Statistika Inferensia untuk Sampel Besar...2019/09/05 · Statistika Inferensia untuk Sampel Besar...
Transcript of Statistika Inferensia untuk Sampel Besar...2019/09/05 · Statistika Inferensia untuk Sampel Besar...
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Dr. Kusman Sadik, M.Si
Sekolah Pascasarjana Departemen Statistika IPB
Semester Ganjil 2019/2020
Analisis Statistika (STK511)
IPB University─ Bogor Indonesia ─ Inspiring Innovation with Integrity
Statistika Inferensia untuk Sampel Besar (Drawing Inferences from Large Samples)
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Inferences are generalizations about a population that
are made on the basis of a sample collected from the
population.
For instance, a researcher interested in the growth of
pine trees plants 40 seedlings.
The heights of these 40 plants would be a sample that
is hopefully representative of the population consisting
of all current and future seedlings that could be
planted.
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Statistical inference deals with drawing conclusions
about population parameters from an analysis of the
sample data.
The two most important types of inferences are:
(1) estimation of parameters; and (2) testing of
statistical hypotheses.
The true value of a parameter is an unknown constant
that can be correctly ascertained only by an
exhaustive study of the population, if indeed that were
possible.3
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Our objective may be to obtain a guess or an estimate
of the unknown true value along with a determination
of its accuracy. This type of inference is called
estimation of parameters.
An alternative objective may be to examine whether
the sample data support or contradict the investigator
’s conjecture about the true value of the parameter.
This latter type of inference is called testing of
statistical hypotheses.
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The object of point estimation is to calculate, from the
sample data, a single number that is likely to be close to
the unknown value of the parameter.
The available information is assumed to be in the form
of a random sample of size n taken from the population.
We wish to formulate a statistic such that its value
computed from the sample data would reflect the value
of the population parameter as closely as possible.
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A statistic intended for estimating a parameter is
called a point estimator, or simply an estimator
(penduga).
The standard deviation (simpangan baku) of an
estimator is called its standard error (galat baku)
SE.
The standard deviation, alternatively called the
standard error of the estimator, provides information
about its variability.
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Populasi parameter : μ dan σ2
Sampel statistik : ത𝑋 dan 𝑆2
ത𝑋 adalah penduga (estimator) bagi μ
𝑆2 adalah penduga (estimator) bagi σ2
Galat baku (standard error) bagi ത𝑋 SE( ത𝑋) = σ
𝑛
100(1 – α)% error margin bagi ത𝑋 (𝑧𝛼/2)σ
𝑛
Jika σ tidak diketahui dapat diganti dengan S
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Z0.05 maknanya adalah P(Z > k) = 0.05
Karena P(Z > k) = 0.05 maka P(Z ≤ k) = 0.95
akan diperoleh k = 1.645
Sehingga Z0.05 = 1.64511
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Z0.025 P(Z > k) = 0.025 P(Z ≤ k) = 0.975 k = 1.96
Sehingga Z0.025 = 1.96
Contoh untuk beberapa nilai α :
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Johnson (Example 3), hlm. 301
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For point estimation, a single number lies in the
forefront even though a standard error is attached.
Instead, it is often more desirable to produce an
interval of values that is likely to contain the true
value of the parameter.
We insist that before sampling the proposed interval
will contain the true value with a specified high
probability. This probability, called the level of
confidence, is typically taken as .90, .95, or .99.
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Jadi SK(95%): 2.39 ≤ μ ≤ 3.01
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Johnson (Example 6), hlm. 309
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The goal of testing statistical hypotheses is to determine
if a claim or conjecture about some feature of the
population, a parameter, is strongly supported by the
information obtained from the sample data.
Here we illustrate the testing of hypotheses concerning a
population mean μ.
The available data will be assumed to be a random
sample of size n from the population of interest.
The sample size n will be large ( n > 30 for a rule of
thumb ).
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In the language of statistics, the claim or the research
hypothesis that we wish to establish is called the
alternative hypothesis H1.
The opposite statement, one that nullifies the research
hypothesis, is called the null hypothesis H0
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𝑍hit =ത𝑋 − 𝜇0
𝑆/ 𝑛
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Tolak H0 jikap-value < α
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Johnson (Example 9), hlm. 323-325
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Johnson (Example 9), hlm. 323-325
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Selesaikan contoh soal ini menggunakan Minitab, kemudian
bandingkan hasilnya dengan jawaban sebelumnya.
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Johnson (Example 10), hlm. 323-325
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Johnson (Example 13), hlm. 333-335
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Johnson (Example 14), hlm. 333-335
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Johnson (Exercise 8.4), hlm. 304
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Johnson (Exercise 8.14), hlm. 304
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Johnson (Exercise 8.22), hlm. 313
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Johnson (Exercise 8.22), hlm. 313
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Selesaikan soal ini menggunakan Minitab, kemudian
bandingkan hasilnya dengan jawaban Anda pada No.3 di
atas.
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Johnson (Exercise 8.55), hlm. 328
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Johnson (Exercise 8.74), hlm. 337
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Johnson (Exercise 8.74), hlm. 337
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Selesaikan soal ini menggunakan Minitab, kemudian
bandingkan hasilnya dengan jawaban Anda pada No.6 di
atas.
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Johnson, R.A. and Bhattacharyya, G.K. 2010.
Statistics, Principles and Methods 6th. John Wiley
& Sons, Inc., New York.
Montgomery, D.C. 2013. Design and Analysis of
Experiments 8th. John Wiley & Sons, Inc.,
Canada.
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Bisa di-download di
kusmansadik.wordpress.com
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