423 -0.39341283 -0.97487639 -0.60950282 -0.25415060 -0.08329700 -0.71898936 [1] -1.1941283 -0.6941283 -0.1941283 0.3058717 0.8058717 1.3058717 1.8058717 2.3058717 2.8058717 3.3058717
[11] 3.8058717 4.3058717 4.8058717yy
[-1.19,-0.194] (-0.194,0.806] (0.806,1.81] (1.81,2.81] (2.81,3.81] (3.81,4.81]
143 159 178 170 158 171
Chi-squared test for given probabilities
data: table(yy)
X-squared = 4.7732, df = 5, p-value = 0.4442Interpretacja wyniku!
[1] -13.4832388 -12.4832388 -11.4832388 -10.4832388 -9.4832388 -8.4832388 -7.4832388 -6.4832388 -5.4832388
[10] -4.4832388 -3.4832388 -2.4832388 -1.4832388 -0.4832388 0.5167612 1.5167612 2.5167612 3.5167612
[19] 4.5167612 5.5167612 6.5167612 7.5167612 8.5167612 9.5167612 10.5167612 11.5167612 12.5167612
[28] 13.5167612 14.5167612 15.5167612 16.5167612
Chi-squared test for given probabilities
data: xx
X-squared = 735.62, df = 29, p-value < 2.2e-16Interpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: x
D = 0.581, p-value < 2.2e-16
alternative hypothesis: two-sidedInterpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: x
D = 0.48439, p-value < 2.2e-16
alternative hypothesis: two-sidedInterpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: x
D = 0.02226, p-value = 0.7047
alternative hypothesis: two-sidedInterpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: y
D = 0.662, p-value < 2.2e-16
alternative hypothesis: two-sidedInterpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: y
D = 0.022288, p-value = 0.7032
alternative hypothesis: two-sidedInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D = 0.273, p-value < 2.2e-16
alternative hypothesis: two-sidedInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D^+ = 0.273, p-value < 2.2e-16
alternative hypothesis: the CDF of x lies above that of yInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D^- = 0.26, p-value < 2.2e-16
alternative hypothesis: the CDF of x lies below that of yInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D = 0.35, p-value = 9.57e-06
alternative hypothesis: two-sidedInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D^+ = 0.35, p-value = 4.785e-06
alternative hypothesis: the CDF of x lies above that of yInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D^- = 0.04, p-value = 0.8521
alternative hypothesis: the CDF of x lies below that of yInterpretacja wyniku!
rm(list = ls())
set.seed(20200429)
x <- rnorm(1000, mean = 2, sd = 5)
set.seed(20200429)
y <- runif(1000, -1, 5) [1] 4.6714832 0.8892507 -2.2792958 3.4292737 0.9942545 4.7910496 -1.4429176 4.1738261 9.0957510 -1.5264687 [1] 3.2205911 2.9156664 1.4725917 3.5789826 0.1762271 4.7592749 2.6750318 2.6564900 1.5217459 4.7267674[1] 4.6714832 0.8892507 -2.2792958 3.4292737 0.9942545 4.7910496[1] 5.775721 1.399777 -3.369136 7.444115 3.920325 -2.672647
Cramer-von Mises normality test
data: x
W = 0.11129, p-value = 0.07867Interpretacja wyniku!
Aproksymacja chi-kwadrat mo戼㹦e by攼㸶 niepoprawna
Chi-squared test for given probabilities
data: v
X-squared = 161.27, df = 999, p-value = 1Interpretacja wyniku!
