Pre-MBA Statistics

所在平台: Coursera

课程主页: https://www.coursera.org/learn/pre-mbastatistics

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课程简介

课程名称：预MBA统计学 课程概述：欢迎参加预MBA统计学课程！在本课程结束时，您将能够描述统计学如何用于总结、分析和解释数据。课程介绍了一些描述性统计和推断统计的基本概念。您将学习区分各种数据类型，并描述可以对每种数据类型执行的操作及使用的正确工具。此外，课程还讨论了概率的概念，这是统计学的基础。 课程大纲： 1. 数据类型：在此模块中，您将学习各种数据类型。您将深入了解根据数据的组织方式和每种数据所能进行的推断量来区分数据类型。模块还分析了不同数据类型的独特特征，并将教授您各种数据的可用性和可解释性的操作。 2. 概率：本模块将介绍概率基础和随机变量概念。这为数据行为及不确定性如何数学建模提供了比较正式的方法。最终，模块讨论了随机变量及其特殊数学实体，这些实体能够很好地建模数值数据并有助于推断。 3. 抽样：在此模块中，您将学习在调查中使用的不同抽样方法。抽样可以是完全随机的或非随机的。您将了解这些技术的优缺点，并识别出适用于您所处情况的正确方法。此外，您将分析两个重要结果的展示：大数法则和中心极限定理。 4. 点估计和区间估计：收集整个总体的数据通常是昂贵的，有时甚至是不可能的。然而，您可以轻松地从总体中收集样本数据。在此模块中，您将学习如何根据所收集的样本数据对总体特征进行推断。课程将教授您点估计，并能够构建数据总体的均值和标准差的点估计。如果您感兴趣的数据以比例表示，您也可以构建该比例的点估计。模块还讨论了区间估计。您将学习如何围绕点估计构建置信区间，以确保您对总体参数落在该区间内的信心。 5. 假设检验：在给定的样本值和有关该样本来自具有某些特征的总体的声明下，经过本模块的学习，您将能够构建测试来验证或驳回该声明。您将学习构建和执行均值和比例检验的逻辑，同时了解如何根据来自两个总体的样本比较两者特性的方法。 6. 同行评审作业：这是一个基于课程所教概念的同行评审作业。在该作业中，您将能够在现实情况下应用在课程中学到的技能。

课程大纲

Name:Types of Data

Description:In this module, you will learn about various types of data. You will gain insight into the types of data based on how they can be organized and the amount of inference possible from each of them. The module also analyzes the unique characteristics of diverse types of data. Lastly, you will also learn operations with usability and interpretability of various kinds of data.

Name:Probability

Description:In this module, you will learn about the basics of probability and the concept of random variables. This provides a relatively more formal approach to how data behaves and how uncertainties are modeled mathematically. Finally, the module discusses random variables and the special mathematical entities that model numerical data well and help in inferences.

Name:Sampling

Description:In this module, you will learn about different types of sampling methods used in surveys. Such sampling can be completely randomized or non-randomized. You will learn the pros and cons of these techniques and identify the right method to use in the situation you have in hand. You will also analyze the presentation of two important results: the law of large numbers and the central limit theorems.

Name:Point and Interval Estimation

Description:The task of collecting data from all members of a population is often expensive and sometimes impossible. You can, however, easily collect sample data from a population. In this module, you will learn to make inferences about the characteristics of the population from which you have collected sample data. In this module, you will learn about point estimation and then be able to construct a point estimate of the mean and standard deviation of data in the population. If the data you are interested in is expressed as a proportion, you can construct a point estimate of that proportion. The module also discusses interval estimation. You will learn how to build a confidence interval or a range around a point estimate so that you are appropriately confident that the population parameter will fall within that interval regardless of the sample from which the point estimate was obtained.

Name:Hypothesis Testing

Description:Given a sample of values and a claim that the sample comes from a population with certain characteristics, after going through this module, you will be able to construct tests that will justify or reject such a claim. You will learn the logic behind constructing and executing tests for means and proportions. You will also learn about tests to compare the properties of two populations based on samples from both populations.

Name:Peer Review Assignment

Description:This is a peer-review assignment based on the concepts taught in the Pre-MBA Statistics course. In this assignment, you will be able to apply the skills learned in the course in a realistic situation.

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