The characteristics of breast cancer research using big data analysis techniques: recurrence gene study or utilization behavior of hospital but sample statistic error

Breast cancer is a major cause of female death, and various big data analysis methods have been applied to breast cancer. This study lists cases in which big data analysis was applied to breast cancer research. In addition, statistics and percentages from each specific sample were proposed. However, research on the use of big data has a blind spot that relies on sample characteristics. Therefore, before sampling big data, statistical inference should be discussed more precisely through pre-examination and sample statistical errors should be reduced by professional statistical evaluation of the analysis method.

A review of basic statistics and terms.

This chapter provides a basic framework of terms and general statistics. A number of basic terms such as population, sample, statistic, parameter, estimate, random, deduction and induction, accuracy and precision, effect versus affect, discrete and continuous variables among others, have been defined and some of the most commonly referenced test statistics have been presented.

Predictive Power of Adaptive Candlestick Patterns in Forex Market. Eurusd Case

The Efficient Market Hypothesis (EMH) states that all available information is immediately reflected in the price of any asset or financial instrument, so that it is impossible to predict its future values, making it follow a pure stochastic process. Among all financial markets, FOREX is usually addressed as one of the most efficient. This paper tests the efficiency of the EURUSD pair taking only into consideration the price itself. A novel categorical classification, based on adaptive criteria, of all possible single candlestick patterns is presented. The predictive power of candlestick patterns is evaluated from a statistical inference approach, where the mean of the average returns of the strategies in out-of-sample historical data is taken as sample statistic. No net positive average returns are found in any case after taking into account transaction costs. More complex candlestick patterns are considered feeding supervised learning systems with the information of past bars. No edge is found even in the case of considering the information of up to 24 preceding candlesticks.

The Statistical Model

This chapter covers some of the basics of time series statistics as they relate to our model. An analytic solution to our model is derived with log preferences. Then the chapter discusses how to program up the model and compute sample statistic from model simulations. Finally it discusses Monte Carlo simulations to determine the sampling distributions for statistics from our model simulations.

The impacts of hydraulic clamming in shallow water and the importance of incorporating anthropogenic disturbances into habitat assessments

Hydraulic dredging for shellfish is known to create some of the highest levels of disturbance, affecting the benthic microfaunal community and the physical characteristics of the substrate. Properly conducted benthic habitat assessments are complex and time consuming, resulting in assessments not being conducted increasing the uncertainty in post impact studies. Hydraulic dredging for Atlantic surfclams (Spisula soldidissima) took place at Herring Cove, Massachusetts in the winter of 2014–2015 resulting in areas of high impact disturbance of the seafloor. Surveys conducted in the summer of 2015 included hydroacoustics, benthic invertebrate sampling, video, and grain size analysis for the creation of a habitat map of Herring Cove. The four habitats (A–D) identified were a mix of sand, shell, cobble, algae, and eelgrass. Habitat type “D” is a mix of sand, algae and cobble material and occurred at 12 of 18 stations. These 12 stations were distributed across areas of “high” (n = 4), “low” (n = 2), and “no” (n = 6) hydraulic dredge disturbance. Once habitat was accounted for, benthic invertebrate community structure varied significantly (Analysis of similarity; significance level of sample statistic: 0.3%) between areas of “high”, “low” to “no” disturbance. Areas of “low” to “no” dredge track coverage contained high abundances of bivalves, echinoderms, and isopods, whereas highly disturbed areas had highest abundances of polychaetes and oligochaetes. Future mapping efforts, especially surveys with biological components, need to include and quantify the level, type and spatial distribution of anthropogenic alterations. More attention should be given to “reference maps” instead of “baseline maps”. The latter of which omits to acknowledge pre-existing anthropogenic disturbances and has the potential to skew monitoring of restoration and management efforts.

PENGARUH MEDIA PEMBELAJARAN POHON ANGKA TERHADAP KEMAMPUAN MENGENAL KONSEP BILANGAN PADA ANAK USIA 5-6 TAHUN DI TKK SATAP St. THERESIA WOLOMELI KABUPATEN NGADA

Penelitian ini bertujuan untuk mengetahui pengaruh hasil media pembelajaran pohon angka terhadap kemampuan mengenal konsep bilangan sebelum ada perlakuan dan sesudah ada perlakuan pada TKK Satap St. Theresia Wolomeli. Penelitian ini merupakan penelitian “Pre-Experimental jenis The One Group Pretest-Posttest Design”. Populasi dalam penelitian adalah anak TKK Satap St. Theresia Wolomeli berjumlah 22 anak dengan jumlah pria 7 dan wanita 15. Pengujian hipotesis menggunakan program SPSS 16.00 from windows pada kolom y nilai t-test 25.084 > 1,98 (dengan db n1 – 1) (22-1=21, taraf sig. 5%), dan nilai signifikansi = 0.000 < 0,05 maka keputusan H0 ditolak dan H1 diterima. Dari rata-rata hasil media pembelajaran pohon angka pada tabel One-Sample Statistic variabel y lebih besar dari variabel X (14.86 > 8.000). Berdasarkan hasil analisis data tersebut dinyatakan ada pengaruh media pembelajaran pohon angka terhadap kemampuan mengenal konsep bilangan pada anak usia 5-6 tahun.

Estudio comparativo de la actitud hacia la estadística en estudiantes de administración de una universidad privada de Lima Este, 2016

El propósito de este estudio consistió en reportar los resultados de la aplicación de la escala de actitud hacia la estadística (EAE) a una muestra no aleatoria de 92 estudiantes de la Escuela Profesional de administración de una universidad privada de Lima Este correspondiente a los turnos mañana, tarde y noche, en relación a las siguientes dimensiones; valor que le dan a la estadística, nivel de dificultad percibida, el afecto hacia la estadística y la competencia cognitiva de los estudiantes, que cursaban la materia de estadística. Alrededor de un 60% manifiestan una actitud favorable o muy favorable hacia la estadística y solo un 4.3% desfavorable. No se encontraron diferencias significativas en el puntaje total de la escala de los estudiantes de esta muestra en función del turno de estudio: mañana, tarde y noche. ABSTRACT The purpose of this research lies in to report the results of Attitude Scale Application to (EAE) Random Sample Statistic of 92 students of Business School from Lima private university corresponding to morning, afternoon and evening shifts respect to the following dimensions: Statistic value, Perceived difficulty level, Statistic Affection, Cognitive Competence Stastistic Students. Around 60% show favorable attitude to statistic course but not 4.3%. There is no meaningful differences in the Students Scale Final Results who attend in morning, afternoon and evening shifts.

Effective Optimization of Statistical Decisions for Age Replacement Problems under Parametric Uncertainty

In this chapter, an innovative model for age replacement is proposed. The costs included in the age replacement model are not assumed to be constants. For effective optimization of statistical decisions for age replacement problems under parametric uncertainty, based on a past random sample of lifetimes, the pivotal quantity averaging (PQA) approach is suggested. The PQA approach represents a simple and computationally attractive statistical technique. In this case, the transition from the original problem to the equivalent transformed problem (in terms of pivotal quantities and ancillary factors) is carried out via invariant embedding a sample statistic in the original problem. The approach allows one to eliminate unknown parameters from the problem and to find the better decision rules, which have smaller risk than any of the well-known decision rules. Unlike the Bayesian approach, the proposed approach is independent of the choice of priors. For illustration, numerical examples are given.