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\nCS1 – Actuarial Statistics Core Principles  \nFor 2025 Examinations  \nActuarial Statistics (CS1)  \nSyllabus for the 2025 Examinations  \nThis syllabus includes information to support the study of this subject. It will guide you through what you need to learn, application of learning as well as the skills that you need to develop. Information regarding the assessment of this subject is also included.  \nThis syllabus includes:  \n• Aim of the subject  \n• How this subject links across the Qualifications  \n• Subject topics and topic weightings  \n• Subject objectives  \n• Assessment information  \nAim  \nActuarial Statistics provides a grounding in mathematical and statistical techniques that are of particular relevance to actuarial work.  \nLinks across the Qualifications  \nAssociateship Qualification  \nThe principles introduced in CS1 are further developed in CS2 Risk Modelling and Survival Analysis. CM1 Actuarial Mathematics for Modelling and CM2 Economic Modelling apply the principles and techniques in CS1 to the creation of actuarial and financial models  \nTopics and Topic Weightings  \nThis subject covers the following topics:  \n1. Data Analysis [10%]  \n2. Random variables and distributions [20%]  \n3. Statistical inference [25%]  \n4. Regression Theory and applications [30%]  \n5. Bayesian statistics [15%]  \nObjectives  \n1 Data analysis [10%]  \nProduction of simple visualisations and statistics from a data set.  \n1.1 Describe the purpose and function of data analysis  \n1.1.1 Aims of a data analysis (e.g. descriptive, inferential and predictive)  \n1.1.2 Stages and suitable tools used to conduct a data analysis to solve real-world problems  \n1.1.3 Sources of data and their characteristics, including extremely large data sets  \n1.1.4 Meaning and value of reproducible research and the elements required to ensure a data analysis is reproducible  \nComplete exploratory data analysis  \n1.1.5 Appropriate tools to calculate suitable summary statistics and undertake exploratory data visualisations  \n1.1.6 Interpret and make statistical inferences using Pearson’s, Spearman’s and Kendall’s measures of correlation for bivariate data  \n1.1.7 Principal component analysis to reduce the dimensionality of a complex data set  \n2 Random variables and distributions [20%]  \nThe basic properties and uses of commonly-used probability distributions and the statistical properties of data generated by randomly sampling from a known distribution.  \n2.1 Understand the characteristics of basic univariate distributions and how to generate samples from them  \n2.1.1 Geometric, binomial, negative binomial, hypergeometric, Poisson and uniform discrete distributions on a finite set  \n2.1.2 Normal, lognormal, exponential, gamma, chi-square, t, F, beta and uniform continuous distributions on an interval  \n2.1.3 Evaluation of probabilities and quantiles associated with these distributions (by calculation or using statistical software as appropriate)  \n2.1.4 Poisson process and the connection between the Poisson process and the Poisson distribution  \n2.1.5 Generation of basic discrete and continuous random variables using the inverse transform method  \n2.1.6 Generation of discrete and continuous random variables using statistical software  \n2.2 Determine the characteristics of jointly distributed random variables  \n2.2.1 Probability function or density function for marginal and conditional distributions of jointly distributed random variables  \n2.2.2 The conditions under which random variables are independent  \n2.2.3 Covariance, the correlation and the expected value of a function of two jointly distributed random variables  \n2.2.4 Mean and variance of linear combinations of random variables  \n2.3 Evaluate expectations and conditional expectations  \n2.3.1 Conditional expectation of one random variable given the value of another random variable  \n2.3.2 Mean and variance of a random variable as an expectation of conditional expected v","cbCainwM1LpBTcEk","https://ap.wps.com/l/cbCainwM1LpBTcEk","pdf",154229,"English","# Aim\n# Links across the Qualifications\n# Topics and Topic Weightings\n# Objectives\n## Data Analysis\n## Random variables and distributions\n## Statistical inference","[{\"question\":\"What is the aim of the CS1 Actuarial Statistics subject?\",\"answer\":\"It provides grounding in mathematical and statistical techniques relevant to actuarial work, including practical data analysis and core inference methods.\"},{\"question\":\"How does CS1 link to other qualifications?\",\"answer\":\"CS1 principles are developed further in CS2 Risk Modelling and Survival Analysis, while CM1 and CM2 apply the CS1 principles to actuarial and financial model creation.\"},{\"question\":\"Which major topic areas and weightings are included in CS1?\",\"answer\":\"CS1 covers Data Analysis (10%), Random variables and distributions (20%), Statistical inference (25%), Regression Theory and applications (30%), and Bayesian statistics (15%).\"}]","CS1 - Actuarial Statistics Core Principles - Syllabus for the 2025 Examinations | PDF",1790490575,15]