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Kenapa harus beriklan di blog Tahu Gezrot?
1. Iklan Anda ditayangkan di 2 blog sekaligus, yaitu Opan-Sopana.blogspot.com dan LOWONGANKERJA.bengkeldata.com

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Analisis Data Statistik : Analisis Data Faktor-Faktor Konsumsi di Indonesia dengan Pendekatan ECM (Error Correction Model)

Tujuan dari penelitian ini adalah mengkaji pengaruh variabel pendapatan nasional, inflasi, suku bunga dan jumlah uang beredar terhadap konsumsi masyarakat yang digambarkan oleh variabel pengeluaran konsumsi mayarakat. Peneilitan ini dilakukan di Indonesia pada kurun waktu tahun 1988 sampai 2005. Metode yang digunakan adalah dengan menggunakan Pendekatan ECM (Error Correction Model).

Besarnya tingkat pengaruh variabel pendapatan nasional, inflasi, suku bunga deposito riil dan jumlah uang beredar terhadap pengeluaran konsumsi di Indonesia dalam jangka pendek yaitu 69,98 % sedangkan sisanya dipengaruhi oleh variabel lain di luar model regresi yang tidak diteliti dalam penelitian ini.

Dalam jangka panjang variasi variabel independent mampu pengaruhi variasi dependent sebesar 0,984057 menunjukkan bahwa variabel independent lebih mampu menjelaskan variabel dependent sebesar 98,40% dan sisanya dijelaskan oleh variabel lain diluar model yang tidak diikutsertakan dalam penelitian ini.

Tanda koefisien koreksi kesalahan sebesar 0,69 menunjukkan bahwa 0,69 ketidaksesuaian antara pengeluaran konsumsi (Y) yang aktual dengan yang diinginkan akan dieliminasi atau dihilangkan dalam satu tahun. Hasil dari uji asumsi klasik ternyata ditemukan masalah asumsi klasik yaitu multikolinearitas, sedangkan heteroskedasitas dan autokorelasi tidak ada masalah.

Untuk menghilangkan masalah multikolinearitas, dilakukan dengan cara menghilangkan variabel jumlah uang beredar. Sehingga diperoleh spesifikasi model penelitian yang dipakai adalah tepat dan mampu menjelaskan hubungan jangka pendek dan jangka panjang. Dengan demikian persamaan tersebut sudah sahih dan tidak ada alasan untuk ditolak.

Berikut kesimpulan yang dapat di ambil dari penelitian ini:

Besarnya pengaruh variabel pendapatan nasional, inflasi, suku bunga deposito riil dan jumlah uang beredar terhadap pengeluaran konsumsi di Indonesia dalam jangka pendek yaitu 75,12 % sisanya dipengaruhi oleh variabel lain di luar model regresi yang tidak diteliti dalam penelitian ini.

Variabel pendapatan nasional pada jangka pendek dan jangka panjang secara statistik positif dan signifikan, berarti pendapatan nasional berpengaruh terhadap pengeluaran konsumsi masyarakat di Indonesia periode 1988-2005.

Variabel tingkat inflasi pada jangka pendek secara statistik tidak signifikan, berarti tingkat inflasi tidak berpengaruh terhadap pengeluaran konsumsi masyarakat di Indonesia periode 1988-2005

Variabel suku bunga deposito dalam jangka pendek tidak berpengaruh terhadap pengeluaran konsumsi. Dalam jangka panjang mempunyai hubungan yang tidak signifikan yang artinya tidak berpengaruh terhadap pengeluaran konsumsi

Variabel jumlah uang beredar dalam jangka pendek tidak berpengaruh terhadap pengeluaran konsumsi.

Berdasarkan pengujian secara serempak dengan menggunakan uji F menunjukkan bahwa variabel independent secara bersama-sama mempengaruhi variabel dependent, artinya pendapatan nasional, inflasi, suku bunga deposito dan jumlah uang beredar berpengaruh secara bersama-sama terhadap pengeluaran konsumsi masyarakat Indonesia.

