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[[Image:Linear regression.svg|thumb|right|300px|Ilustrasi regresi linear pada suatu himpunan data. [[Analisis regresi]] adalah suatu bagian penting dalam statistika matematika.]]
[[Berkas:Linear regression.svg|jmpl|ka|300px|Ilustrasi regresi linear pada suatu himpunan data. [[Analisis regresi]] adalah suatu bagian penting dalam statistika matematika.]]


'''Statistika matematika''' ({{lang-en|Mathematical statistics}}) merupakan aplikasi [[matematika]] pada [[statistika]], yang asalnya dilahirkan sebagai suatu sains untuk negara (''state'') — kumpulan dan analisis fakta-fakta mengenai suatu negara: ekonomi, tanah, militer, populasi dan lain-lainnya. Teknik matematika yang digunakan di sini meliputi [[analisis matematis]], [[aljabar linear]], [[:en:stochastic analysis|analisis stokastik]], [[persamaan diferensial]], dan [[:en:measure-theoretic probability theory|teori probabilitas pengukuran-teoritis]].<ref>{{cite book|last=Lakshmikantham,|first=ed. by D. Kannan,... V.|title=Handbook of stochastic analysis and applications|date=2002|publisher=M. Dekker|location=New York|isbn=0824706609}}</ref><ref>{{cite book|last=Schervish|first=Mark J.|title=Theory of statistics|date=1995|publisher=Springer|location=New York|isbn=0387945466|edition=Corr. 2nd print.}}</ref>
'''Statistika matematika''' ({{lang-en|Mathematical statistics}}) adalah aplikasi [[matematika]] pada [[statistika]], yang asalnya dilahirkan sebagai suatu sains untuk negara (''state'')—kumpulan dan analisis fakta-fakta mengenai suatu negara: ekonomi, tanah, militer, populasi dan lain-lainnya. Teknik matematika yang digunakan di sini meliputi [[analisis matematis]], [[aljabar linear]], [[:en:stochastic analysis|analisis stokastik]], [[persamaan diferensial]], dan [[:en:measure-theoretic probability theory|teori probabilitas pengukuran-teoretis]].<ref>{{cite book|last=Lakshmikantham,|first=ed. by D. Kannan,... V.|title=Handbook of stochastic analysis and applications|date=2002|publisher=M. Dekker|location=New York|isbn=0824706609}}</ref><ref>{{cite book|last=Schervish|first=Mark J.|title=Theory of statistics|date=1995|publisher=Springer|location=New York|isbn=0387945466|edition=Corr. 2nd print.}}</ref>
<!--
<!--
==Introduction==
==Introduction==
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While the tools of data analysis work best on data from randomized studies, they are also applied to other kinds of data --- for example, from [[natural experiments]] and [[observational studies]], in which case the inference is dependent on the model chosen by the statistician, and so subjective.<ref>[[David A. Freedman (statistician)|Freedman, D.A.]] (2005) ''Statistical Models: Theory and Practice'', Cambridge University Press. ISBN 978-0-521-67105-7</ref>
While the tools of data analysis work best on data from randomized studies, they are also applied to other kinds of data --- for example, from [[natural experiments]] and [[observational studies]], in which case the inference is dependent on the model chosen by the statistician, and so subjective.<ref>[[David A. Freedman (statistician)|Freedman, D.A.]] (2005) ''Statistical Models: Theory and Practice'', Cambridge University Press. ISBN 978-0-521-67105-7</ref>
-->
Statistika matematika telah diilhami oleh [[:en:applied statistics|statistika terapan]] serta mengembangkan banyak prosedur pada penerapannya.


⚫
== Topik ==
Mathematical statistics has been inspired by and has extended many procedures in [[applied statistics]].

