统计代写|商业分析作业代写Statistical Modelling for Business代考|Cumulative distributions and ogives

如果你也在 怎样代写商业分析Statistical Modelling for Business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。

商业分析就是利用数据分析和统计的方法,来分析企业之前的商业表现,从而通过分析结果来对未来的商业战略进行预测和指导 。

statistics-lab™ 为您的留学生涯保驾护航 在代写商业分析Statistical Modelling for Business方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写商业分析Statistical Modelling for Business方面经验极为丰富,各种代写商业分析Statistical Modelling for Business相关的作业也就用不着说。

我们提供的商业分析Statistical Modelling for Business及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等楖率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
统计代写|商业分析作业代写Statistical Modelling for Business代考|Cumulative distributions and ogives

统计代写|商业分析作业代写Statistical Modelling for Business代考|Cumulative distributions and ogives

Another way to summarize a distribution is to construct a cumulative distribution. To do this, we use the same number of classes, the same class lengths, and the same class boundaries that we have used for the frequency distribution of a data set. However, in order to construct a cumulative frequency distribution, we record for each class the number of

measurements that are less than the upper boundary of the class. To illustrate this idea, Table $2.10$ gives the cumulative frequency distribution of the payment time distribution summarized in Table $2.7$ (page 64). Columns (1) and (2) in this table give the frequency distribution of the payment times. Column (3) gives the cumulative frequency for each class. To see how these values are obtained, the cumulative frequency for the class $10<13$ is the number of payment times less than 13 . This is obviously the frequency for the class $10<13$, which is 3 . The cumulative frequency for the class $13<16$ is the number of payment times less than 16 , which is obtained by adding the frequencies for the first two classes-that is, $3+14=17$. The cumulative frequency for the class $16<19$ is the number of payment times less than 19-that is, $3+14+23=40$. We see that, in general, a cumulative frequency is obtained by summing the frequencies of all classes representing values less than the upper buumdary of the class.

Column (4) gives the cumulative relative frequency for each class, which is obtained by summing the relative frequencies of all classes representing values less than the upper boundary of the class. Or, more simply, this value can be found by dividing the cumulative frequency for the class by the total number of measurements in the data set. For instance, the cunulative relative frequency for the class $19<22$ is $52 / 65-.8$. Culum (5) gives the cumulative percent frequency for each class, which is obtained by summing the percent frequencies of all classes representing values less than the upper boundary of the class. More simply, this value can be found by multiplying the cumulative relative frequency of a class by 100. For instance, the cumulative percent frequency for the class $19<22$ is $.8 \times(100)=80$ percent.

As an example of interpreting Table $2.10 .60$ of the 65 payment times are 24 days or less. or, equivalently, $92.31$ percent of the payment times (or a fraction of $.9231$ of the payment times) are 24 days or less. Also, notice that the last entry in the cumulative frequency distribution is the total number of measurements (here, 65 payment times). In addition, the last entry in the cumulative relative frequency distribution is $1.0$ and the last entry in the cumulalive percent frequency disiribution is $100 \%$. In general, for any dala sel, these lasi eniries will be, respectively, the total number of measurements, $1.0$, and $100 \%$.

An ogive (pronounced “oh-jive”) is a graph of a cumulative distribution. To construct a frequency ogive, we plot a point above each upper class boundary at a height equal to the cumulative frequency of the class. We then connect the plotted points with line segments. A similar graph can be drawn using the cumulative relative frequencies or the cumulative percent frequencies. As an example, Figure $2.14$ gives a percent frequency ogive of the payment times. Looking at this figure, we see that, for instance, a little more than 25 percent (actually, $26.15$ percent according to Table $2.10$ ) of the payment times are less than 16 days, while 80 percent of the payment times are less than 22 days. Also notice that we have completed the ogive by plotting an additional point at the lower boundary of the first (leftmost) class at a height equal to zero. This depicts the fact that none of the payment times is less than 10 days. Finally, the ogive graphically shows that all ( 100 percent) of the payment times are less than 31 days.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Dot Plots

