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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introductory-probability-and-statistics/
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课程名称：入门概率与统计 课程概述：这是一个关于概率和统计的入门课程，旨在为更高级的统计课程，特别是推断统计和研究方法课程奠定基础。课程包含85个视频讲座，教你如何使用电子表格进行概率估计和统计分析。 课程结构分为10个部分： 1. **统计学概述**：统计的单数与复数含义、特征、性质与范围、类型（描述性与推断性）、统计的局限性。 2. **描述性统计**：集中趋势的度量、离散度的度量和形状的度量。 3. **概率**：概率的简介、基本计数规则、事件与样本空间、集合与维恩图、概率的不同方法、加法和乘法规则、全概率定律、贝叶斯定理。 4. **随机变量**：意义、离散随机变量、连续随机变量、期望值、方差、概率分布（二项式、泊松、正态分布）。 5. **抽样分布**：总体与样本、参数与统计量、均值的抽样分布、抽样类型、非概率抽样、抽样分布定理。 6. **估计**：估计量与估计的含义、一个好的估计量的特性、点估计、区间估计、标准误的概念、置信区间的构造、样本量确定。 7. **假设检验**：简介、原假设与备择假设的意义、双尾与单尾检验、错误类型、假设检验程序、人口均值检验（大样本与小样本）、独立样本的均值检验、配对t检验、两人口方差的F检验。 8. **方差分析（ANOVA）**：单因素ANOVA、使用Excel进行单因素ANOVA、使用Excel进行双因素ANOVA（不重复与重复）、N因素ANOVA。 9. **相关分析**：概念介绍、散点图、皮尔逊相关系数、斯皮尔曼等级相关、可能误差、人口相关系数的假设检验。 10. **回归分析**：回归简介、回归线、经典线性回归模型的假设、最小二乘法、决定系数(R平方)、OLS估计的标准误、置信区间、假设检验（双尾与单尾）、回归分析实例、回归模型的预测、使用Excel进行回归估计。 该课程将真实地教授您统计学，消除您在概率和统计方面的所有疑惑。如果您想简单地学习概率与统计，必须报名参加此课程。
This is an introductory course in probability and statistics. This course helps to serve as a foundation for higher levels of a statistics course, particularly inferential statistics and research methods course. This course provides 85 video lectures and it also teaches you how to estimate the probability and do statistical analysis using spreadsheets. The course is structured into 10 sections:What is Statistics- Meaning of Statistics in Singular & Plural Sense, Characteristics of Stat, Nature & Scope, Types -Descriptive & Inferential, Distrust and other limitations of Statistics.Descriptive Statistics- Measures of Central Tendency, Measures of Dispersion and Measures of ShapeProbability- Introduction to Probability, Fundamental Rules of Counting, Events & and Sample Space, Set & Venn Diagram, Approaches to Probability, Addition Rule, Multiplication Rule, The Law of Total Probability, Bayes' Theorem.Random Variable- Meaning, Discrete Random Variable, Continous Random Variable, Expected Value, Variance, Probability distributions- Binomial, Poisson, Normal DistributionSampling Distribution- Population & Sample, Parameters & Statistics, Sampling Distribution of Mean, Types of Sampling, Non-Probability Sampling, Theorems of Sampling Distribution Estimation -Estimator & Estimate, Qualities of a good estimator, Point Estimate, Interval Estimate, the concept of standard error Confidence Interval construction, Sample size determination.Hypothesis Testing- Introduction, Meaning of Null and Alternate Hypothesis, Two-tail & One-tail Tests, Types of Error, Hypothesis Testing Procedure, Hypothesis Test of a Population Mean: Large and Small Sample, Hypothesis Test of Population Mean: Two Independent Samples, Hypothesis Test of a Population Mean: Paired t-test, Hypothesis Test of Two Population Variance: F-test. ANOVA: One-Way ANOVA, One- Way ANOVA using Excel, Two-Way ANOVA without replication using excel, Two-Way ANOVA with replication using excel, N-Way ANOVA. Correlation Analysis -Intro to Concept, Scatter Plot, Karl Pearson Coefficient of Correlation, Spearman Rank Order Correlation, Probable Error, Hypothesis Testing of Population Coefficient of Correlation. Regression Analysis- Introduction to Regression, Regression Line, Assumptions of the Classical Linear Regression Model, OLS Method, Coefficient of Determination (R Square), Standard Error of OLS estimates, Confidence Interval for alpha and beta, Hypothesis testing, Two-Tail, One -Tail, Regression Analysis Solved Example, Forecasting With Regression Model, Regression Estimation Using Excel. This course will teach you statistics in a real sense and help you to remove your all doubts relating to statistics and probability. If you want really learn probability and statistics in a simple way, you must enrol for this course.