⚫
==Topik==
Berikut adalah sejumlah topik penting dalam statistika matematika:<ref>Hogg, R. V., A. Craig, and J. W. McKean. "Intro to Mathematical Statistics." (2005).</ref><ref>Larsen, Richard J. and Marx, Morris L. "An Introduction to Mathematical Statistics and Its Applications" (2012). Prentice Hall.</ref>
Berikut adalah sejumlah topik penting dalam statistika matematika:<ref>Hogg, R. V., A. Craig, and J. W. McKean. "Intro to Mathematical Statistics." (2005).</ref><ref>Larsen, Richard J. and Marx, Morris L. "An Introduction to Mathematical Statistics and Its Applications" (2012). Prentice Hall.</ref>
=== Sebaran probabilitas===
=== Sebaran probabilitas ===
{{main|Sebaran probabilitas}}
{{main|Sebaran probabilitas}}
[[Sebaran probabilitas]] atau "distribusi probabilitas" menggunakan [[peluang (matematika)|kemungkinan peluang atau probabilitas]] pada [[:en:measure (mathematics)|subset yang dapat diukur]] dari kemungkinan hasil suatu [[:en:Experiment (probability theory)|eksperimen]] yang bersifat acak (''random''), [[:en:Survey methodology|survey]], atau prosedur [[:en:statistical inference|inferensi statistik]].<!-- Examples are found in experiments whose [[sample space]] is non-numerical, where the distribution would be a [[categorical distribution]]; experiments whose sample space is encoded by discrete [[random variables]], where the distribution can be specified by a [[probability mass function]]; and experiments with sample spaces encoded by continuous random variables, where the distribution can be specified by a [[probability density function]]. More complex experiments, such as those involving [[stochastic processes]] defined in [[continuous time]], may demand the use of more general [[probability measure]]s.
[[Sebaran probabilitas]] atau "distribusi probabilitas" menggunakan [[peluang (matematika)|kemungkinan peluang atau probabilitas]] pada [[:en:measure (mathematics)|subset yang dapat diukur]] dari kemungkinan hasil suatu [[:en:Experiment (probability theory)|eksperimen]] yang bersifat acak (''random''), [[survei]], atau prosedur [[Statistika inferensi|inferensi statistik]].<!-- Examples are found in experiments whose [[sample space]] is non-numerical, where the distribution would be a [[categorical distribution]]; experiments whose sample space is encoded by discrete [[random variables]], where the distribution can be specified by a [[probability mass function]]; and experiments with sample spaces encoded by continuous random variables, where the distribution can be specified by a [[probability density function]]. More complex experiments, such as those involving [[stochastic processes]] defined in [[continuous time]], may demand the use of more general [[probability measure]]s.


A probability distribution can either be [[Univariate distribution|univariate]] or [[Multivariate distribution|multivariate]]. A univariate distribution gives the probabilities of a single [[random variable]] taking on various alternative values; a multivariate distribution (a joint probability distribution) gives the probabilities of a [[random vector]]—a set of two or more random variables—taking on various combinations of values. Important and commonly encountered univariate probability distributions include the [[binomial distribution]], the [[hypergeometric distribution]], and the [[normal distribution]]. The [[multivariate normal distribution]] is a commonly encountered multivariate distribution.
A probability distribution can either be [[Univariate distribution|univariate]] or [[Multivariate distribution|multivariate]]. A univariate distribution gives the probabilities of a single [[random variable]] taking on various alternative values; a multivariate distribution (a joint probability distribution) gives the probabilities of a [[random vector]]—a set of two or more random variables—taking on various combinations of values. Important and commonly encountered univariate probability distributions include the [[binomial distribution]], the [[hypergeometric distribution]], and the [[normal distribution]]. The [[multivariate normal distribution]] is a commonly encountered multivariate distribution.
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*[[Student's t distribution]], the distribution of the ratio of a [[standard normal]] variable and the square root of a scaled [[chi squared distribution|chi squared]] variable; useful for inference regarding the [[mean]] of normally distributed samples with unknown variance (see [[Student's t-test]])
*[[Student's t distribution]], the distribution of the ratio of a [[standard normal]] variable and the square root of a scaled [[chi squared distribution|chi squared]] variable; useful for inference regarding the [[mean]] of normally distributed samples with unknown variance (see [[Student's t-test]])
*[[Beta distribution]], for a single probability (real number between 0 and 1); conjugate to the [[Bernoulli distribution]] and [[binomial distribution]]
*[[Beta distribution]], for a single probability (real number between 0 and 1); conjugate to the [[Bernoulli distribution]] and [[binomial distribution]]
-->

