如果你也在 怎样代写统计推断Statistical inference这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。统计推断Statistical inference是利用数据分析来推断概率基础分布的属性的过程。推断性统计分析推断人口的属性,例如通过测试假设和得出估计值。假设观察到的数据集是从一个更大的群体中抽出的。
统计推断Statistical inference可以与描述性统计进行对比。描述性统计只关注观察到的数据的属性,它并不依赖于数据来自一个更大的群体的假设。在机器学习中,推理一词有时被用来代替 “通过评估一个已经训练好的模型来进行预测”;在这种情况下,推断模型的属性被称为训练或学习(而不是推理),而使用模型进行预测被称为推理(而不是预测);另见预测推理。
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我们提供的统计推断Statistical inference及其相关学科的代写,服务范围广, 其中包括但不限于:
澳洲代考|统计推断代考Statistical inference代考|The Place of Statistics in the Social Sciences
It was over 70 years ago that W. H. Auden $(1966$, p. 225) issued this commandment in a Harvard commencement address; and, at least since J. W. Tankard (1984) quoted this Phi Beta Kappa poem in his book The Statistical Pioneers, few critics of statistics in the social sciences have been able to resist repeating it. Grounds for Auden’s concern were already evident: At around the same time, Maurice Kendall (1942), commenting on the growing influence of statisticians, observed that, “Beginning as a small sect concerned only with political economy, they have already overrun every branch of science with a rapidity of conquest rivaled only by Attila, Mohammed, and the Colorado beetle” (p. 69). Ironically, and remarkably, Kendall made his observation just before the explosive growth of statistics following World War II. With the advent of electronic computers and space-age grants to research and higher education, his comparison has long since faded into meaninglessness; with respect not only to their speed of conquest but equally to the breadth of their empire, the social scientists have put Attila to shame. In 1942, when Kendall was writing, only $29 \%$ of medical schools taught statistics, but by 1952 , that figure was $82 \%$ (Marks, 1997 , p. 137 , n. 5). And medicine was the field where conquest by statistics was most vigorously contested.
Beginning just before World War II, the new theory of statistical inference developed during the 1920 s and 1930 s transformed the social sciences and neighboring disciplines. Although, with respect to issues of application, I shall be focusing on psychology, the area of my training and experience, and secondarily on medicine, the fields of education, sociology, agriculture, and economics were similarly infected. Comparative experiments, designed to investigate questions of whether certain treatments had an “effect” or not, were now assumed to have, in statistical significance, an objective criterion for yes-or-no answers to those questions, and research in these fields, in its modern forms, mushroomed in the $1940 \mathrm{~s}$ and $1950 \mathrm{~s}$ partly as a result. A course in statistics, with an overwhelming emphasis on inferential over descriptive techniques, is today the one requirement across virtually all universities in these disciplines. Statistical inference has thus been regarded, almost universally for the last 70 years, as the backbone of research in the social sciences. Its monopoly is ominously secured, and attested, by the fact that it now crucially shapes the research questions that can be asked in these fields, and few practitioners can any longer conceive of alternatives.
澳洲代考|统计推断代考Statistical inference代考|Statistical Inference and its Misconceptions
With Oakes’s pretest out of the way, it is time to concretize the initial focus of this essay by providing an informal clarification of what is meant by statistical inference and taking a brief look at some of the criticisms. For simplicity I shall be speaking in this chapter only of significance testing; other techniques comprised by statistical inference (e.g., confidence intervals) I shall get to in due course.
In a typical significance test, we might want to determine whether our treatment had had an effect. Now it might be supposed that we could just look at our data and see whether there were a difference between our treatment and control data. But when psychologists speak of a treatment effect, they are talking about a difference that is unobservable in principle. We actually imagine our treatment and control data to have been randomly sampled from a hypothetical infinite population of such scores, and our statement of an effect pertains to a difference between these hypothetical populations. When we do a significance test, we start by assuming something we typically don’t believe for a moment-for example, that the treatment has no effect. The significance level we calculate is the probability of drawing two such different samples if there were no difference in the population means. If this probability lies beyond a conventional cut-off, we reject the null hypothesis of no effect.
So the first thing that is strange about the procedure is that it is wholly hypothetical. Except in survey research, we don’t take random samples, but that is the basis for our probability calculations, which otherwise have no meaning.
