fullpacracing

STATISTIKA
UJI NORMALITAS
UJI HOMOGENITAS
UJI LINEARITAS
DAN KEBERARTIAN MODEL REGRESI DAN KORELASI

For a long time, one of my dreams was to describe the nature of uncertainty axiomatically, and it looks like I've finally done it in my co∼eventum mechanics! Now it remains for me to explain to everyone the co∼eventum mechanics in the... more

For a long time, one of my dreams was to describe the nature of uncertainty axiomatically, and it looks like I've finally done it in my co∼eventum mechanics! Now it remains for me to explain to everyone the co∼eventum mechanics in the most approachable way. This is what I'm trying to do in this work. The co∼eventum mechanics is another name for the co∼event theory, i.e., for the theory of experience and chance which I axiomatized in 2016 [1, 2]. In my opinion, this name best reflects the co∼event-based idea of the new dual theory of uncertainty, which combines the probability theory as a theory of chance, with its dual half, the believability theory as a theory of experience. In addition, I like this new name indicates a direct connection between the co∼event theory and quantum mechanics, which is intended for the physical explanation and description of the conict between quantum observers and quantum observations [4]. Since my theory of uncertainty satises the Kolmogorov axioms of probability theory, to explain this co∼eventum mechanics I will use a way analogous to the already tested one, which explains the theory of probability as a theory of chance describing the results of a random experiment. The simplest example of a random experiment in probability theory is the ' tossing a coin '. Therefore, I decided to use this the simplest random experiment itself, as well as the two its analogies: the ' 'flipping a coin ' and the ' spinning a coin ' to explain the co∼eventum mechanics, which describes the results of a combined experienced random experiment. I would like to resort to the usual for the probability theory ' coin-based ' analogy to explain (and first of all for myself) the logic of the co∼eventum mechanics as a logic of experience and chance. Of course, this analogy one may seem strange if not crazy. But I did not come up with a better way of tying the explanations of the logic of the co∼eventum mechanics to the coin-based explanations that are commonly used in probability theory to explain at least for myself the logic of the chance through a simple visual ' coin-based ' model that clarifies what occurs as a result of a combined experienced random experiment in which the experience of observer faces the chance of observation. I hope this analogy can be useful not only for me in understanding the co∼eventum mechanics.

You yourself, or what is the same, your experience is such ``coin' that, while you aren't questioned, it rotates all the time in ``free flight'. And only when you answer the question the ``coin' falls on one of the sides: ``Yes' or... more

You yourself, or what is the same, your experience is such ``coin' that, while you aren't questioned, it rotates all the time in ``free flight'. And only when you answer the question the ``coin' falls on one of the sides: ``Yes' or ``No' with the believability that your experience tells you.

Eventology of multivariate statistics Eventology and mathematical eventology Philosophical eventology and philosophy of probability Practical eventology Eventology of safety Eventological economics and psychology Mathematics in the... more

Eventology of multivariate statistics
Eventology and mathematical eventology
Philosophical eventology and philosophy of probability
Practical eventology
Eventology of safety
Eventological economics and psychology
Mathematics in the humanities, socio-economic and natural sciences
Financial and actuarial mathematics
Multivariate statistical analysis
Multivariate complex analysis
Decision-making under risk and uncertainty
Risk measurement and risk models
Theory of fuzzy events and generalized theory of uncertainty
System analysis and events management
EEC'2016 ~ workshop on axiomatizing experience and chance, and Hilbert's sixth problem
With topics from quantum physics, probability and believability to economics, sociology and psychology, the workshop will be intended for an interdisciplinary discussion on mathematical theories of experience and chance. Topics of discussion include the results, thoughts and ideas on axiomatization of the eventological theory of experience and chance in the framework of the decision of Hilbert sixth problem.
Eventology of experience and chance
Beleivability theory and statistics of experience
Probability theory and statistics of chance
Axiomatizing experience and chance

Imagine that you are conducting an experiment, as a result of which the event occurs or does not occur. You must determine whether this event occurred or not. Your experience tells you that many times in the past you, being exactly in the... more

Imagine that you are conducting an experiment, as a result of which the event occurs or does not occur. You must determine whether this event occurred or not. Your experience tells you that many times in the past you, being exactly in the same circumstances, only in half of the cases came to the conclusion this event happened. In this situation I would say your believability of this event happened is equaled to the half. But you can't answer in this way. You are required to answer only either ``Yes' or ``No'. What would you do in this situation? You must not think such situations are big rare. Refresh your memory what are you doing every day under uncertainty? Every day you are doing only it and no more. The question arises how you do this action?
If I say you ``toss a coin in your mind, or in your thoughts' I'll do the mistake. The point is every one of us has an ability to make the believabilistic choice. You yourself, or what is the same, your experience is such ``coin' that, while you aren't questioned, it rotates all the time in ``free flight'. And only when you answer the question the ``coin' falls on one of the sides: ``Yes' or ``No' with the believability that your experience tells you.
Therefore, to denote this action, I will choose the new expression ``tossing an experienced coin' in an experienced experiment that becomes dual to the usual expression ``tossing a coin' in a random experiment.