=== Inferensi statistik ===
=== Inferensi statistik ===
{{main|Statistika inferensi}}
{{main|Statistika inferensi}}
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* a particular realization of the random process; i.e., a set of data.
* a particular realization of the random process; i.e., a set of data.
-->
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===Regresi===
=== Regresi ===
{{main|Analisis regresi}}
{{main|Analisis regresi}}


Dalam [[statistika]], '''analisis regresi''' adalah suatu proses statistik untuk memperkirakan hubungan antara variabel-variabel. Termasuk di dalamnya adalah teknik-tenik untuk pembuatan model dan analisis beberapa variabel, ketika fokusnya adalah hubungan antara suatu [:en:dependent variable|variabel dependen]] dan satu atau lebih [[:en:independent variable|variabel independen]].<!-- More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the [[conditional expectation]] of the dependent variable given the independent variables – that is, the [[average value]] of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a [[quantile]], or other [[location parameter]] of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a [[function (mathematics)|function]] of the independent variables called the '''regression function'''. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a [[probability distribution]].
Dalam [[statistika]], '''analisis regresi''' adalah suatu proses statistik untuk memperkirakan hubungan antara variabel-variabel. Termasuk di dalamnya adalah teknik-tenik untuk pembuatan model dan analisis beberapa variabel, ketika fokusnya adalah hubungan antara suatu [[:en:dependent variable|variabel dependen]] dan satu atau lebih [[:en:independent variable|variabel independen]].<!-- More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the [[conditional expectation]] of the dependent variable given the independent variables–that is, the [[average value]] of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a [[quantile]], or other [[location parameter]] of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a [[function (mathematics)|function]] of the independent variables called the '''regression function'''. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a [[probability distribution]].


Many techniques for carrying out regression analysis have been developed. Familiar methods such as [[linear regression]] and [[ordinary least squares]] regression are [[parametric statistics|parametric]], in that the regression function is defined in terms of a finite number of unknown [[parameter]]s that are estimated from the [[data]]. [[Nonparametric regression]] refers to techniques that allow the regression function to lie in a specified set of [[function (mathematics)|functions]], which may be [[dimension|infinite-dimensional]].
Many techniques for carrying out regression analysis have been developed. Familiar methods such as [[linear regression]] and [[ordinary least squares]] regression are [[parametric statistics|parametric]], in that the regression function is defined in terms of a finite number of unknown [[parameter]]s that are estimated from the [[data]]. [[Nonparametric regression]] refers to techniques that allow the regression function to lie in a specified set of [[function (mathematics)|functions]], which may be [[dimension|infinite-dimensional]].
-->
-->
===Statistika non-parametrik ===
=== Statistika non-parametrik ===
<!--{{main|Nonparametric statistics}}-->
<!--{{main|Nonparametric statistics}}-->
'''[[:en:Nonparametric statistics|Statistika non-parametrik]]''' adalah [[statistika]] yang tidak didasarkan atas familia [[:en:parametrization|berparameter]] dari [[sebaran probabilitas]]. Termasuk di dalamnya adalah baik [[:en:descriptive statistics|statistika deskriptif]] dan [[Statistika inferensi|inferensi]]. Parameter yang umum adalah rata-rata, variansi, dan lain-lain.<!-- [[parametric statistics]], nonparametric statistics make no assumptions about the [[probability distribution]]s of the variables being assessed.
'''[[Statistika nonparametrik]]''' adalah [[statistika]] yang tidak didasarkan atas familia [[:en:parametrization|berparameter]] dari [[sebaran probabilitas]]. Termasuk di dalamnya adalah baik [[:en:descriptive statistics|statistika deskriptif]] dan [[Statistika inferensi|inferensi]]. Parameter yang umum adalah rata-rata, variansi, dan lain-lain.<!-- [[parametric statistics]], nonparametric statistics make no assumptions about the [[probability distribution]]s of the variables being assessed.