澳洲代考|统计推断代考STATISTICAL INFERENCE代考|The Root of the Problem in the Dualistic Concept of Probability
The source of confusion over statistical inference is the concept of probability, on which statistical inference rests. It is evident from reflection on everyday usage that the concept of probability covers several aspects or meanings. On the one hand, it adverts to frequency of occurrence in some set or series of events. If we say “set,” we have the classical, or Laplacean, measure of probability from gambling as the ratio of the number of cases favorable to an event to the total number of equiprobable cases; if the definition is to avoid circularity, judgments of equiprobability must appeal to some principle of symmetry or indifference.
If we say “series,” we have the frequency definition of probability, in terms of a sequence of events, whether real or ideally constructed. The frequency definition is often confused with the classical, but the distinction is important. The frequency conception is regarded by its proponents as having the advantage, over the classical, of being empirical; actuarial probabilities based on mortality statistics are the cardinal example. The classical ratio, in contrast, is based on an a priori model (e.g., of a fair coin). Either definition presupposes a random process (rolling a die, deaths from the plague), with the probability of an event either assumed or estimated from data; calculations then concern probabilities of particular outcomes of the random process.
统计推断代写
澳洲代考|统计推断代考STATISTICAL INFERENCE代考|THE PLACE OF STATISTICS IN THE SOCIAL SCIENCES
70 多年前,WH Auden(1966,页。225)在哈佛毕业典礼演讲中发布了这条诫命;而且,至少从 JW Tankard 开始1984在他的《统计先驱》一书中引用了这首 Phi Beta Kappa 诗,很少有社会科学统计评论家能够拒绝重复它。奥登担心的理由已经很明显:大约在同一时间,莫里斯·肯德尔1942在评论统计学家日益增长的影响力时,他观察到,“作为一个只关注政治经济学的小教派,他们已经以只有阿提拉、穆罕默德和科罗拉多甲虫可以匹敌的速度征服了科学的每一个分支”p.69. 具有讽刺意味的是,肯德尔在二战后统计数据爆炸式增长之前做出了他的观察。随着电子计算机的出现和太空时代对研究和高等教育的资助,他的比较早已变得毫无意义。社会科学家不仅在征服速度方面,而且在帝国的广度方面,都让阿提拉感到羞耻。1942 年,肯德尔写作时,只有29%的医学院教授统计学,但到 1952 年,这个数字是82% 米一个rķs,1997,p.137,n.5. 医学是统计学征服最激烈的领域。
就在第二次世界大战之前开始,在 1920 年代和 1930 年代发展起来的新的统计推断理论改变了社会科学和邻近学科。虽然,在应用问题上,我将重点关注心理学,我的培训和经验领域,其次是医学,但教育、社会学、农业和经济学领域也受到了类似的影响。旨在调查某些治疗是否具有“效果”的问题的比较实验,现在被假定在统计学上具有对这些问题的回答是或否的客观标准,以及这些领域的研究,在它的现代形式,如雨后春笋般涌现1940 s和1950 s部分原因。统计学课程,压倒性地强调推论而非描述性技术,是当今这些学科几乎所有大学的一个要求。因此,在过去的 70 年中,统计推断几乎被普遍认为是社会科学研究的支柱。它的垄断得到了不祥的保证,并得到了证明,因为它现在对这些领域可以提出的研究问题至关重要,而且很少有从业者可以再设想替代方案。
