Modifikasi Uji Chi-Square untuk Uji Kesamaan Dua Populasi

Modifikasi Uji Chi-Square untuk Uji Kesamaan Dua Populasi Multinomial Terhadap Alternatif Terbatas yang Diurutkan

Ringkasan

Uji chi-square yang dimodifikasi untuk menguji persamaan dua populasi multinornial terhadap pemesanan Alternatif yang dibatasi dalam satu sampel dan dua kasus sampel yang dibuat. Hubungan antara Konsep ketergantungan yang disebut ketergantungan chi-square dan stokastik ordering yang telah ditetapkan. Tabulasi distribusi asimtotik dari statistik uji berdasarkan hipotesis nol diberikan. Simulasi digunakan untuk membandingkan kekuatan tes ini dengan kekuatan uji rasio likelihood  dari stokastik ordering dari dua populasi multinornial.

Kata Kunci : Uji chi-square yang dimodifikasi ; Distribusi asimtotik ; Uji satu sampel ; Uji dua sampel ; Ordering; Distribusi normal.

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Download CorelDraw Graphics Suite X7 Full Version

Download CorelDraw Graphics Suite X7 Full Version

      CorelDraw Graphics Suite X7 Full Version merupakan salah satu software terbaru dari CorelDraw yang dikenal sebagai salah satu program terbaik dalam bidang grafis dan desain. Anda para desainer pasti sudah kenal dengan aplikasi yang bernama CorelDraw ini bukan? Ya benar software yang satu ini benar-benar menjadi software yang paling banyak digunakan oleh orang-orang di seluruh dunia untuk mendesain berbagai macam benda dengan hasil yang sangat menawan. Fitur yang ditawarkan oleh program CorelDraw Graphics Suite X7 Full ini juga sangat lengkap dan sempurna untuk semua kebutuhan desain anda.

System Requirements

• Microsoft® Windows® 8 (32-bit or 64-bit editions), Microsoft® Windows® 7 (32-bit or 64-bit editions), all with latest service packs installed
• Note that Windows XP is not supported as it is more than ten years old. You need to update to Windows 7 or 8 as we have suggested to you in Migrate Your Operating System to 64-bit. In order to run the 64-bit version of CorelDRAW X7, you do need to have a 64-bit operating system.
• Intel Core 2 Duo or AMD Athlon 64
• 2 GB RAM
• 1 GB hard disk space (for installation without content). Up to 6GB needed to install extra content
• Mouse, tablet or multi-touch screen
• 1024 x 768 screen resolution (768 x 1024 on a Tablet PC)
• DVD drive
• Internet connection required for Membership and Subscription services, installing updates and access to some features, including the Content Exchange, QR code tools and ConceptShare

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New Nonparametric Rank Tests for Interactions in Factorial Designs with Repeated Measures

New Nonparametric Rank Tests for Interactions in Factorial Designs with Repeated Measures

Techniques have been proposed for the nonparametric analysis of interactions in factorial designs. They rank the observations and then perform parametric tests on ranks. The aligned rank tests belong to one class of these. Aligning implies that some estimate of a location (e.g., for the effect on a certain level of a given factor), such as the mean or median of the observation, is subtracted from each observation. These data, thus aligned according to the desired main and/or interaction effects, are then ranked and parametric tests are performed on these aligned ranks. A second class of such tests, named the new rank based methods, first rank the (not aligned) observations, and then the relative treatment effects are defined in reference to the distribution of the variables measured and estimated through elaborate calculations on these ranks.

The alignment methodology was introduced by Hodges and Lehmann (1962) and extended to two-way layouts by Sen (1968). McSweeney (1967) developed a test (M test) for interaction using the aligned ranks in the two-way layout. The aligned rank tests were publicized by: Hettmansperger (1984), Puri and Sen (1985), Sawilowsky (1990), and Higgins and Tashtoush (1994). More recently, Beasley and Zumbo (2003; 2009) added the aligned Friedman rank test for interactions in split-plot or repeated measures designs. Reviews of the aligned rank tests have been provided by Sawilowsky (1990), Higgins and Tashtoush (1994), Toothaker and Newman (1994), Kelley and Sawilowsky (1997), Richter and Payton (1999), Peterson (2002), and Rodriguez, Álvarez, and Remirez (2009). Salazar-Álvarez, Tercero-Gómez, Temblador-Pérez, and Conover (2014) recently reviewed nonparametric test for interactions. They overlooked the – in my opinion – important contributions by Beasley and Zumbo (2003; 2009) on designs with repeated measures. The general conclusion of these reviews was that the aligned rank tests are valid nonparametric alternatives for the parametric tests for the interaction, especially when sample sizes are small (Sawilowsky, 1990) or the departure from normality of the distribution of the observations is extreme (e.g., heavy tailed; Kelley & Sawilowsky, 1997).

