Examples of using Statistical inference in English and their translations into Greek
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That's enough to make a statistical inference.
Recall that statistical inference permits us to draw conclusions about a population based on a sample.
Statistics: Descriptive Statistics and Statistical Inference.
Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists.
The basic concepts of statistical inference are analyzed.
Reach consensus about what the observations tell us about the world we observe(statistical inference).
Bartlett's theorem and asymptotic statistical inference for autocorrelations.
In one of these interpretations,the theorem is used directly as part of a particular approach to statistical inference.
In later editions,Fisher explicitly contrasted the use of the p-value for statistical inference in science with the Neyman- Pearson method, which he terms"Acceptance Procedures".
One particular approach to such inference is known as predictive inferencebut the prediction can be undertaken within any of the several approaches to statistical inference.
There are many similarities between machine learning theory and statistical inference, although they use different terms.
Using multiple regression model and statistical inference, we tried to determine whether the institutional framework, the crisis and macro-economic factors affect entrepreneurship.
The science of data makes extensive use of algorithms,machine learning and statistical inference for extracting knowledge and predictions.
Many statistical inference procedures for linear models require an intercept to be present, so it is often included even if theoretical considerations suggest that its value should be zero.
Secondly we move to the Statistical Methodology tools, which include descriptive statistics,correlation, statistical inference, design of experiments and regression analysis.
Since its inception it has broadened to find applications in many other areas, including statistical inference, natural language processing, cryptography, neurobiology, the evolution and function of molecular codes, model selection in ecology, thermal physics, quantum computing, linguistics, plagiarism detection, pattern recognition, anomaly detection and other forms of data analysis.
Up to now decade or so, programming has started to vary with the growing popularity of machine learning,which entails creating frameworks for machines to study through statistical inference.
Compulsory courses Course Description The objectives of this course is to introduce the students to the basic principles of statistical inference and modelling in order to be able to use them in problems of management science.
Up to now decade or so, programming has began to change with the rising recognition of machine studying,which involves creating frameworks for machines to learn through statistical inference.
In such cases, knowledge that the function values are contaminated by random"noise" leads naturally to algorithms that use statistical inference tools to estimate the"true" values of the function and/or make statistically optimal decisions about the next steps.
In the past decade or so, programming has started to change with the growing popularity of machine learning,which involves creating frameworks for machines to learn via statistical inference.
The principle behind confidence intervals was formulated to provide an answer to the question raised in statistical inference: how do we resolve the uncertainty inherent in results derived from data that are themselves only a randomly selected subset of a population?
In probability and statistics, a probability distribution assigns a probability to each measurable subset of the possible outcomes of a random experiment, survey,or procedure of statistical inference.
The principle behind confidence intervals was formulated to provide an answer to the question raised in statistical inference of how to deal with the uncertainty inherent in results derived from data that are themselves only a randomly selected subset of an entire statistical population of possible datasets.
In a sense, this differs much from the modern meaning of probability, which, in contrast, is a measure of the weight of empirical evidence, andis arrived at from inductive reasoning and statistical inference.
Objectives Upon completion of the learning process the student will be able To apply linear models in observational data To check the suitability of the models,To test the hypotheses of models To develop statistical inference for model parameters To make predictions by using the models To apply various forms of non-linear models to data and to assess their suitability To apply the methodology of Analysis of Variance.
Students learn how to ask appropriate questions, how to collect/extract data effectively, how to summarize, interpret, and draw conclusions from data at hand, andhow to understand the limitations of statistical inference.
Bernhardt, Donthu and Kennett(2000)suggest that another pitfall of many satisfaction studies is the tendency to rely on cross sectional analysis for statistical inference(Anderson, Fornell and Lehmann 1994, provides an exception).
In probability research and statistics probability distribution is a tool which assigns a probability to each measurable subset of the possible outcomes of a random experiment, survey,or procedure of statistical inference.