Search result for Statistical inference Online Courses & Certifications
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Statistical Inference
by Brian Caffo, PhD , Roger D. Peng, PhD , Jeff Leek, PhD- 4.2
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Approx. 54 hours to complete
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. Welcome to Statistical Inference Course Book: Statistical Inference for Data Science...
Fitting Statistical Models to Data with Python
by Brenda Gunderson , Brady T. West , Kerby Shedden- 4.4
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Approx. 15 hours to complete
In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods....
Introductory Statistics : Basic Ideas and Instruments for Statistical Inference
by Ryu Keun-kwan , Sangbeom Kim- 0.0
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3 Weeks
This course utilizes real-life applications of Statistics in an exploration of the Statistical Inferenceprocess. Statistical Inference is the process by which data is used to draw a conclusionoruncover ascientific truthabout a population from asample. This course aims to familiarize the student with several ideas and instruments for statistical inference....
$49
Advanced Statistical Inference and Modelling Using R
by Elena Moltchanova- 0.0
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6 Weeks
Advanced Statistical Inference and Modelling Using R is part two of the Statistical Analysis in R professional certificate. This course is directed at people who are already familiar with basic linear regression and fundamentals of statistical inference....
$249
Data Science: Inferential Thinking through Simulations
by Ani Adhikari , John DeNero , David Wagner- 0.0
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5 Weeks
This is called statistical inference. In this course, you will learn the framework for statistical inference and apply them to real-world data sets. This course will teach you the power of statistical inference: given a random sample, how do we predict some quantity that we cannot observe directly? The logical and conceptual frameworks of statistical inference...
$199
Causal Inference 2
by Michael E. Sobel- 0.0
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Approx. 6 hours to complete
This course offers a rigorous mathematical survey of advanced topics in causal inference at the Master’s level. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships....
Statistics and R
by Rafael Irizarry , Michael Love- 0.0
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4 Weeks
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. 3x: Statistical Inference and Modeling for High-throughput Experiments...
$249
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Introduction to Linear Models and Matrix Algebra
by Rafael Irizarry , Michael Love- 0.0
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4 Weeks
We perform statistical inference on these differences. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts. 3x: Statistical Inference and Modeling for High-throughput Experiments...
$149
Mathematical Biostatistics Boot Camp 2
by Brian Caffo, PhD- 4.4
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Approx. 12 hours to complete
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples. Exact inference for The Odds Ratio...
Comprehensive Linear Modeling with R
by Geoffrey Hubona, Ph.D.- 3.9
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14.5 hours on-demand video
These include basic, conditional and simultaneous inference techniques; analysis of variance (ANOVA); linear regression; survival analysis; generalized linear models (GLMs); parametric and non-parametric smoothers and generalized additive models (GAMs); longitudinal and mixed-effects, split-plot and other nested model designs. The course concludes with a section on the special considerations and techniques for establishing simultaneous inference in the linear modeling domain....
$12.99