A very simple graph that can be used to summarize a data set is called a dot plot. To make a dot plot we draw a horizontal axis that spans the range of the measurements in the data set. We then place dots above the horizontal axis to represent the measurements. As an example, Figure $2.18$ (a) shows a dot plot of the exam scores in Table $2.8$ (page 68). Remember, these are the scores for the first exam given before implementing a strict attendance policy. The horizontal axis spans exam scores from 30 to 100 . Each dot above the axis represents an exam score. For instance, the two dots above the score of 90 tell us that two students received a 90 on the exam. The dot plot shows us that there are two concentrations of scores-those in the $80 \mathrm{~s}$ and $90 \mathrm{~s}$ and those in the $60 \mathrm{~s}$. Figure $2.18(\mathrm{~b})$ gives a dot plot of the scores on the second exam (which was given after imposing the attendance policy). As did the percent frequency polygon for Exam 2 in Figure $2.13$ (page 69), this second dot plot shows that the attendance policy eliminated the concentration of scores in the $60 \mathrm{~s}$.

Dot plots are useful for detecting outliers, which are unusually large or small observations that are well separated from the remaining observations. For example, the dot plot for exam 1 indicates that the score 32 seems unusually low. How we handle an outlier depends on its cause. If the outlier results from a measurement error or an error in recording or processing the data, it should be corrected. If such an outlier cannot be corrected, it should be discarded. If an outlier is not the result of an error in measuring or recording the data, its cause may reveal important information. For example, the outlying exam score of 32 convinced the author that the student needed a tutor. After working with a tutor, the student showed considerable improvement on Exam 2. A more precise way to detect outliers is presented in Section 3.3.

统计代写|商业分析作业代写Statistical Modelling for Business代考|back-to-back stem-and-leaf display

If we wish to compare two distributions, it is convenient to construct a back-to-back stemand-leaf display. Figure $2.20$ presents a back-to-back stem-and-leaf display for the previously discussed exam scores. The left side of the display summarizes the scores for the first exam. Remember, this exam was given before implementing a strict attendance policy. The right side of the display summarizes the scores for the second exam (which was given after imposing the attendance policy). Looking at the left side of the display, we see that for the first exam there are two concentrations of scores-those in the $80 \mathrm{~s}$ and $90 \mathrm{~s}$ and those in the $60 \mathrm{~s}$. The right side of the display shows that the attendance policy eliminated the concentration of scores in the $60 \mathrm{~s}$ and illustrates that the scores on exam 2 are almost single peaked and somewhat skewed to the left.

Stem-and-leaf displays are useful for detecting outliers, which are unusually large or small observations that are well separated from the remaining observations. For example, the stem-and-leaf display for exam 1 indicates that the score 32 seems unusually low. How we handle an outlier depends on its cause. If the outlier results from a measurement error or an error in recording or processing the data, it should be corrected. If such an outlier cannot be corrected, it should be discarded. If an outlier is not the result of an error in measuring or recording the data, its cause may reveal important information. For example, the outlying exam score of 32 convinced the author that the student needed a tutor. After working with a tutor, the student showed considerable improvement on exam 2. A more precise way to detect outliers is presented in Section 3.3.

统计代写|商业分析作业代写Statistical Modelling for Business代考|Cumulative distributions and ogives

金融中的随机方法代写

统计代写|商业分析作业代写Statistical Modelling for Business代考|Cumulative distributions and ogives

总结分布的另一种方法是构建累积分布。为此,我们使用与用于数据集频率分布的相同数量的类、相同的类长度和相同的类边界。然而,为了构建累积频率分布,我们记录每个类的数量