Błąd w poleceniu 'chisq.test(y)':
wszystkie wpisy 'x' muszą być nieujemne oraz skończone [1] -0.774558080 -0.459011670 -0.165688826 -0.931717390 -0.557456438 -0.059941750 -0.070385757 -0.018147833
[9] -0.768142478 -0.985676002 -0.812243139 -0.667685071 -0.953479179 -0.001755881 -0.985878188 -0.769708028
[17] -0.523014728 -0.273197286 -0.806061134 -0.293130086 -0.082851095 -0.778385488 -0.264328896 -0.750303022
[25] -0.087063489 -0.105209374 -0.528968196 -0.144532620 -0.231985062 -0.505583453 -0.307254543 -0.571332299
[33] -0.134119838 -0.708954140 -0.368703438 -0.260358631 -0.543540496 -0.943924432 -0.713043388 -0.042399266
[41] -0.177349371 -0.687168158 -0.041845309 -0.197333938 -0.791663572 -0.392224447 -0.639529231 -0.737074837
[49] -0.458525225 -0.666554539 -0.757966761 -0.126263821 -0.422168402 -0.759431269 -0.631188556 -0.610675837
[57] -0.774739987 -0.456104595 -0.153861335 -0.043877859 -0.790630813 -0.808079943 -0.452797469 -0.811868130
[65] -0.807759208 -0.479214219 -0.852418177 -0.993603960 -0.804138533 -0.568458313 -0.509545429 -0.011551832
[73] -0.159390502 -0.053273968 -0.334688193 -0.340534896 -0.160814290 -0.516160637 -0.242104731 -0.752340706
[81] -0.874383947 -0.331755495 -0.373883135 -0.292805829 -0.162503794 -0.612528816 -0.694291612 -0.330250801
[89] -0.243591165 -0.901544767 -0.455351744 -0.616433110 -0.382176313 -0.066072336 -0.703175373 -0.096358399
[97] -0.493803163 -0.117744283 -0.654803821 -0.572179593 -0.246416953 -0.808070840 -0.394317290 -0.229937719
[105] -0.411889528 -0.677631333 -0.160661523 -0.501827216 -0.807501515 -0.083157544 -0.128178237 -0.337590765
[113] -0.212472433 -0.210635480 -0.996744194 -0.217153860 -0.826740840 -0.632368882 -0.235269646 -0.474523466
[121] -0.051744392 -0.184939863 -0.327123516 -0.476962590 -0.037486764 -0.527944332 -0.736527469 -0.091727185
[129] -0.530672375 -0.273964711 -0.327652875 -0.958741308 -0.366725228 -0.400749226 -0.330075766 -0.315880306
[137] -0.789588202 -0.650324373 -0.849804247 -0.691238732 -0.978339911 -0.308380773 -0.679004692 -0.776305452
[145] -0.626182226 -0.473648730 -0.173046334Aproksymacja chi-kwadrat mo戼㹦e by攼㸶 niepoprawna
Chi-squared test for given probabilities
data: y - min(y)
X-squared = 968.65, df = 999, p-value = 0.7489Interpretacja wyniku!
[1] -1.1967442 -0.6967442 -0.1967442 0.3032558 0.8032558 1.3032558 1.8032558 2.3032558 2.8032558 3.3032558
[11] 3.8032558 4.3032558 4.8032558yy
[-1.2,-0.197] (-0.197,0.803] (0.803,1.8] (1.8,2.8] (2.8,3.8] (3.8,4.8]
115 155 195 156 153 190
Chi-squared test for given probabilities
data: table(yy)
X-squared = 26.373, df = 5, p-value = 7.552e-05Interpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: x
D = 0.571, p-value < 2.2e-16
alternative hypothesis: two-sidedInterpretacja wyniku!
Cramer-von Mises normality test
data: x
W = 0.11129, p-value = 0.07867Interpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: x
D = 0.46835, p-value < 2.2e-16
alternative hypothesis: two-sidedInterpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: x
D = 0.021549, p-value = 0.7419
alternative hypothesis: two-sidedInterpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: y
D = 0.696, p-value < 2.2e-16
alternative hypothesis: two-sidedInterpretacja wyniku!
One-sample Kolmogorov-Smirnov test
data: y
D = 0.032295, p-value = 0.2479
alternative hypothesis: two-sidedInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D = 0.284, p-value < 2.2e-16
alternative hypothesis: two-sidedInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D^+ = 0.268, p-value < 2.2e-16
alternative hypothesis: the CDF of x lies above that of yInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D^- = 0.284, p-value < 2.2e-16
alternative hypothesis: the CDF of x lies below that of yInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and t
D = 0.094, p-value = 0.0002908
alternative hypothesis: two-sidedInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and t
D^+ = 0, p-value = 1
alternative hypothesis: the CDF of x lies above that of yInterpretacja wyniku!
Two-sample Kolmogorov-Smirnov test
data: x and y
D^- = 0.284, p-value < 2.2e-16
alternative hypothesis: the CDF of x lies below that of yInterpretacja wyniku!
One Sample t-test
data: x
t = 13.394, df = 999, p-value < 2.2e-16
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
1.828938 2.456857
sample estimates:
mean of x
2.142898 Interpretacja wyniku!
One Sample t-test
data: x
t = 0.89315, df = 999, p-value = 0.372
alternative hypothesis: true mean is not equal to 2
95 percent confidence interval:
1.828938 2.456857
sample estimates:
mean of x
2.142898 Interpretacja wyniku!
Welch Two Sample t-test
data: x[1:500] and x[501:1000]
t = 0.61361, df = 997.52, p-value = 0.5396
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.4317099 0.8245226
sample estimates:
mean of x mean of y
2.241101 2.044695 Interpretacja wyniku!