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Keywords: Penelitian, pendapatan, inflasi, suku bunga, uang, pengeluaran, konsumsi, ECM, deposito, regresi, independent, dependent, uji asumsi klasik, multikolinearitas, heteroskedasitas, autokorelasi, uji F

Olahdata Skripsi: Analisis data statistik / Analisa data statistik apa yang cocok untuk skripsi, tesis, disertasi, penelitian Anda ? ( Part I )

Apakah Anda sedang menyususn skripsi, tesis atau disertasi, atau penelitian? Dalam menyusun skripsi, tesis, disertasi, atau penelitian seringkali kita dihadapkan pada suatu permasalahan yang menjadi momok bagi mahasiswa/peneliti, yaitu analisis data / analisa data. Saya yakin bahwa Anda sebagai mahasiswa/peneliti memahami betul proses/tahapan dalam membuat skripsi, tesis atau disertasi, atau penelitian. Mulai dari tahapan mencari tema skripsi, tesis atau disertasi, atau penelitian, menetukan permasalahan, membuat kerangka pemikiran, membuat proposal, hingga menentukan lokasi penelitian. Nah ada satu lagi yang mungkin/bahkan ’lupa’ yaitu metode analisis data / analisa data yang digunakan. Apabila Anda belum memahami analisis data / analisa data –nya, maka Beta Consulting ( www.Bengkeldata.com / www.olah-data.com ) dengan senang hati akan membantu Anda untuk menentukan analisis data / analisa data yang cocok dengan skripsi, tesis atau disertasi, atau penelitian Anda.

Metode analisis data / analisa data apa yang cocok dengan skripsi, tesis, disertasi, atau penelitian Anda? Mari kita bahas contoh beberapa analisis data / analisa data yang cocok dengan skripsi, tesis, disertasi, atau penelitian. Yuukk Mariii :)

Pertama, misalkan skripsi, tesis, disertasi, atau penelitian Anda itu membahas bagaimana hubungan diantara dua variabel. Maka metode analisis data / analisa data yang digunakan adalah metode regresi sederhana. Apabila skripsi, tesis, disertasi, atau penelitian ada 3 variabel atau lebih, maka metode analisis data / analisa data yang digunakan adalah metode regresi berganda atau regresi multivariabel / regresi multivariat.

Tunggu dulu... Metode regresi sederhana / regresi berganda atau regresi multivariabel / regresi multivariat juga ada macamnya. Selain regresi sederhana, ada juga regresi logistik. Oke, sekali lagi Beta Consulting ( www.Bengkeldata.com / www.olah-data.com ) dengan senang hati akan membantu Anda untuk menentukan analisis data / analisa data yang cocok dengan skripsi, tesis atau disertasi, atau penelitian Anda.

Beta Consulting ( www.Bengkeldata.com / www.olah-data.com ) dengan senang hati dan didukung oleh tim yang berpengalaman, akan membantu Anda untuk menentukan analisis data / analisa data yang cocok dengan skripsi, tesis atau disertasi, atau penelitian Anda.

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Analisis Data Statistik : WHAT IS ECONOMETRICS?

Literally interpreted, econometrics means “economic measurement.” Although measurement is an important part of econometrics, the scope of econometrics is much broader, as can be seen from the following quotations: Econometrics, the result of a certain outlook on the role of economics, consists of the application of mathematical statistics to economic data to lend empirical support to the models constructed by mathematical economics and to obtain numerical results.

Econometrics may be defined as the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference.

Econometrics may be defined as the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena.
Econometrics is concerned with the empirical determination of economic laws.

The art of the econometrician consists in finding the set of assumptions that are both sufficiently specific and sufficiently realistic to allow him to take the best possible advantage of the data available to him.
Econometricians are a positive help in trying to dispel the poor public image of economics (quantitative or otherwise) as a subject in which empty boxes are opened by assuming the existence of can-openers to reveal contents which any ten economists will interpret in 11 ways.
The method of econometric research aims, essentially, at a conjunction of economic theory and actual measurements, using the theory and technique of statistical inference as a bridge pier.

METHODOLOGY OF ECONOMETRICS

How do econometricians proceed in their analysis of an economic problem?
That is, what is their methodology? Although there are several schools of thought on econometric methodology, we present here the traditional or classical methodology, which still dominates mpirical research in economics and other social and behavioral sciences.
Broadly speaking, traditional econometric methodology proceeds along the following lines:

1. Statement of theory or hypothesis.
2. Specification of the mathematical model of the theory
3. Specification of the statistical, or econometric, model
4. Obtaining the data
5. Estimation of the parameters of the econometric model
6. Hypothesis testing
7. Forecasting or prediction
8. Using the model for control or policy purposes.


For further information, please read a book: Basic Econometric 4th Ed. Gujarati.(2004)

Enjoy....

