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课程介绍
真正理解假设检验
在你计算出平均值并绘制图表之后，会发生什么？如何从描述性统计迈向自信决策？如何运用假设检验来解决现实世界中的问题？在这门为期四小时的 Python 推断基础课程中，你将通过实践获得基于数据做出可靠结论的经验。 你将全面学习抽样，并了解不当抽样如何使统计推断偏离课程。分析广泛的场景
你将首先学习正态性和相关性的假设检验，以及参数检验和非参数检验。 你将使用 SciPy 运行这些测试，并解读其输出，以用于决策。接下来，你将通过效应量和统计功效来衡量结果的强度，同时通过应用校正来避免虚假相关。最后，你将运用模拟、随机化和元分析来处理广泛的数据，包括重新分析其他研究人员的结果。从大数据中得出可靠结论
完成课程后，你将能够成功运用大数据，并据此做出领导者可以信赖的有原则的决策。 你将远远超越图表和汇总统计，产出可靠、可重复且可解释的结果。先修要求
课程大纲
课程大纲
1
Inferential Statistics and Sampling
In this chapter, we'll explore the relationship between samples and statistically justifiable conclusions. Choosing a sample is the basis of making sound statistical decisions, and we’ll explore how the choice of a sample affects the outcome of your inference.
- Statistical inference and random sampling50 经验值
- Sampling and point estimates100 经验值
- Repeated sampling, point estimates and inference100 经验值
- Sampling and bias50 经验值
- Visualizing samples100 经验值
- Inference and bias100 经验值
- Confidence intervals and sampling50 经验值
- Normal sampling distributions100 经验值
- Calculating confidence intervals100 经验值
- Drawing conclusions from samples100 经验值
2
Hypothesis Testing Toolkit
Learn all about applying normality tests, correlation tests, and parametric and non-parametric tests for sound inference. Hypothesis tests are tools, and choosing the right tool for the job is critical for statistical decision-making. While you may be familiar with some of these tests in introductory courses, you'll go deeper to enhance your inferential toolkit in this chapter.
3
Effect Size
In this chapter, you'll measure and interpret effect size in various situations, encounter the multiple comparisons problem, and explore the power of a test in depth. While p-values tell you if a significant effect is present, they don't tell you how strong that effect is. Effect size measures how strong an effect a treatment has. Master the factors underpinning effect size in this chapter.
4
Simulation, Randomization, and Meta-Analysis
You’ll expand your inferential statistics toolkit further with a look at bootstrapping, permutation tests, and methods of combining evidence from p-values. Bootstrapping will provide you with a first look at statistical simulation. In the lesson meta-analysis, you’ll learn all about combining results from multiple studies. You’ll end with a look at permutation tests, a powerful and flexible non-parametric statistical tool.
Foundations of Inference in Python
课程完成