Non-parametric methods are widely used for studying populations that take on a ranked order (such as movie reviews receiving one to four stars). The use of non-parametric methods may be necessary when data have a [[ranking]] but no clear numerical interpretation, such as when assessing [[preferences]]. In terms of [[level of measurement|levels of measurement]], non-parametric methods result in "ordinal" data.
Non-parametric methods are widely used for studying populations that take on a ranked order (such as movie reviews receiving one to four stars). The use of non-parametric methods may be necessary when data have a [[ranking]] but no clear numerical interpretation, such as when assessing [[preferences]]. In terms of [[level of measurement|levels of measurement]], non-parametric methods result in "ordinal" data.
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Another justification for the use of non-parametric methods is simplicity. In certain cases, even when the use of parametric methods is justified, non-parametric methods may be easier to use. Due both to this simplicity and to their greater robustness, non-parametric methods are seen by some statisticians as leaving less room for improper use and misunderstanding.
Another justification for the use of non-parametric methods is simplicity. In certain cases, even when the use of parametric methods is justified, non-parametric methods may be easier to use. Due both to this simplicity and to their greater robustness, non-parametric methods are seen by some statisticians as leaving less room for improper use and misunderstanding.
-->


== Statistika, matematika, dan statistika matematika ==
==Statistics, mathematics, and mathematical statistics==
Statistika matematika mempunyai ketumpangtindihan dengan bidang-bidang [[statistika]]. [[Statistikawan]] mempelajari dan memperbaiki prosedur statistika dengan matematika, dan riset statistika sering melahirkan pertanyaan matematis. Teori statistik tergantung pada [[Teori peluang|probabilitas atau peluang]] dan [[:en:optimal decision|teori keputusan]].
Mathematical statistics has substantial overlap with the discipline of [[statistics]]. [[Statisticians|Statistical theorists]] study and improve statistical procedures with mathematics, and statistical research often raises mathematical questions. Statistical theory relies on [[Probability theory|probability]] and [[optimal decision|decision theory]].