澳洲代考|统计推断代考STATISTICAL INFERENCE代考|STATISTICAL INFERENCE AND ITS MISCONCEPTIONS
随着 Oakes 的预先测试的结束,是时候通过非正式地澄清统计推断的含义并简要介绍一些批评来具体化本文的最初重点了。为简单起见,我将在本章中仅讨论显着性检验;统计推断所包含的其他技术和.G.,C○nF一世d和nC和一世n吨和r在一个ls我会在适当的时候到达。
在典型的显着性检验中,我们可能想确定我们的治疗是否有效。现在可以假设我们可以只查看我们的数据,看看我们的处理数据和控制数据之间是否存在差异。但是当心理学家谈到治疗效果时,他们谈论的是原则上无法观察到的差异。我们实际上想象我们的治疗和控制数据是从这些分数的假设无限群体中随机抽样的,我们对效果的陈述与这些假设群体之间的差异有关。当我们进行显着性检验时,我们首先假设一些我们通常暂时不相信的事情——例如,治疗没有效果。我们计算的显着性水平是在总体均值没有差异的情况下抽取两个这样不同的样本的概率。如果此概率超出常规截止值,我们拒绝无效的零假设。
所以这个程序的第一件奇怪的事情是它完全是假设的。除了在调查研究中,我们不随机抽样,但这是我们计算概率的基础,否则没有任何意义。
澳洲代考|统计推断代考STATISTICAL INFERENCE代考|THE ROOT OF THE PROBLEM IN THE DUALISTIC CONCEPT OF PROBABILITY
统计推断混淆的根源是概率的概念,统计推断依赖于此。从日常使用的反思中可以明显看出,概率的概念涵盖了几个方面或含义。一方面,它反映了某些事件或一系列事件的发生频率。如果我们说“集合”,我们有经典的或拉普拉斯式的赌博概率度量,即对事件有利的案例数量与等概率案例总数的比率;如果定义是为了避免循环,对等概率的判断必须诉诸某种对称或无差异原则。
如果我们说“系列”,我们就有概率的频率定义,根据一系列事件,无论是真实的还是理想构造的。频率定义经常与经典混淆,但区别很重要。频率概念被其支持者认为比经典概念具有经验性的优势。基于死亡率统计的精算概率就是主要的例子。相比之下,经典比率基于先验模型和.G.,○F一个F一个一世rC○一世n. 任何一个定义都预设了一个随机过程r○ll一世nG一个d一世和,d和一个吨HsFr○米吨H和pl一个G在和,从数据中假设或估计事件的概率;然后计算涉及随机过程的特定结果的概率。
澳洲代考|统计推断代考Statistical inference代考 请认准UprivateTA™. UprivateTA™为您的留学生涯保驾护航。
微观经济学代写
微观经济学是主流经济学的一个分支,研究个人和企业在做出有关稀缺资源分配的决策时的行为以及这些个人和企业之间的相互作用。my-assignmentexpert™ 为您的留学生涯保驾护航 在数学Mathematics作业代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的数学Mathematics代写服务。我们的专家在图论代写Graph Theory代写方面经验极为丰富,各种图论代写Graph Theory相关的作业也就用不着 说。
线性代数代写
线性代数是数学的一个分支,涉及线性方程,如:线性图,如:以及它们在向量空间和通过矩阵的表示。线性代数是几乎所有数学领域的核心。
博弈论代写
现代博弈论始于约翰-冯-诺伊曼(John von Neumann)提出的两人零和博弈中的混合策略均衡的观点及其证明。冯-诺依曼的原始证明使用了关于连续映射到紧凑凸集的布劳威尔定点定理,这成为博弈论和数学经济学的标准方法。在他的论文之后,1944年,他与奥斯卡-莫根斯特恩(Oskar Morgenstern)共同撰写了《游戏和经济行为理论》一书,该书考虑了几个参与者的合作游戏。这本书的第二版提供了预期效用的公理理论,使数理统计学家和经济学家能够处理不确定性下的决策。
微积分代写
微积分,最初被称为无穷小微积分或 “无穷小的微积分”,是对连续变化的数学研究,就像几何学是对形状的研究,而代数是对算术运算的概括研究一样。
它有两个主要分支,微分和积分;微分涉及瞬时变化率和曲线的斜率,而积分涉及数量的累积,以及曲线下或曲线之间的面积。这两个分支通过微积分的基本定理相互联系,它们利用了无限序列和无限级数收敛到一个明确定义的极限的基本概念 。
计量经济学代写
什么是计量经济学?
计量经济学是统计学和数学模型的定量应用,使用数据来发展理论或测试经济学中的现有假设,并根据历史数据预测未来趋势。它对现实世界的数据进行统计试验,然后将结果与被测试的理论进行比较和对比。
根据你是对测试现有理论感兴趣,还是对利用现有数据在这些观察的基础上提出新的假设感兴趣,计量经济学可以细分为两大类:理论和应用。那些经常从事这种实践的人通常被称为计量经济学家。
Matlab代写
MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习和应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。