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所在平台: Udemy |
课程主页: https://www.udemy.com/course/engineering-statistics-and-probability-theory/
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课程名称：统计与数据分析（الإحصاء وتحليل البيانات） 课程概述：欢迎参加工程统计与概率论课程。本课程将探讨工程统计和概率理论的理论及其在实际商业问题中的应用。每个部分都有丰富的实例、测验和评估考试。我们提供专业的高清教学视频，并通过大量案例研究展示如何将所学知识应用于解决真实的实际问题。 课程内容包括： - 统计与概率的介绍 - 学习统计的意义 - 数据类型及定义：总体、单位和样本 - 随机数表的生成方法 - 参数与统计量的区别 - 统计的分支（描述统计与推断统计） - 多种图表（帕累托图、点图、散点图、频率分布直方图、茎叶图） - 中心趋势度量（均值、中位数、众数） - 变异度量（范围、方差和标准差） - 加权均值和分组数据的标准差 - 概率实验、结果、样本空间和事件的定义 - 概率类型（经典概率、经验概率、主观概率） - 事件组合与计数原则（选择的乘法、排列与组合） - 概率公理与韦恩图 - 加法法则与条件概率 - 贝叶斯定理及独立事件 - 离散概率分布及其类型（离散随机变量、二项分布、超几何分布、泊松分布） - 离散概率分布的均值、方差和标准差 - 连续概率分布（正态分布及其标准化） - 抽样分布（总体与样本、均值的抽样分布） - 对总体均值的估计（点估计与区间估计） - 假设检验的介绍（第一类和第二类错误、显著性水平、假设检验过程） - 大样本和小样本的假设检验 - P值方法的假设检验 - 相关性与回归分析（相关系数、线性回归、协方差） 课程内容丰富，适合需要运用统计和数据分析的方法与技能来解决实际问题的学习者。
Welcome to Engineering Statistics and Probability TheoryThis course will go over theories and implementation of engineering statistics and probability theories to real business problems. Each section has many examples, quizzes, and assessment exams.Our course includes professional HD Videos with extensive case studies to show you how to apply this knowledge to solve real and practical problems.In this course we will cover:Introduction to statistics and probabilityWhy Study Statistics?Types of dataDefinitions: Populations, units, and SampleGeneration of random number tableThe difference between Parameters & StatisticsBranches of Statistics (Descriptive and Inferential statistics)Pareto chartDot plotScatter plotFrequency distributionHistogramStem and Leaf displayMeasures of Central Tendency (Mean, Median, and Mode)Measures of Variation (Range, Variance and Standard Deviation)Weighted MeanStandard Deviation for Grouped DataCoefficient of variationDefinitions (Probability experiment, Outcome, Sample space, and Event)Types of ProbabilityClassical (or theoretical) ProbabilityEmpirical (or statistical) ProbabilitySubjective ProbabilityCombining eventsCounting PrinciplesMultiplication of choicesPermutationCombinationThe Axioms of ProbabilityVenn diagramsThe Addition RuleMutually Exclusive EventsConditional ProbabilityThe Multiplication RuleIndependent EventsBayes' TheoremDiscrete Probability DistributionsTypes of Random VariablesDiscrete Probability Distributions (DPD)Binomial DistributionHypergeometric DistributionPoisson DistributionMean, Variance, and Standard Deviation of DPDContinuous Probability DistributionsNormal DistributionThe Standard Normal DistributionThe Standard Normal Distribution TablesThe Normal Approximation to the Binomial DistributionSampling distributionsPopulations and SamplesThe Sampling Distribution of the MeanThe Sampling Distribution of the Mean (σ Known) -> z-distributionThe Sampling Distribution of the Mean (σ Unknown) -> t-distributionSampling Distribution of the Variance -> χ2-distributionF - DistributionEstimation of Population'sEstimation of Population's MeanPoint EstimationInterval EstimationNormal (s known). Or n ³ 30Normal (s Unknown).Calculation of Sample SizeTests of HypothesesIntroduction to Hypothesis TestingType I and type II errorsLevel of SignificanceHypotheses Testing ProcessTest Statistic SelectionStatistical DecisionHypothesis Testing for the Population's Mean:Large Samples; n ≥ 30 or Normal population (σ Known) à (z)Small Samples: n < 30 and Normal population (σ Unknown) à (t)Tests of Hypotheses Using P-valueHypothesis Testing for ProportionsCorrelation and RegressionCorrelation Coefficient rscatter plotCorrelation CoefficientLinear RegressionRegression LineLinear combination of variablesCovarianceCorrelation using covarianceand much more!