Probability : a Branch of Mathematics

Probability theory is the branch of mathematics concerned with analysis of random phenomena.^[1] The central objects of probability theory are random variables, stochastic processes, and events: mathematical abstractions of non-deterministic events or measured quantities that may either be single occurrences or evolve over time in an apparently random fashion. Although an individual coin toss or the roll of a die is a random event, if repeated many times the sequence of random events will exhibit certain statistical patterns, which can be studied and predicted. Two representative mathematical results describing such patterns are the law of large numbers and the central limit theorem.

As a mathematical foundation for statistics, probability theory is essential to many human activities that involve quantitative analysis of large sets of data. Methods of probability theory also apply to description of complex systems given only partial knowledge of their state, as in statistical mechanics. A great discovery of twentieth century physics was the probabilistic nature of physical phenomena at atomic scales, described in quantum mechanics.

History

The mathematical theory of probability has its roots in attempts to analyse games of chance by Gerolamo Cardano in the sixteenth century, and by Pierre de Fermat and Blaise Pascal in the seventeenth century (for example the "problem of points").

Initially, probability theory mainly considered discrete events, and its methods were mainly combinatorial. Eventually, analytical considerations compelled the incorporation of continuous variables into the theory. This culminated in modern probability theory, the foundations of which were laid by Andrey Nikolaevich Kolmogorov. Kolmogorov combined the notion of sample space, introduced by Richard von Mises, and measure theory and presented his axiom system for probability theory in 1933. Fairly quickly this became the undisputed axiomatic basis for modern probability theory.^[2]

Treatment

Most introductions to probability theory treat discrete probability distributions and continuous probability distributions separately. The more mathematically advanced measure theory based treatment of probability covers both the discrete, the continuous, any mix of these two and more.

Discrete probability distributions

Main article: Discrete probability distribution

Discrete probability theory deals with events that occur in countable sample spaces.

Examples: Throwing dice, experiments with decks of cards, and random walk.

Classical definition: Initially the probability of an event to occur was defined as number of cases favorable for the event, over the number of total outcomes possible in an equiprobable sample space.

For example, if the event is "occurrence of an even number when a die is rolled", the probability is given by , since 3 faces out of the 6 have even numbers and each face has the same probability of appearing.

Modern definition: The modern definition starts with a set called the sample space, which relates to the set of all possible outcomes in classical sense, denoted by . It is then assumed that for each element , an intrinsic "probability" value is attached, which satisfies the following properties:

1. 
2. 

That is, the probability function f(x) lies between zero and one for every value of x in the sample space Ω, and the sum of f(x) over all values x in the sample space Ω is exactly equal to 1. An event is defined as any subset of the sample space . The probability of the event defined as

So, the probability of the entire sample space is 1, and the probability of the null event is 0.

The function mapping a point in the sample space to the "probability" value is called a probability mass function abbreviated as pmf. The modern definition does not try to answer how probability mass functions are obtained; instead it builds a theory that assumes their existence.

Continuous probability distributions

Main article: Continuous probability distribution

Continuous probability theory deals with events that occur in a continuous sample space.

Classical definition: The classical definition breaks down when confronted with the continuous case. See Bertrand's paradox.

Modern definition: If the sample space is the real numbers (), then a function called the cumulative distribution function (or cdf) is assumed to exist, which gives for a random variable X. That is, F(x) returns the probability that X will be less than or equal to x.

The cdf must satisfy the following properties.

1. is a monotonically non-decreasing, right-continuous function;
2. 
3. 

If is differentiable, then the random variable X is said to have a probability density function or pdf or simply density

For a set , the probability of the random variable X being in is defined as

In case the probability density function exists, this can be written as

Whereas the pdf exists only for continuous random variables, the cdf exists for all random variables (including discrete random variables) that take values on

These concepts can be generalized for multidimensional cases on and other continuous sample spaces.

Measure theoretic probability theory

The raison d'être of the measure theoretic treatment of probability is that it unifies the discrete and the continuous, and makes the difference a question of which measure is used. Furthermore, it covers distributions that are neither discrete nor continuous.

An example of such distributions could be a mix of discrete and continuous distributions, for example, a random variable which is 0 with probability 1/2, and takes a value from random normal distribution with probability 1/2. It can still be studied to some extent by considering it to have a pdf of (δ[x] + φ(x)) / 2, where δ[x] is the Kronecker delta function.

Other distributions may not even be a mix, for example, the Cantor distribution has no positive probability for any single point, neither does it have a density. The modern approach to probability theory solves these problems using measure theory to define the probability space:

Given any set Ω, (also called sample space) and a σ-algebra on it, a measure P is called a probability measure if

1. is non-negative;
2. 

