이번역번역이지는최신내용을담고담고않습니다않습니다。최신최신내용을영문영문으로여기를클릭클릭据/a>
로그정규분포는갈톤(高尔顿)분포라고도하며,확률분포의로그가정규분포를가질때의확률분포를말합니다。日志x (x)는가양수인경우에만존재하므로관심있는수량이양수인경우에만로그정규분포를적용할수있습니다。据/P.>据P.>统计和机器学习工具箱™에서는다음과같이로그정규분포를사용여러여러방법을합니다。据/P.>据D.iv class="itemizedlist">
확률분포를표본데이터에피팅하거나(据a href="//www.tatmou.com/kr/help/stats/fitdist.html"> 분포피팅기据/a>앱을사용하여대화형방식으로으로로그를사용합니다。앱에서객체를내보내고함수함수사용할수있습니다。据/P.>据/li>
지정된분포모수를이용해해분포전용(据a href="//www.tatmou.com/kr/help/stats/logncdf.html" hreflang="en"> 일반분포함수(据a href="//www.tatmou.com/kr/help/stats/prob.normaldistribution.cdf.html">Fitdist.据/code>)모수값을지정하여(据a href="//www.tatmou.com/kr/help/stats/makedist.html">
制造主义者据/code>)확률분포객체据a href="//www.tatmou.com/kr/help/stats/prob.lognormaldistribution.html" hreflang="en">
lognormaldistribution.据/code>을을합니다。그런다음객체함수를사용분포분포분포를실행,난수를생성하는등의작업수행합니다。据/P.>据/li>
logncdf.据/code>那据a href="//www.tatmou.com/kr/help/stats/lognpdf.html" hreflang="en">
lognpdf.据/code>那据a href="//www.tatmou.com/kr/help/stats/logninv.html" hreflang="en">
logninv.据/code>那据a href="//www.tatmou.com/kr/help/stats/lognlike.html" hreflang="en">
lognlike.据/code>那据a href="//www.tatmou.com/kr/help/stats/lognstat.html" hreflang="en">
lognstat.据/code>那据a href="//www.tatmou.com/kr/help/stats/lognfit.html" hreflang="en">
lognfit.据/code>那据a href="//www.tatmou.com/kr/help/stats/lognrnd.html" hreflang="en">
lognrnd据/code>)를사용합니다。분포전용함수는여러로그정규분포의모수를받을수있습니다。据/P.>据/li>
CDF.据/code>那据a href="//www.tatmou.com/kr/help/stats/prob.normaldistribution.icdf.html" hreflang="en">
ICDF.据/code>那据a href="//www.tatmou.com/kr/help/stats/prob.normaldistribution.pdf.html">
PDF.据/code>那据a href="//www.tatmou.com/kr/help/stats/prob.normaldistribution.random.html">
随机的据/code>)를지정된분포이름(据code class="literal">'lognormal'据/code>)및모수와함께사용합니다。据/P.>据/li>
로그정규분포는다음과다음과모수사용합니다。据/P.>据D.iv class="table-responsive">
모수据/T.h> | 설명据/T.h> | 지원据/T.h> |
---|---|---|
μ据/code>(据span class="inlineequation">μ据/span>)据/T.D.>据T.D.>로그값의평균据/T.D.>据T.D.>据span class="inlineequation">
|
||
Sigma.据/code>(据span class="inlineequation">σ据/span>)据/T.D.>据T.D.>로그값의표준편차据/T.D.>据T.D.>据span class="inlineequation">
|
x xμsΣ를를갖는로그정규분포를따르면따르면편차갖는평균μ및표준Σ를를갖는정규분포를편차편차편차据/P.>据section itemprop="content">
로그정규분포를데이터에피팅하고모수추정값을구하려면据a href="//www.tatmou.com/kr/help/stats/lognfit.html" hreflang="en"> 중도절단되지않은데이터에에据code class="function">lognfit.据/code>및据code class="function">Fitdist.据/code>는는분포모수에에대한무편향추정값을,据code class="function">m据/code>는최대가능도추정값을구합니다。据/P.>据/li>
중도절단된데이터에에据code class="function">lognfit.据/code>那据code class="function">Fitdist.据/code>那据code class="function">m据/code>는최대가능도추정값을구합니다。据/P.>据/li>
lognfit.据/code>那据a href="//www.tatmou.com/kr/help/stats/fitdist.html">
Fitdist.据/code>또는据a href="//www.tatmou.com/kr/help/stats/mle.html">
m据/code>를사용하십시오。据/P.>据D.iv class="itemizedlist">
모수추정값을반환하는据code class="function">lognfit.据/code>및据code class="function">m据/code>와달리,据code class="function">Fitdist.据/code>는피팅된확률분포객체据a href="//www.tatmou.com/kr/help/stats/prob.lognormaldistribution.html" hreflang="en"> 로그정규확률변수의평균과m분산v는다음과같이로그정규분포모수µ및σ의함수입니다。据/P.>据D.iv id="d123e137042" class="mediaobject">