Matematikawan dan statistikawan seperti [[Gauss]], [[Laplace]], dan [[:en:Charles Sanders Peirce|C. S. Peirce]] menggunakan [[:en:optimal decision|teori keputusan]] dengan [[sebaran probabilitas]] dan [[:en:loss function|fungsi kehilangan]] (atau [[:en:utility function|fungsi kegunaan]]). Pendekatan berdasarkan teori keputusan terhadap inferensi statistik dihidupkan kembali oleh [[Abraham Wald]] dan para penerusnya,<ref>{{Cite book
Matematikawan dan statistikawan seperti [[Gauss]], [[Laplace]], dan [[Charles Sanders Peirce|C. S. Peirce]] menggunakan [[:en:optimal decision|teori keputusan]] dengan [[sebaran probabilitas]] dan [[fungsi kerugian]] (atau [[:en:utility function|fungsi kegunaan]]). Pendekatan berdasarkan [[teori keputusan]] terhadap inferensi statistik dihidupkan kembali oleh [[Abraham Wald]] dan para penerusnya,<ref>{{Cite book
| first = Abraham
|first = Abraham
| last = Wald |authorlink=Abraham Wald
|last = Wald
| title = Sequential analysis
|title = Sequential analysis
|url = https://archive.org/details/in.ernet.dli.2015.90255
| year = 1947
| publisher = John Wiley and Sons
|year = 1947
|publisher = John Wiley and Sons
| location = New York
|location = New York
| isbn = 0-471-91806-7
|isbn = 0-471-91806-7
| quote = See Dover reprint, 2004: ISBN 0-486-43912-7
|quote = See Dover reprint, 2004: ISBN 0-486-43912-7
}}</ref><ref>{{cite book
}}</ref><ref>{{cite book
|first=Abraham
|first=Abraham
|last=Wald
|last=Wald
|authorlink=Abraham Wald
|title=Statistical Decision Functions
|title=Statistical Decision Functions
|url=https://archive.org/details/statisticaldecis0000abra_t1e7
|year=1950
|year=1950
|publisher=John Wiley and Sons, New York
|publisher=John Wiley and Sons, New York
}}</ref><ref>{{cite book|last=Lehmann|first=Erich|authorlink=Erich Leo Lehmann
}}</ref><ref>{{cite book|last=Lehmann|first=Erich|authorlink=Erich Leo Lehmann
| title=Testing Statistical Hypotheses|year=1997 |edition=2nd
|title=Testing Statistical Hypotheses|url=https://archive.org/details/testingstatistic0000lehm_a7b0|year=1997|edition=2nd
|isbn=0-387-94919-4 }}</ref><ref>
|isbn=0-387-94919-4 }}</ref><ref>
{{cite book
{{cite book
| last1=Lehmann
|last1=Lehmann
| first1=Erich
|first1=Erich
| last2=Cassella
|last2=Cassella
| first2=George
|first2=George
⚫
|title=Theory of Point Estimation
| authorlink1=Erich Leo Lehmann
⚫
|year=1998|edition=2nd|isbn= 0-387-98502-6}}</ref><ref>
⚫
| title=Theory of Point Estimation
⚫
| year=1998 |edition=2nd|isbn= 0-387-98502-6}}</ref><ref>
{{cite book
{{cite book
| last1=Bickel|first1= Peter J.|last2=Doksum|first2=Kjell A.
|last1=Bickel|first1= Peter J.|last2=Doksum|first2=Kjell A.
| authorlink1=Peter J. Bickel
|authorlink1=Peter J. Bickel
|title=Mathematical Statistics: Basic and Selected Topics
|title=Mathematical Statistics: Basic and Selected Topics
|volume=1
|volume=1
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|first=Lucien
|first=Lucien
|last=Le Cam
|last=Le Cam
|authorlink=Lucien Le Cam
|title=Asymptotic Methods in Statistical Decision Theory
|title=Asymptotic Methods in Statistical Decision Theory
|url=https://archive.org/details/asymptoticmethod0000leca
|year=1986
|year=1986
|publisher=Springer-Verlag |isbn=0-387-96307-3
|publisher=Springer-Verlag|isbn=0-387-96307-3
}}</ref><ref>{{cite book
}}</ref><ref>{{cite book
|author=Liese, Friedrich and Miescke, Klaus-J.
|author=Liese, Friedrich and Miescke, Klaus-J.
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|publisher=Springer
|publisher=Springer
}}
}}
</ref> serta secara ekstensif menggunakan [[:en:scientific computing|komputing ilmiah]], [[analisis matematis|analisis]], dan [[:en:Optimization (mathematics)|optimisasi]]; untuk [[:en:design of experiments|desain eksperimen]], statistikawan menggunakan [[:en:Algebraic statistics|aljabar]] dan [[:en:Combinatorial design|kombinatorika]].
</ref> serta secara ekstensif menggunakan [[komputasi|komputasi ilmiah]], [[analisis matematis|analisis]], dan [[optimisasi]]; untuk [[perancangan percobaan]], statistikawan menggunakan [[:en:Algebraic statistics|aljabar]] dan [[:en:Combinatorial design|kombinatorika]].