小于类的上限的测量值。为了说明这个想法,表2.10给出了表中汇总的支付时间分布的累积频率分布2.7(第 64 页)。此表中的第 (1) 和 (2) 列给出了支付时间的频率分布。第 (3) 列给出了每个类别的累积频率。要查看这些值是如何获得的,类的累积频率10<13是付款次数少于 13 次。这显然是上课的频率10<13,即 3 。类的累积频率13<16是小于 16 的支付次数,是前两个类的频率相加得到的——即,3+14=17. 类的累积频率16<19是支付次数小于19-即,3+14+23=40. 我们看到,一般来说,累积频率是通过将所有表示值小于该类的上界的类的频率相加来获得的。

第 (4) 列给出了每个类别的累积相对频率,它是通过将所有类别的相对频率相加获得的,这些类别代表的值小于该类别的上边界。或者,更简单地说,可以通过将类的累积频率除以数据集中的测量总数来找到该值。例如,该类的累积相对频率19<22是52/65−.8. Culum (5) 给出了每个类的累积百分比频率,它是通过将所有表示值小于类的上限的类的百分比频率相加而获得的。更简单地说,这个值可以通过将类的累积相对频率乘以 100 来找到。例如,类的累积百分比频率19<22是.8×(100)=80百分。

作为解释表的例子2.10.6065 次付款时间为 24 天或更短。或者,等效地,92.31支付时间的百分比(或一小部分.9231付款时间)为 24 天或更短。此外,请注意累积频率分布中的最后一项是测量的总数(此处为 65 次付款时间)。此外,累积相对频率分布中的最后一项是1.0累积百分比频率分布中的最后一个条目是100%. 一般来说,对于任何 dala sel,这些 lasi eniries 将分别是测量的总数,1.0, 和100%.

ogive(发音为“oh-jive”)是累积分布图。为了构建频率曲线,我们在每个上层类边界上方绘制一个点,高度等于类的累积频率。然后我们将绘制的点与线段连接起来。可以使用累积相对频率或累积百分比频率绘制类似的图表。例如,图2.14给出支付时间的百分比频率。看看这个数字,我们看到,例如,略高于 25%(实际上,26.15根据表百分比2.10) 的付款时间少于 16 天,而 80% 的付款时间少于 22 天。另请注意,我们通过在第一个(最左边)类的下边界处绘制一个附加点,高度为零,从而完成了 ogive。这描述了没有一个付款时间少于 10 天的事实。最后,ogive 以图形方式显示所有(100%)的付款时间都少于 31 天。

统计代写|商业分析作业代写Statistical Modelling for Business代考|Dot Plots

可用于汇总数据集的非常简单的图形称为点图。为了制作点图,我们绘制了一个横轴,该轴跨越了数据集中的测量范围。然后我们在水平轴上方放置点来表示测量值。例如,图2.18(a) 显示了表中考试成绩的点图2.8(第 68 页)。请记住,这些是在实施严格的考勤政策之前给出的第一次考试的分数。横轴跨越从 30 到 100 的考试分数。轴上方的每个点代表一个考试分数。例如,90 分以上的两个点告诉我们有两个学生在考试中获得了 90 分。点图向我们展示了分数的两个浓度——那些在80 s和90 s和那些在60 s. 数字2.18( b)给出第二次考试分数的点图(在实施出勤政策后给出)。与图 2 中考试 2 的百分比频率多边形一样2.13(第 69 页),第二个点图显示出勤政策消除了分数集中在60 s.

点图对于检测异常值非常有用,这些异常值是与其余观测值完全分离的异常大或小观测值。例如,考试 1 的点图表明分数 32 似乎异常低。我们如何处理异常值取决于其原因。如果异常值是由测量错误或记录或处理数据的错误引起的,则应予以纠正。如果无法纠正这样的异常值,则应将其丢弃。如果异常值不是测量或记录数据错误的结果,则其原因可能会揭示重要信息。例如,32 分的偏远考试分数使作者确信该学生需要一位导师。在与导师合作后,学生在考试 2 中表现出相当大的进步。第 3.3 节介绍了一种更精确的检测异常值的方法。

统计代写|商业分析作业代写Statistical Modelling for Business代考|back-to-back stem-and-leaf display