lognormaldistribution.据/code>을을합니다。객체속성据code class="argument">μ据/code>와据code class="argument">Sigma.据/code>는모수추정값을합니다합니다。据/P.>据/section>
기술통계량据/h4>
또한,다음과같이평균m과분산v로부터로그정규모수모수모수와와σ를를계산할수据/P.>据D.iv id="d123e137056" class="mediaobject">
로그로그정규분포에대한대한밀도밀도(PDF)는는같습니다。据/P.>据D.iv id="d123e137064" class="mediaobject">
예제는据a href="//www.tatmou.com/kr/help/stats/lognormal-distribution.html" class="intrnllnk">로그정규분포pdf계산하기据/a>항목을참조하십시오。据/P.>据/section>
로그로그정규분포에에대한누적(CDF)는다음과같습니다。据/P.>据D.iv id="d123e137076" class="mediaobject">
누적분포함수据/h3>
예제는据a href="//www.tatmou.com/kr/help/stats/lognormal-distribution.html" class="intrnllnk">로그정규분포cdf계산하기据/a>항목을참조하십시오。据/P.>据/section>
미국의4인가족의이据code class="literal">mu = log(20,000)据/code>이고据code class="literal">Sigma = 1据/code>인인정규분포를따른다가정해해。수입밀도를계산하고합니다합니다。据/P.>据P.>모수모수값을지정지정하여로그분포객체를를정규분포객체를据/P.>据D.iv class="code_responsive">
pdf값을계산합니다。据/P.>据D.iv class="code_responsive">
PDF를플로팅합니다。据/P.>据D.iv class="code_responsive">
평균据code class="literal">μ据/code>및표준편차据code class="literal">Sigma.据/code>를를갖는로그정규정규분포에据code class="literal">X据/code>의값에서cdf값을계산합니다。据/P.>据D.iv class="code_responsive">
CDF를플로팅합니다。据/P.>据D.iv class="code_responsive">
X据/em>가모수据span class="emphasis">μ.据/em>및据span class="emphasis">σ据/em>를갖는로그정규분포를따르면日志(据span class="emphasis">X据/em>)는평균据span class="emphasis">μ.据/em>및표준편차据span class="emphasis">σ据/em>를갖는정규분포를따릅니다。분포분포를사용하여정규분포와로그정규사이의의를를사합니다。据/P.>据P.>모수모수값을지정지정하여로그분포객체를를정규분포객체를据/P.>据D.iv class="code_responsive">
로그로그정규분포의의평균을계산계산据/P.>据D.iv class="code_responsive">
로그정규분포의평균은据code class="literal">μ据/code>모수와동일하지않습니다。로그값의평균은据code class="literal">μ据/code>와와합니다。난수를생성하여이관계를확인합니다。据/P.>据P.>로그정규분포에서난수를생성하고그로그값을계산합니다。据/P.>据D.iv class="code_responsive">
로그값의평균을합니다합니다。据/P.>据D.iv class="code_responsive">
이플롯을통해据code class="literal">X据/code>의로그값이정규분포된것을알수있습니다。据/P.>据P.>据code class="literal">histfit据/code>은据code class="literal">Fitdist.据/code>를사용하여분포를데이터에피팅합니다。피팅에사용되는모수를가져오려면据code class="literal">Fitdist.据/code>를사용하십시오。据/P.>据D.iv class="code_responsive">
추정된정규분포모수는로그정규분포모수5와2에가깝습니다。据/P.>据D.iv class="procedure">
로그정규분포에서생성된수입데이터를사용하여로그정규분포pdf와버(毛刺)분포pdf를비교합니다。据/P.>据P.>수입데이터를생성합니다。据/P.>据D.iv class="code_responsive">
버(毛刺)분포분포피팅합니다。据/P.>据D.iv class="code_responsive">
수입수입이터의의(毛刺)분포pdf와로그정규pdf를동일한图에플로팅합니다。据/P.>据D.iv class="code_responsive">
예제据/h3>
로그정규분포pdf계산하기据/h4>
pd = makedist(据span style="color:#A020F0">'lognormal'据/span>那据span style="color:#A020F0">“亩”据/span>,log(20000),据span style="color:#A020F0">'sigma'据/span>1)据/P.re>
PD = LogNormalDistribution Lognormal Dircutsion Mu = 9.90349 sigma = 1据/P.re>
X =(10:1000:125010)';y = pdf(pd,x);据/P.re>
绘图(x,y)h = gca;h.xtick = [0 30000 60000 90000 120000];h.xticklabel = {据span style="color:#A020F0">'0'据/span>那据span style="color:#A020F0">'$ 30,000'据/span>那据span style="color:#A020F0">'$ 60,000'据/span>那据span style="color:#0000FF">......据/span>'$ 90,000'据/span>那据span style="color:#A020F0">“120000美元”据/span>};据/P.re>
로그정규분포cdf계산하기据/h4>
x = 0:0.2:10;mu = 0;Sigma = 1;p = logncdf(x,mu,sigma);据/P.re>
绘图(x,p)网格据span style="color:#A020F0">在据/span>包含(据span style="color:#A020F0">'X'据/span>) ylabel (据span style="color:#A020F0">'P'据/span>)据/P.re>
