Examples of using Statistical inference in English and their translations into Italian
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Some examples of statistical inference methods.
Statistical inference: NHST versus model fitting.
General aspects of statistical inference: population and sample.
Statistical inference and its application in business administration;
Introduction to probability theory and statistical inference.
Prerequisites: Statistical Inference in parallel.
Being able to use the tools- also advanced- of statistical inference.
Add tags for"Statistical inference: a short course".
The level of confidence we are willing to concede in statistical inference.
Introduction to statistical inference: hypothesis testing;
the principles of statistical inference.
Critical appraisal statistical inference- article by Tamir(1988)|.
Statistical inference: Sample and sample functions, sampling variability.
Apply the principle of statistical inference from sample to population.
Statistical Inference: Point and interval estimation, statistical hypothesis tests.
He is also known for his work in the theory of statistical inference and in multivariate analysis.
Statistical inference, theory of the estimators, maximum likelihood, test of hypothesis.
That's enough to make a statistical inference.
Prerequisites: basic statistical inference, basic knowledge of Eviews.
probability, and statistical inference.
Introduction to statistical inference: Statistical models; Parametric statistical models;
using a molecular clock are based on statistical inference and not on direct evidence.
Understand the logic behind statistical inference- the science of drawing conclusions from limited
machine learning, statistical inference and modeling or computer systems for data science.
banking and statistical inference.
Some models characterize the acquisition of semantic information as a form of statistical inference from a set of discrete experiences, distributed across a number of"contexts.
artificial intelligence, statistical inference, operations research, and optimization.
he held that conventional statistical inference about unobservable population parameters amounts to inference
before exploring statistical inference, machine learning, mathematical modeling, and data visualization.
which focuses more on questions of statistical inference such as how much uncertainty is present in a curve