The logic of uncertainty is not the logic of experience and as well as it is not the logic of chance. It is the logic of experience and chance. Experience and chance are two inseparable poles. These are two dual reflections of one... more

The logic of uncertainty is not the logic of experience and as well as it is not the logic of chance. It is the logic of experience and chance. Experience and chance are two inseparable poles. These are two dual reflections of one essence, which is called co∼event. The theory of experience and chance is the theory of co∼events. To study the co∼events, it is not enough to study the experience and to study the chance. For this, it is necessary to study the experience and chance as a single entire, a co∼event. In other words, it is necessary to study their interaction within a co∼event. The new co∼event axiomatics and the theory of co∼events following from it were created precisely for these purposes. In this work, I am going to demonstrate the effectiveness of the new theory of co∼events in a studying the logic of uncertainty. I will do this by the example of a co∼event splitting of the logic of the Bayesian scheme, which has a long history of fierce debates between Bayesionists and frequentists. I hope the logic of the theory of experience and chance will make its modest contribution to the application of these old dual debaters.
Keywords: Eventology, event, probability, probability theory, Kolmogorov’s axiomatics, experience, chance, cause, consequence, co∼event, set of co∼events, bra-event, set of bra-events, ket-event, set of ket-events, believability, certainty, believability theory, certainty theory, theory of co∼events, theory of experience and chance, co∼event dualism, co∼event axiomatics, logic of uncertainty, logic of experience and chance, logic of cause and consequence, logic of the past and the future, Bayesian scheme.

Distribusi sampling adalah distribusi probabilitas dari suatu statistik (yaitu semua pengamatan yang mungkin dari statistik untuk sampel dari suatu ukuran sampel yang diberikan).

Sepakbola adalah salah satu olahraga terpopuler di dunia saat ini. Seluruh lapisan masyarakat mulai dari anak-anak hingga orang tua mengenal permainan olahraga yang satu ini. Tidak heran jika klub-klub dan para pemain sepakbola sering... more

Sepakbola adalah salah satu olahraga terpopuler di dunia saat ini. Seluruh lapisan masyarakat mulai dari anak-anak hingga orang tua mengenal permainan olahraga yang satu ini. Tidak heran jika klub-klub dan para pemain sepakbola sering menjadi sorotan di media. Eropa memiliki sebuah liga antarklub paling kompetitif dan terpopuler saat ini, yaitu Liga Champions, sebuah liga yang mempertemukan klub-klub terbaik di seluruh daratan Eropa. Liga Champions Eropa 2015 saat ini telah menyelesaikan babak penyisihan grup dengan meloloskan 16 klub ke babak perdelapan final. Gol-gol yang tercipta pada babak 16 besar tersebut adalah yang paling ditunggu-tunggu oleh semua orang. Sejumlah orang terkadang menebak-nebak jumlah gol yang akan terjadi, namun jika hanya menggunakan tebakan biasa saja, hasilnya tidak akan efektif.

Variabel acak adalah keluaran dari suatu percobaan yang dinyatakan secara numerik. Notasi variabel acak dinyatakan dalam huruf besar (misal X, Y, W) dan suatu nilai dari variabel acak tersebut ditulis dengan huruf kecil (misal x, y, w).... more

Variabel acak adalah keluaran dari suatu percobaan yang dinyatakan secara numerik. Notasi variabel acak dinyatakan dalam huruf besar (misal X, Y, W) dan suatu nilai dari variabel acak tersebut ditulis dengan huruf kecil (misal x, y, w). Contoh: Var.acak dinyatakan dengan fungsi X = X (s) = s 2 ; s adalah bilangan real, elemen-elemen pada sample-space S, yaitu himpunan {0 < s < 12}; maka titik-titik pada S akan dipetakan pada himpunan real {0 < x < 144} Syarat suatu fungsi X sebagai variable acak ialah: 1. Himpunan {X < x} merupakan suatu event untuk semua nilai real x-∞< x < ∞ 2. P{X =-∞} = 0 dan P{X = ∞}= 0 Variabel acak diskrit hanya memiliki nilai-nilai diskrit (countable), sedangkan variabel acak kontinyu mempunyai nilai-nilai (manapun) pada suatu range yang kontinyu.