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Statistical Models Cambridge In Statistical And Probabilistic Mathematics 11

Salman Rushdie

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Statistical models are mathematical representations of real-world phenomena. They are used to describe the relationships between variables, and to make predictions about future events. Statistical models are used in a wide variety of fields, including economics, finance, marketing, and healthcare.

Statistical Models (Cambridge in Statistical and Probabilistic Mathematics 11)

Statistical Models (Cambridge Series in Statistical and Probabilistic Mathematics Book 11)

by A. C. Davison

4.3 out of 5

Language	:	English
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Print length	:	736 pages

The Cambridge Statistical and Probabilistic Mathematics 11 course is a one-year course that introduces students to the fundamental concepts of statistical modeling. The course covers a wide range of topics, including:

Descriptive statistics
Inferential statistics
Linear regression
Logistic regression
Time series analysis

The course is designed to provide students with the skills and knowledge necessary to use statistical models to solve real-world problems. Students who successfully complete the course will be able to:

Understand the basic concepts of statistical modeling
Apply statistical models to real-world problems
Interpret the results of statistical analyses
Communicate statistical findings to others

Course Content

The Cambridge Statistical and Probabilistic Mathematics 11 course is divided into four modules:

Module 1: Descriptive statistics
Module 2: Inferential statistics
Module 3: Linear regression
Module 4: Logistic regression and time series analysis

Module 1 introduces students to the basic concepts of descriptive statistics, including measures of central tendency, measures of variability, and graphical representations of data. Module 2 introduces students to the basic concepts of inferential statistics, including hypothesis testing, confidence intervals, and regression analysis.

Module 3 introduces students to the fundamental concepts of linear regression, including the least squares method, the coefficient of determination, and the analysis of variance. Module 4 introduces students to the fundamental concepts of logistic regression and time series analysis, including the logistic function, the autoregressive integrated moving average (ARIMA) model, and the exponential smoothing model.

Assessment

The Cambridge Statistical and Probabilistic Mathematics 11 course is assessed through a combination of coursework and examinations. Coursework составляет 50% of the final grade, and examinations contribute the remaining 50%. Coursework consists of two assignments, which are submitted throughout the year. Examinations consist of a two-hour written examination and a one-hour oral examination.

Resources

There are a number of resources available to help students succeed in the Cambridge Statistical and Probabilistic Mathematics 11 course.

The course textbook is [name of textbook].
The course website contains a wealth of information, including lecture notes, practice questions, and past papers.
The course lecturer is available for office hours to help students with their studies.
There are a number of online resources available to help students with statistical modeling, including [website 1], [website 2], and [website 3].

The Cambridge Statistical and Probabilistic Mathematics 11 course is a challenging but rewarding course that provides students with the skills and knowledge necessary to use statistical models to solve real-world problems. Students who successfully complete the course will be well-prepared for further study in statistics or a related field.

Statistical Models (Cambridge Series in Statistical and Probabilistic Mathematics Book 11)

by A. C. Davison

4.3 out of 5