All language subtitles for 004 Discrete Distributions The Uniform Distribution_en

af Afrikaans
ak Akan
sq Albanian
am Amharic
ar Arabic
hy Armenian
az Azerbaijani
eu Basque
be Belarusian
bem Bemba
bn Bengali
bh Bihari
bs Bosnian
br Breton
bg Bulgarian
km Cambodian
ca Catalan
ceb Cebuano
chr Cherokee
ny Chichewa
zh-CN Chinese (Simplified)
zh-TW Chinese (Traditional)
co Corsican
hr Croatian
cs Czech
da Danish
nl Dutch
en English
eo Esperanto
et Estonian
ee Ewe
fo Faroese
tl Filipino
fi Finnish
fr French
fy Frisian
gaa Ga
gl Galician
ka Georgian
de German
el Greek
gn Guarani
gu Gujarati
ht Haitian Creole
ha Hausa
haw Hawaiian
iw Hebrew
hi Hindi
hmn Hmong
hu Hungarian
is Icelandic
ig Igbo
id Indonesian
ia Interlingua
ga Irish
it Italian
ja Japanese
jw Javanese
kn Kannada
kk Kazakh
rw Kinyarwanda
rn Kirundi
kg Kongo
ko Korean
kri Krio (Sierra Leone)
ku Kurdish
ckb Kurdish (Soranî)
ky Kyrgyz
lo Laothian
la Latin
lv Latvian
ln Lingala
lt Lithuanian
loz Lozi
lg Luganda
ach Luo
lb Luxembourgish
mk Macedonian
mg Malagasy
ms Malay
ml Malayalam
mt Maltese
mi Maori
mr Marathi
mfe Mauritian Creole
mo Moldavian
mn Mongolian
my Myanmar (Burmese)
sr-ME Montenegrin
ne Nepali
pcm Nigerian Pidgin
nso Northern Sotho
no Norwegian
nn Norwegian (Nynorsk)
oc Occitan
or Oriya
om Oromo
ps Pashto
fa Persian Download
pl Polish
pt-BR Portuguese (Brazil)
pt Portuguese (Portugal)
pa Punjabi
qu Quechua
ro Romanian
rm Romansh
nyn Runyakitara
ru Russian
sm Samoan
gd Scots Gaelic
sr Serbian
sh Serbo-Croatian
st Sesotho
tn Setswana
crs Seychellois Creole
sn Shona
sd Sindhi
si Sinhalese
sk Slovak
sl Slovenian
so Somali
es Spanish
es-419 Spanish (Latin American)
su Sundanese
sw Swahili
sv Swedish
tg Tajik
ta Tamil
tt Tatar
te Telugu
th Thai
ti Tigrinya
to Tonga
lua Tshiluba
tum Tumbuka
tr Turkish
tk Turkmen
tw Twi
ug Uighur
uk Ukrainian
ur Urdu
uz Uzbek
vi Vietnamese
cy Welsh
wo Wolof
xh Xhosa
yi Yiddish
yo Yoruba
zu Zulu
Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:03,510 --> 00:00:04,598 Instructor: Hi there. 2 00:00:04,598 --> 00:00:06,330 In in this lecture we are going to discuss 3 00:00:06,330 --> 00:00:08,580 the uniform distribution. 4 00:00:08,580 --> 00:00:11,760 For starters, we use the letter U 5 00:00:11,760 --> 00:00:14,220 to define the uniform distribution, 6 00:00:14,220 --> 00:00:17,580 followed by the range of of the values in the data set. 7 00:00:17,580 --> 00:00:20,610 Therefore, we read the following statement as 8 00:00:20,610 --> 00:00:24,660 variable X, follows a discreet uniform distribution 9 00:00:24,660 --> 00:00:27,750 ranging from three to seven. 10 00:00:27,750 --> 00:00:29,940 Events which follow the uniform distribution 11 00:00:29,940 --> 00:00:32,883 are ones where all outcomes have equal probability. 12 00:00:33,750 --> 00:00:37,143 One such event is rolling a single standard, six sided di. 13 00:00:39,319 --> 00:00:41,370 When we roll a standard six sided di, 14 00:00:41,370 --> 00:00:45,600 we have equal change of getting any value from one to six. 15 00:00:45,600 --> 00:00:47,520 The graph of the probability distribution 16 00:00:47,520 --> 00:00:52,520 would have six, equally tall bars, all reaching up to 1/6. 17 00:00:52,740 --> 00:00:55,020 Many events in gambling provide such odds, 18 00:00:55,020 --> 00:00:57,903 where each individual outcome is equally likely. 19 00:00:58,950 --> 00:01:01,350 Not only that, but many everyday situations 20 00:01:01,350 --> 00:01:03,780 follow the uniform distribution. 21 00:01:03,780 --> 00:01:06,630 If your friend offers you three, identical chocolate bars, 22 00:01:06,630 --> 00:01:09,480 the probability assigned to you choosing one of them 23 00:01:09,480 --> 00:01:11,643 also follow the uniform distribution. 24 00:01:13,170 --> 00:01:15,660 One big drawback of uniform distributions 25 00:01:15,660 --> 00:01:17,910 is that the expected value provides us 26 00:01:17,910 --> 00:01:20,130 no relevant information 27 00:01:20,130 --> 00:01:22,770 because all outcomes have the same probability. 28 00:01:22,770 --> 00:01:25,470 The expected value, which is 3.5, 29 00:01:25,470 --> 00:01:27,183 brings no predictive power. 30 00:01:28,110 --> 00:01:30,090 We can still apply the formulas from earlier 31 00:01:30,090 --> 00:01:34,683 and get a mean of 3.5 at a variance of 105 over 36. 32 00:01:35,790 --> 00:01:39,150 These values however, are completely uninterpretable 33 00:01:39,150 --> 00:01:42,063 and there is no real intuition behind what they mean. 34 00:01:43,440 --> 00:01:45,930 The main takeaway is that when an event is following 35 00:01:45,930 --> 00:01:50,160 the uniform distribution, each outcome is equally likely. 36 00:01:50,160 --> 00:01:53,220 Therefore, both the mean and the variance 37 00:01:53,220 --> 00:01:55,410 are uninterpretable and possess 38 00:01:55,410 --> 00:01:57,453 no predictive power whatsoever. 39 00:01:59,190 --> 00:02:02,820 Okay, sadly, the uniform is not the only 40 00:02:02,820 --> 00:02:05,490 discreet distribution, for which we cannot construct 41 00:02:05,490 --> 00:02:08,009 useful prediction intervals. 42 00:02:08,009 --> 00:02:10,020 In the next video, we will introduce 43 00:02:10,020 --> 00:02:12,300 the Bernoulli distribution. 44 00:02:12,300 --> 00:02:13,323 Thanks for watching. 3526

Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.