==Lihat pula==
== Lihat pula ==
*{{en}} [[:en:Asymptotic theory (statistics)|Teori asimptotik]]
* {{en}} [[:en:Asymptotic theory (statistics)|Teori asimptotik]]
* [[Statistika]]
* [[Statistika]]
* [[Ukuran pemusatan data]]
* [[Ukuran penyebaran data]]


==Referensi==
== Referensi ==
<references/>
<references/>

== Bacaan lebih lanjut ==
* {{cite book|last= Kurnianingsih|first= Sri|authorlink=|coauthors=Kuntarti, Sulistiyono|title=Matematika SMA dan MA 2A Untuk Kelas XI Semester 1 Program IPA|year= 2007|publisher= Esis/Erlangga|location= Jakarta|id= ISBN 979-734-502-5 }} {{id icon}}
* {{cite book|last= Kurnianingsih|first= Sri|authorlink=|coauthors=Kuntarti, Sulistiyono|title=Matematika SMA dan MA 2A Untuk Kelas XI Semester 1 Program IPS|year= 2007|publisher= Esis/Erlangga|location= Jakarta|id= ISBN 979-734-563-7 }} {{id icon}}


== Pustaka tambahan ==
== Pustaka tambahan ==
* Borovkov, A. A. (1999). ''Mathematical Statistics''. CRC Press. ISBN 90-5699-018-7
* Borovkov, A. A. (1999). ''Mathematical Statistics''. [[CRC Press]]. ISBN 90-5699-018-7
* [http://www.math.uah.edu/stat/ Virtual Laboratories in Probability and Statistics (Univ. of Ala.-Huntsville)]
* [http://www.math.uah.edu/stat/ Virtual Laboratories in Probability and Statistics (Univ. of Ala.-Huntsville)]
* [http://www.trigonella.ch/statibot/english/ StatiBot], interactive online expert system on statistical tests.
* [http://www.trigonella.ch/statibot/english/ StatiBot], interactive online expert system on statistical tests.
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{{Statistika}}
{{Statistika}}
{{Bidang matematika}}
{{Bidang matematika}}

{{Authority control}}


{{DEFAULTSORT:Statistika matematika}}
{{DEFAULTSORT:Statistika matematika}}
[[Category:Matematika]]
[[Kategori:Matematika]]
[[Category:Statistika]]
[[Kategori:Statistika]]

Revisi terkini sejak 25 Februari 2024 18.48

Ilustrasi regresi linear pada suatu himpunan data. Analisis regresi adalah suatu bagian penting dalam statistika matematika.

Statistika matematika (bahasa Inggris: Mathematical statistics) adalah aplikasi matematika pada statistika, yang asalnya dilahirkan sebagai suatu sains untuk negara (state)—kumpulan dan analisis fakta-fakta mengenai suatu negara: ekonomi, tanah, militer, populasi dan lain-lainnya. Teknik matematika yang digunakan di sini meliputi analisis matematis, aljabar linear, analisis stokastik, persamaan diferensial, dan teori probabilitas pengukuran-teoretis.[1][2] Statistika matematika telah diilhami oleh statistika terapan serta mengembangkan banyak prosedur pada penerapannya.

Topik[sunting | sunting sumber]

Berikut adalah sejumlah topik penting dalam statistika matematika:[3][4]

Sebaran probabilitas[sunting | sunting sumber]

Artikel utama: Sebaran probabilitas

Sebaran probabilitas atau "distribusi probabilitas" menggunakan kemungkinan peluang atau probabilitas pada subset yang dapat diukur dari kemungkinan hasil suatu eksperimen yang bersifat acak (random), survei, atau prosedur inferensi statistik.

Inferensi statistik[sunting | sunting sumber]

Artikel utama: Statistika inferensi

Inferensi statistik adalah suatu proses untuk menarik kesimpulan dari data yang menjadi subjek variasi acak, misalnya, kesalahan observasi atau variasi pengambilan sampel.[5]

Regresi[sunting | sunting sumber]

Artikel utama: Analisis regresi

Dalam statistika, analisis regresi adalah suatu proses statistik untuk memperkirakan hubungan antara variabel-variabel. Termasuk di dalamnya adalah teknik-tenik untuk pembuatan model dan analisis beberapa variabel, ketika fokusnya adalah hubungan antara suatu variabel dependen dan satu atau lebih variabel independen.

