Probability Theory and Mathematical Statistics

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For the most part, statistical inference makes propositions about populations, using data drawn from the population of interest via some form of random sampling. More generally, data about a random process is obtained from its observed behavior during a finite period of time. Given a parameter or hypothesis about which one wishes to make inference, statistical inference most often uses:.


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In statistics , regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.


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More specifically, regression analysis helps one understand how the typical value of the dependent variable or 'criterion variable' changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are fixed.

Less commonly, the focus is on a quantile , or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function.

Pluralistic Teaching Practice in Probability Theory and Mathematical Statistics

In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution. Many techniques for carrying out regression analysis have been developed. Familiar methods, such as linear regression , are parametric , in that the regression function is defined in terms of a finite number of unknown parameters that are estimated from the data e. Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions , which may be infinite-dimensional.

Nonparametric statistics are values calculated from data in a way that is not based on parameterized families of probability distributions. They include both descriptive and inferential statistics. The typical parameters are the mean, variance, etc.

Paradoxes in Probability Theory and Mathematical Statistics | Mathematical Association of America

Unlike parametric statistics , nonparametric statistics make no assumptions about the probability distributions of the variables being assessed [ citation needed ]. Non-parametric methods are widely used for studying populations that take on a ranked order such as movie reviews receiving one to four stars. The use of non-parametric methods may be necessary when data have a ranking but no clear numerical interpretation, such as when assessing preferences. In terms of levels of measurement , non-parametric methods result in "ordinal" data. As non-parametric methods make fewer assumptions, their applicability is much wider than the corresponding parametric methods.

In particular, they may be applied in situations where less is known about the application in question. Also, due to the reliance on fewer assumptions, non-parametric methods are more robust. Another justification for the use of non-parametric methods is simplicity.

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In certain cases, even when the use of parametric methods is justified, non-parametric methods may be easier to use. Due both to this simplicity and to their greater robustness, non-parametric methods are seen by some statisticians as leaving less room for improper use and misunderstanding.

Mathematical statistics is a key subset of the discipline of statistics.


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  7. Statistical theorists study and improve statistical procedures with mathematics, and statistical research often raises mathematical questions. Statistical theory relies on probability and decision theory.

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    Mathematicians and statisticians like Gauss , Laplace , and C. Peirce used decision theory with probability distributions and loss functions or utility functions. The decision-theoretic approach to statistical inference was reinvigorated by Abraham Wald and his successors, [7] [8] [9] [10] [11] [12] [13] and makes extensive use of scientific computing , analysis , and optimization ; for the design of experiments , statisticians use algebra and combinatorics.

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    Main article: Probability distribution. Main article: Statistical inference.

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    3. Probability Theory

    About Marek Fisz. Marek Fisz. Books by Marek Fisz. Define a probability generating function, a moment generating function, a cumulant generating function and cumulants, derive them in simple cases, and use them to evaluate moments Define basic discrete and continuous distributions, be able to apply them and simulate them in simple cases Explain the concepts of independence, jointly distributed random variables and conditional distributions, and use generating functions to establish the distribution of linear combinations of independent random variables.

    Explain the concepts of a compound distribution, and apply them. State the central limit theorem, and apply it.

    Pluralistic Teaching Practice in Probability Theory and Mathematical Statistics

    Explain the concepts of random sampling, statistical inference and sampling distribution, and state and use basic sampling distributions. Describe the main methods of estimation and the main properties of estimators, and apply them. Mathematical Statistics with Applications. Costs associated with this module Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.