英國(guó)皇家統(tǒng)計(jì)學(xué)會(huì)期刊C輯(應(yīng)用統(tǒng)計(jì)學(xué))是一份享譽(yù)國(guó)際的期刊，為國(guó)內(nèi)外的統(tǒng)計(jì)學(xué)家所著。該雜志關(guān)注的是通過(guò)調(diào)整或發(fā)展方法，或通過(guò)展示新的或現(xiàn)有的統(tǒng)計(jì)方法的適當(dāng)應(yīng)用來(lái)處理現(xiàn)實(shí)生活中統(tǒng)計(jì)問(wèn)題的新的解決辦法的論文。因此，在他們的核心期刊上的論文是由各種各樣的例子和統(tǒng)計(jì)數(shù)據(jù)激勵(lì)的。本課程內(nèi)容涵蓋所有跨學(xué)科領(lǐng)域，例如農(nóng)業(yè)、遺傳學(xué)、工業(yè)、醫(yī)學(xué)和物理科學(xué)的應(yīng)用，以及關(guān)于設(shè)計(jì)問(wèn)題的論文(例如與實(shí)驗(yàn)、調(diào)查或觀察性研究有關(guān)的論文)。對(duì)統(tǒng)計(jì)方法的深刻理解并不是欣賞其內(nèi)容的必要條件。雖然由實(shí)際例子推動(dòng)的統(tǒng)計(jì)計(jì)算發(fā)展的論文在其范圍內(nèi)，但該雜志不關(guān)心簡(jiǎn)單的數(shù)值插圖或模擬研究。C系列的重點(diǎn)是實(shí)際統(tǒng)計(jì)分析的個(gè)案研究。目標(biāo)和范圍英國(guó)皇家統(tǒng)計(jì)學(xué)會(huì)的期刊C系列(應(yīng)用統(tǒng)計(jì)學(xué))促進(jìn)了針對(duì)現(xiàn)實(shí)生活問(wèn)題的統(tǒng)計(jì)方法的論文。應(yīng)用應(yīng)該是論文的中心，而不是說(shuō)明性的，以激勵(lì)工作和證明任何方法的發(fā)展。所有的論文都應(yīng)該對(duì)實(shí)質(zhì)性的應(yīng)用進(jìn)行充分的描述，并為任何新理論提供理由。個(gè)案研究可能特別適當(dāng)，并應(yīng)包括一些背景細(xì)節(jié)，但也應(yīng)作出新的統(tǒng)計(jì)貢獻(xiàn)，例如調(diào)整或發(fā)展方法，或證明適當(dāng)應(yīng)用新的或現(xiàn)有的統(tǒng)計(jì)方法來(lái)解決具有挑戰(zhàn)性的應(yīng)用問(wèn)題。論文描述了跨學(xué)科工作尤其受歡迎,那些給有趣的新穎應(yīng)用現(xiàn)有的方法或提供新的見(jiàn)解的實(shí)際應(yīng)用方法,和論文解釋創(chuàng)新分析通用的應(yīng)用問(wèn)題,但不一定是專注于一個(gè)特定的應(yīng)用程序也有一個(gè)地方在c系列短通信也可能是適當(dāng)?shù)摹２灰哉嬲膽?yīng)用為動(dòng)機(jī)的方法學(xué)論文是不能接受的;也不是只有簡(jiǎn)單的數(shù)值說(shuō)明或主要描述統(tǒng)計(jì)技術(shù)特性的模擬研究的論文。但是，如果描述統(tǒng)計(jì)計(jì)算發(fā)展的論文是由實(shí)際例子驅(qū)動(dòng)的，則鼓勵(lì)它們。應(yīng)該避免擴(kuò)展代數(shù)處理。

The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies).A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.Aims and ScopeThe Journal of the Royal Statistical Society, Series C (Applied Statistics), promotes papers that are focused on statistical methods for real life problems. Applications should be central to papers, rather than illustrative, to motivate the work and to justify any methodological developments. All papers should feature an adequate description of a substantial application and a justification for any new theory. Case-studies may be particularly appropriate and should include some contextual details, though there should also be a novel statistical contribution, for instance by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to solve challenging applied problems. Papers describing interdisciplinary work are especially welcome, as are those that give interesting novel applications of existing methodology or provide new insights into the practical application of methods, and papers explaining innovative analysis of generic applied problems but not necessarily focused on a particular application also have a place in Series C. Short communications may also be appropriate. Methodological papers that are not motivated by a genuine application are not acceptable; nor are papers that include only brief numerical illustrations or that mainly describe simulation studies of properties of statistical techniques. However, papers describing developments in statistical computing are encouraged, provided that they are driven by practical examples. Extended algebraic treatment should be avoided.