Statistical Inference

ALUMNOS MATRICULADOS
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    Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.


    Syllabus

    Week 1: Probability & Expected Values
    This week, we’ll focus on the fundamentals including probability, random variables, expectations and more.

    Week 2: Variability, Distribution, & Asymptotics
    We’re going to tackle variability, distributions, limits, and confidence intervals.

    Week: Intervals, Testing, & Pvalues
    We will be taking a look at intervals, testing, and pvalues in this lesson.

    Week 4: Power, Bootstrapping, & Permutation Tests
    We will begin looking into power, bootstrapping, and permutation tests.



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    COMUNIDAD MOOC IR AL CURSO

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    • GRATIS
    • 13-03-2017FECHA INICIO
    • Johns Hopkins University
    • Brian Caffo

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