Statistika non-parametrik[sunting | sunting sumber]

Statistika nonparametrik adalah statistika yang tidak didasarkan atas familia berparameter dari sebaran probabilitas. Termasuk di dalamnya adalah baik statistika deskriptif dan inferensi. Parameter yang umum adalah rata-rata, variansi, dan lain-lain.

Statistika, matematika, dan statistika matematika[sunting | sunting sumber]

Statistika matematika mempunyai ketumpangtindihan dengan bidang-bidang statistika. Statistikawan mempelajari dan memperbaiki prosedur statistika dengan matematika, dan riset statistika sering melahirkan pertanyaan matematis. Teori statistik tergantung pada probabilitas atau peluang dan teori keputusan.

Matematikawan dan statistikawan seperti Gauss, Laplace, dan C. S. Peirce menggunakan teori keputusan dengan sebaran probabilitas dan fungsi kerugian (atau fungsi kegunaan). Pendekatan berdasarkan teori keputusan terhadap inferensi statistik dihidupkan kembali oleh Abraham Wald dan para penerusnya,[6][7][8][9][10][11][12] serta secara ekstensif menggunakan komputasi ilmiah, analisis, dan optimisasi; untuk perancangan percobaan, statistikawan menggunakan aljabar dan kombinatorika.

Lihat pula[sunting | sunting sumber]

Referensi[sunting | sunting sumber]

  1. ^ Lakshmikantham,, ed. by D. Kannan,... V. (2002). Handbook of stochastic analysis and applications. New York: M. Dekker. ISBN 0824706609. 
  2. ^ Schervish, Mark J. (1995). Theory of statistics (edisi ke-Corr. 2nd print.). New York: Springer. ISBN 0387945466. 
  3. ^ Hogg, R. V., A. Craig, and J. W. McKean. "Intro to Mathematical Statistics." (2005).
  4. ^ Larsen, Richard J. and Marx, Morris L. "An Introduction to Mathematical Statistics and Its Applications" (2012). Prentice Hall.
  5. ^ Upton, G., Cook, I. (2008) Oxford Dictionary of Statistics, OUP. ISBN 978-0-19-954145-4
  6. ^ Wald, Abraham (1947). Sequential analysis. New York: John Wiley and Sons. ISBN 0-471-91806-7. See Dover reprint, 2004: ISBN 0-486-43912-7 
  7. ^ Wald, Abraham (1950). Statistical Decision Functions. John Wiley and Sons, New York. 
  8. ^ Lehmann, Erich (1997). Testing Statistical Hypotheses (edisi ke-2nd). ISBN 0-387-94919-4. 
  9. ^ Lehmann, Erich; Cassella, George (1998). Theory of Point Estimation (edisi ke-2nd). ISBN 0-387-98502-6. 
  10. ^ Bickel, Peter J.; Doksum, Kjell A. (2001). Mathematical Statistics: Basic and Selected Topics. 1 (edisi ke-Second (updated printing 2007)). Pearson Prentice-Hall. 
  11. ^ Le Cam, Lucien (1986). Asymptotic Methods in Statistical Decision Theory. Springer-Verlag. ISBN 0-387-96307-3. 
  12. ^ Liese, Friedrich and Miescke, Klaus-J. (2008). Statistical Decision Theory: Estimation, Testing, and Selection. Springer. 

Bacaan lebih lanjut[sunting | sunting sumber]

  • Kurnianingsih, Sri (2007). Matematika SMA dan MA 2A Untuk Kelas XI Semester 1 Program IPA. Jakarta: Esis/Erlangga. ISBN 979-734-502-5.  (Indonesia)
  • Kurnianingsih, Sri (2007). Matematika SMA dan MA 2A Untuk Kelas XI Semester 1 Program IPS. Jakarta: Esis/Erlangga. ISBN 979-734-563-7.  (Indonesia)

Pustaka tambahan[sunting | sunting sumber]

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