|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/measures-of-central-tendency-dispersion-in-data-analytics/
课程评论：没有评论
Coursera《数据分析中的集中趋势与离散程度度量》课程总结： 本课程深入探讨了数据分析中衡量数据“中心”和“分散”程度的两个核心概念。 **一、 集中趋势度量 (Measures of Central Tendency)** 集中趋势度量旨在用一个数值来代表一组数据的基本特征或“平均”水平。本课程重点讲解了最常用的三种集中趋势度量： * **均值 (Mean)**：也称为算术平均数，是最常用的集中趋势度量。它是所有数值的总和除以数值的个数。计算公式为：均值 = ΣX/N，其中 N 是数值的个数。 * **中位数 (Median)**：指将一组数据按大小顺序排列后，位于最中间的数值。它使得样本或总体中 50% 的数值小于或等于它。 * **众数 (Mode)**：指在一组数据中出现频率最高的数值。众数的优点在于其含义直观，并且是唯一可以用于名义数据的集中趋势度量。 **二、 离散程度度量 (Measures of Dispersion)** 离散程度度量用于衡量数据集中各个数值之间的差异程度，即数据偏离其中心的程度。本课程重点讲解了： * **标准差 (Standard Deviation)**：用于描述数据点与其均值之间的平均离散程度。标准差是方差的平方根。方差和标准差在总体和样本中的符号表示不同（总体用 σ² 和 σ，样本用 s² 和 s）。 通过学习本课程，您将掌握如何计算和理解这些关键的统计度量，从而更好地分析和解读数据。
Measures of central tendency are statistics that tell us basic characterisitcs about a set of data. These are single number representatives of general characteristics of the group (not individual cases withing hte group), often called averages. Measures of central tendency are some of the most basic and useful statistical functions. They summarize a sample or population by a single typical value.There are three measures of central tendency and each one plays a different role in determining where the center of the distribution or the average score lies. The commonly used measures of central tendency for numerical data are the mean , median and mode. In this course we will explain the calculation of Mean, Median and Modei) MEAN is the most commonly used measure of central tendency.The the mean is often referred to as the statistical average. The arithmetic mean is commonly called the average. When the word "mean" is used without a modifier, it can be assumed that it refers to the arithmetic mean. The mean is the sum of all the values divided by the number of values. The formula is:MEAN = ΣX/Nwhere N is the number of values.ii) MEDIAN is a measure of central tendency determined as the least data value such that 50% of all values in the sample, or population, are less than or equal to it.iii) The MODE is the most frequently occurring score in a distribution and is used as a measure of central tendency. The advantage of the mode as a measure of central tendency is that its meaning is obvious. Further, it is the only measure of central tendency that can be used with nominal data. iv) If everything were the same, we would have no need of statistics. But, people's heights, ages, etc., do vary. We often need to measure the extent to which scores in a dataset differ from each other. Such a measure is called the dispersion of a distribution. Here we present various measures of dispersion that describe how scores within the distribution differ from the distribution's mean, mode and median. So in this course , we will also explain the calculation of Standard DeviationThe standard deviation is simply the square root of the variance. In some sense, taking the square root of the variance is opposite the squaring of the differences that we did when we calculated the variance. Variance and standard deviation of a population are designated by and , respectively. Variance and standard deviation of a sample are designated by s2 and s, respectively.