If is a Borel σ-algebra then there is a unique probability measure on for any cdf, and vice versa. The measure corresponding to a cdf is said to be induced by the cdf. This measure coincides with the pmf for discrete variables, and pdf for continuous variables, making the measure theoretic approach free of fallacies.

The probability of a set in the σ-algebra is defined as

where the integration is with respect to the measure induced by

Along with providing better understanding and unification of discrete and continuous probabilities, measure theoretic treatment also allows us to work on probabilities outside , as in the theory of stochastic processes. For example to study Brownian motion, probability is defined on a space of functions.

Probability distributions

Main article: Probability distributions

Certain random variables occur very often in probability theory because they well describe many natural or physical processes. Their distributions therefore have gained special importance in probability theory. Some fundamental discrete distributions are the discrete uniform, Bernoulli, binomial, negative binomial, Poisson and geometric distributions. Important continuous distributions include the continuous uniform, normal, exponential, gamma and beta distributions.

Convergence of random variables

Main article: Convergence of random variables

In probability theory, there are several notions of convergence for random variables. They are listed below in the order of strength, i.e., any subsequent notion of convergence in the list implies convergence according to all of the preceding notions.

Convergence in distribution: As the name implies, a sequence of random variables converges to the random variable in distribution if their respective cumulative distribution functions converge to the cumulative distribution function of , wherever is continuous.

Most common short hand notation:

Weak convergence: The sequence of random variables is said to converge towards the random variable weakly if for every ε > 0. Weak convergence is also called convergence in probability.

Most common short hand notation:

Strong convergence: The sequence of random variables is said to converge towards the random variable strongly if . Strong convergence is also known as almost sure convergence.

Most common short hand notation:

Intuitively, strong convergence is a stronger version of the weak convergence, and in both cases the random variables show an increasing correlation with . However, in case of convergence in distribution, the realized values of the random variables do not need to converge, and any possible correlation among them is immaterial.

Law of large numbers

Main article: Law of large numbers

Common intuition suggests that if a fair coin is tossed many times, then roughly half of the time it will turn up heads, and the other half it will turn up tails. Furthermore, the more often the coin is tossed, the more likely it should be that the ratio of the number of heads to the number of tails will approach unity. Modern probability provides a formal version of this intuitive idea, known as the law of large numbers. This law is remarkable because it is nowhere assumed in the foundations of probability theory, but instead emerges out of these foundations as a theorem. Since it links theoretically-derived probabilities to their actual frequency of occurrence in the real world, the law of large numbers is considered as a pillar in the history of statistical theory.[1]

The law of large numbers (LLN) states that the sample average of (independent and identically distributed random variables with finite expectation μ) converges towards the theoretical expectation μ.

It is in the different forms of convergence of random variables that separates the weak and the strong law of large numbers

It follows from LLN that if an event of probability p is observed repeatedly during independent experiments, the ratio of the observed frequency of that event to the total number of repetitions converges towards p.

Putting this in terms of random variables and LLN we have are independent Bernoulli random variables taking values 1 with probability p and 0 with probability 1-p. E(Y_i) = p for all i and it follows from LLN that converges to p almost surely.

Central limit theorem

Main article: Central limit theorem

The central limit theorem is the reason for the ubiquitous occurrence of the normal distribution in nature; it is one of the most celebrated theorems in probability and statistics.^{[citation needed]}

The theorem states that the average of many independent and identically distributed random variables with finite variance tends towards a normal distribution irrespective of the distribution followed by the original random variables. Formally, let be independent random variables with mean and variance 0.\," class="tex" v:shapes="_x0000_i1084" border="0" height="19" width="60">Then the sequence of random variables

converges in distribution to a standard normal random variable.

Bibliography

• Pierre Simon de Laplace (1812). Analytical Theory of Probability.

The first major treatise blending calculus with probability theory, originally in French: Théorie Analytique des Probabilités.

• Andrei Nikolajevich Kolmogorov (1950). Foundations of the Theory of Probability.

The modern measure-theoretic foundation of probability theory; the original German version (Grundbegriffe der Wahrscheinlichkeitrechnung) appeared in 1933.

• Patrick Billingsley (1979). Probability and Measure. New York, Toronto, London: John Wiley and Sons.

• Henk Tijms (2004). Understanding Probability. Cambridge Univ. Press.

A lively introduction to probability theory for the beginner.

• Gut, Allan (2005). Probability: A Graduate Course. Springer-Verlag. ISBN 0387228330.

Source: http://en.wikipedia.org/wiki/Probability_theory