如果我们想比较两个分布,构建一个背靠背的茎叶显示是很方便的。数字2.20为前面讨论的考试分数提供了一个背靠背的茎叶显示。显示屏的左侧总结了第一次考试的分数。请记住,此考试是在实施严格的出勤政策之前进行的。显示屏的右侧总结了第二次考试的分数(这是在实施出勤政策后给出的)。查看显示屏的左侧,我们看到第一次考试有两个分数集中度——那些在80 s和90 s和那些在60 s. 显示右侧显示考勤政策消除了分数集中在60 s并说明考试 2 的分数几乎是单峰的,并且有些偏左。

茎叶显示可用于检测异常值,这些异常值是与其余观测值完全分开的异常大或小观测值。例如,考试 1 的茎叶显示表明分数 32 似乎异常低。我们如何处理异常值取决于其原因。如果异常值是由测量错误或记录或处理数据的错误引起的,则应予以纠正。如果无法纠正这样的异常值,则应将其丢弃。如果异常值不是测量或记录数据错误的结果,则其原因可能会揭示重要信息。例如,32 分的偏远考试分数使作者确信该学生需要一位导师。在与导师合作后,学生在考试 2 中表现出相当大的进步。

统计代写|商业分析作业代写Statistical Modelling for Business代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

在概率论概念中,随机过程随机变量的集合。 若一随机系统的样本点是随机函数,则称此函数为样本函数,这一随机系统全部样本函数的集合是一个随机过程。 实际应用中,样本函数的一般定义在时间域或者空间域。 随机过程的实例如股票和汇率的波动、语音信号、视频信号、体温的变化,随机运动如布朗运动、随机徘徊等等。

贝叶斯方法代考

贝叶斯统计概念及数据分析表示使用概率陈述回答有关未知参数的研究问题以及统计范式。后验分布包括关于参数的先验分布,和基于观测数据提供关于参数的信息似然模型。根据选择的先验分布和似然模型,后验分布可以解析或近似,例如,马尔科夫链蒙特卡罗 (MCMC) 方法之一。贝叶斯统计概念及数据分析使用后验分布来形成模型参数的各种摘要,包括点估计,如后验平均值、中位数、百分位数和称为可信区间的区间估计。此外,所有关于模型参数的统计检验都可以表示为基于估计后验分布的概率报表。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

statistics-lab作为专业的留学生服务机构,多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务,包括但不限于Essay代写,Assignment代写,Dissertation代写,Report代写,小组作业代写,Proposal代写,Paper代写,Presentation代写,计算机作业代写,论文修改和润色,网课代做,exam代考等等。写作范围涵盖高中,本科,研究生等海外留学全阶段,辐射金融,经济学,会计学,审计学,管理学等全球99%专业科目。写作团队既有专业英语母语作者,也有海外名校硕博留学生,每位写作老师都拥有过硬的语言能力,专业的学科背景和学术写作经验。我们承诺100%原创,100%专业,100%准时,100%满意。

机器学习代写

随着AI的大潮到来,Machine Learning逐渐成为一个新的学习热点。同时与传统CS相比,Machine Learning在其他领域也有着广泛的应用,因此这门学科成为不仅折磨CS专业同学的“小恶魔”,也是折磨生物、化学、统计等其他学科留学生的“大魔王”。学习Machine learning的一大绊脚石在于使用语言众多,跨学科范围广,所以学习起来尤其困难。但是不管你在学习Machine Learning时遇到任何难题,StudyGate专业导师团队都能为你轻松解决。

多元统计分析代考


基础数据: $N$ 个样本, $P$ 个变量数的单样本,组成的横列的数据表
变量定性: 分类和顺序;变量定量:数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。

回归分析代写

多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。

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。

R语言代写问卷设计与分析代写
PYTHON代写回归分析与线性模型代写
MATLAB代写方差分析与试验设计代写
STATA代写机器学习/统计学习代写
SPSS代写计量经济学代写
EVIEWS代写时间序列分析代写
EXCEL代写深度学习代写
SQL代写各种数据建模与可视化代写

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