정규분포와로그정규분포사이의관계据/h4>
pd = makedist(据span style="color:#A020F0">'lognormal'据/span>那据span style="color:#A020F0">“亩”据/span>5,据span style="color:#A020F0">'sigma'据/span>2)据/P.re>
PD = LogNormalDistribution Lognormal分布Mu = 5 sigma = 2据/P.re>
意思是(pd)据/P.re>
ans = 1.0966e + 03据/P.re>
rng (据span style="color:#A020F0">“默认”据/span>);据span style="color:#228B22">%的再现性据/span>x =随机(PD,10000,1);logx = log(x);据/P.re>
m =意味着(计算lnx)据/P.re>
m = 5.0033.据/P.re>
X据/code>가로그로그정규분포분포를때문때문据code class="literal">X据/code>의로그의평균은据code class="literal">X据/code>의据code class="literal">μ据/code>모수에가깝습니다。据/P.>据P.>정규정규가피팅된据code class="literal">计算lnx据/code>의히스토그램을생성합니다。据/P.>据D.iv class="code_responsive">
histfit(logx)据/P.re>
pd_normal = fitdist(计算lnx,据span style="color:#A020F0">'普通的'据/span>)据/P.re>
pd_normal =正规分布正常分布Mu = 5.00332 [4.96445,5.04219] sigma = 1.98296 [1.95585,2.01083]据/P.re>
로그정규분포pdf와버(毛刺)분포pdf비교하기据/h4>
rng (据span style="color:#A020F0">“默认”据/span>)据span style="color:#228B22">%的再现性据/span>y =随机(据span style="color:#A020F0">'lognormal'据/span>,日志(25000),0.65,[500,1]);据/P.re>
pd = fitdist(y,据span style="color:#A020F0">'毛刺'据/span>)据/P.re>
PD = Burrdistribution Burr分布Alpha = 26007.2 [21165.5,31956.4] C = 2.63743 [2.3053,3.0174] k = 1.09658 [0.775479,1.55064]据/P.re>
p_burr = pdf (pd, sortrows (y));p_lognormal = pdf (据span style="color:#A020F0">'lognormal'据/span>,排出(y),log(25000),0.65);绘图(Sortrows(Y),P_BURR,据span style="color:#A020F0">' - '据/span>,sortrows(y),p_lognormal,据span style="color:#A020F0">' - 。'据/span>) 标题(据span style="color:#A020F0">'Burr和Lognormal PDF适合收入数据'据/span>) 传奇(据span style="color:#A020F0">“毛刺分布”据/span>那据span style="color:#A020F0">'lognormal分布'据/span>)据/P.re>
관련분포据/h3>
[1] Abramowitz,Milton和Irene A. Stegun,EDS。数学函数手册:用公式,图形和数学表。9.多佛打印;[nachdr。Der Ausg。von 1972]。数学的多佛书。纽约,纽约:Dover Pural,2013。据/P.>据/D.iv>
M.埃文斯,N.黑斯廷斯和B.皮科克。《统计分布》,第2版,霍博肯,新泽西州:约翰·威利父子公司,1993。据/P.>据/D.iv>
终身数据的统计模型和方法。新泽西州霍博肯:Wiley-Interscience出版社,1982年。据/P.>据/D.iv>
[4] Marsaglia,G.和W. W. Tsang。“快速,易于实现的方法,用于从降低或对称的单峰密度函数中采样。”暹罗学报科学与统计计算。卷。5,第2,1984,第349-359页。据/P.>据/D.iv>
[5] Meeker,W.Q.和L. A. Escobar。可靠性数据的统计方法。Hoboken,NJ:John Wiley&Sons,Inc。,1998年。据/P.>据/D.iv>
Mood, A. M., F. A. Graybill和dc . Boes。《统计理论导论》,第三版,纽约:麦格劳-希尔出版社,1974年版。540 - 541页。据/P.>据/D.iv>
logncdf.据/code>
|据span itemscope itemtype="//www.tatmou.com/help/schema/MathWorksDocPage/SeeAlso" itemprop="seealso">lognfit.据/code>
|据span itemscope itemtype="//www.tatmou.com/help/schema/MathWorksDocPage/SeeAlso" itemprop="seealso">logninv.据/code>
|据span itemscope itemtype="//www.tatmou.com/help/schema/MathWorksDocPage/SeeAlso" itemprop="seealso">lognlike.据/code>
|据span itemscope itemtype="//www.tatmou.com/help/schema/MathWorksDocPage/SeeAlso" itemprop="seealso">lognormaldistribution.据/code>
|据span itemscope itemtype="//www.tatmou.com/help/schema/MathWorksDocPage/SeeAlso" itemprop="seealso">lognpdf.据/code>
|据span itemscope itemtype="//www.tatmou.com/help/schema/MathWorksDocPage/SeeAlso" itemprop="seealso">lognrnd据/code>
|据span itemscope itemtype="//www.tatmou.com/help/schema/MathWorksDocPage/SeeAlso" itemprop="seealso">lognstat.据/code>