Pioneers on the new rank-based methods were Akritas (1990), Akritas and Arnold (1994), Akritas, Arnold, and Brunner (1997), and Akritas and Brunner (1997). Brunner and Puri (2001) reviewed these methods.

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Journal Using Nonparametric Tests To Evaluate Traffic Forecasting Performance

Using Nonparametric Tests To Evaluate Traffic Forecasting Performance

Many models attempt to predict the behavior of a system. These models may be physical, mathematical,statistical, or simulation representations of the system. Within the transportation field, physical models can be scale models of the geographical area of interest, mathematical models can be queuing models, statistical models can be platoon dispersion models, and simulation models can be meso- or microsimulation models. These models may operate on cross-section data, which represent a snapshot of a system at a particular point in time, or on time series data, which represent the “movement” of a system through time.

If the appropriate model for the system is known, a dataset is used to calibrate the parameters in the model and then the model is applied. If the model is not known, then procedures are necessary to select from a range of models. As part of this selection process, a commonly used procedure is to split the dataset into two portions for training and testing purposes. The training portion, which is usually the larger, is used to calibrate the parameters in the model, and then the testing portion is used to assess the accuracy of the calibrated model in reproducing observed behavior. If the performance of the model with the testing dataset is deemed adequate, then the two datasets are pooled and the model recalibrated. Sometimes there is either insufficient data of acceptable quality to enable this partition to take place or no obvious way of dividing the datasets. In such cases, a with-replacement sampling approach may be adopted to construct the two datasets. To accurately assess without bias a model’s goodness-of-fit, the modeler must first determine the values of the calibration parameters and then assess the performance of that model

This paper, while incorporating forecasting models, is not concerned with a detailed study of the relative merits of these models, but with methods of assessing their ability to produce useable forecasts. In particular, this paper does not concern itself with the accepted iterative procedures of model identification, model estimation, and model diagnosis. It is assumed that these stages have been successfully completed and that the practitioner is now interested in how the model performs.

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Buku Elementary Survey Sampling

Elementary Survey Sampling

“Twenty-two percent of Americans doubt that the Holocaust ever occurred,” blasted a news statement in 1993. How could this be? How could so many doubt the occurrence of one of the most significant events of the twentieth century—and, perhaps, of all time? The answer lies in the poll, or survey, itself and in the difficulty of communicating with language, which always produces some degree of imprecision. The question actually asked by the Roper Organization was as follows:

Does it seem possible or does it seem impossible to you that the Nazi extermination of the Jews never happened?

Among the respondents, 22% said “it seemed possible” and another 12% said they did not know. Only 65% said it was “impossible it never happened.” The double negative in the question, it seems, confused the respondents to the point that they were not sure how to phrase their answers. Picking up on this point, the Gallup Organization conducted a follow-up poll that asked the question in more detailed form:

The term Holocaust usually refers to the killing of millions of Jews in Nazi death camps during World War II. In your opinion, did the Holocaust: definitely happen, probably happen, probably not happen, or definitely not happen?

Among the respondents to this poll, 83% said the Holocaust definitely happened and another 13% said it probably happened. Only 1% said it definitely did not happen. Quite a difference! In a separate poll, Gallup asked the Roper question again and found that 37% of the respondents said it seemed possible that it never happened.

Polls and surveys increasingly guide political, research, and business decisions, but they can be highly volatile and are little understood. An excellent study of current trends in collecting and using data is the book Tainted Truth: The Manipulation of Fact in America by Cynthia Crossen (1994, p. 14), in which she points out that

We are skeptical about statistical and factual information, but not as skeptical as we think. . . . we respect numbers and we cannot help believing them. Yet, more and more of the information we use to buy, elect, advise, acquit and heal has been created not to expand our knowledge but to sell a product or advance a cause. That’s what surveys do, they basically manufacture news.

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