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CS1 – Actuarial Statistics

CS1 – This will be examined in a set of 2 papers. CS1A is 3 hours paper-based assessment and CS1B is 1 hour 45 minutes’ online exam which R programming along with Statistics and Modelling. Syllabus topics covers – Probability distributions, generating functions, Joint distributions, Conditional expectation, The Central…

4Modules
16Chapters
162Classes
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CS1
Actuarial Statistics
Curriculum

4 modules, 16 chapters, 162 classes

Open a chapter to see every class, or search for a topic.

Module 1Module 2Module 3Module 4

  1. Class 11Uniform distribution – discrete case
  2. Class 12Bernoulli distribution
  3. Class 13Binomial Distribution
  4. Class 14Geometric distribution
  5. Class 15Hyper geometric distribution
  6. Class 16Negative binomial distribution
  7. Class 17Poisson distribution
  8. Class 18Poisson process
  9. Class 19Exponential Distribution
  10. Class 110Waiting time in Poisson process
  11. Class 111Uniform distribution – Continuous case
  12. Class 112Gamma Distribution
  13. Class 113Chi Square Distribution
  14. Class 114Beta Distribution
  15. Class 115Normal Distribution
  16. Class 116Log Normal Distribution
  17. Class 117Inverse transform method for continuous distribution

  1. Class 21Moment Generating function
  2. Class 22Moment Generating function MGF - Calculating Moments
  3. Class 23Moment Generating function MGF - expansion method
  4. Class 24Moment Generating function MGF - Uniqueness property
  5. Class 25Moment Generating function MGF - Linear function
  6. Class 26Cumulant Generating function CGF
  7. Class 27Moment Generating function MGF - Linear combination method

  1. Class 31Joint Probability function - discrete case
  2. Class 311Conditional Probability density function - discrete case
  3. Class 312Problem solving - Joint Probability function - discrete case
  4. Class 32Joint Probability function - Continuous case
  5. Class 321Conditional Probability density function - Continuous case
  6. Class 323Problem solving 1 - Joint Probability function - Continuous case
  7. Class 324Problem solving 2 - Joint Probability function - Continuous case
  8. Class 33Expectation of sum and product
  9. Class 34Covariance
  10. Class 341Correlation coefficient
  11. Class 342Variance of sum
  12. Class 35Convolution
  13. Class 36Moment generating function MGF - linear combination
  14. Class 361Moment generating function MGF - linear combination Exponential and Gamma
  15. Class 362Moment generating function MGF - linear combination- Normal distribution

  1. Class 41Conditional expectation
  2. Class 42Expectation of conditional expectation of y given x
  3. Class 43Variance of conditional distribution of y given x
  4. Class 44Exam type questions and solutions
Module 1 exam-type questions and solutions

  1. Class 51Central limit theorem
  2. Class 52Normal Approximation – Gamma Distribution
  3. Class 53Continuity correction
  4. Class 54Normal Approximation – Binomial Distribution
  5. Class 55Normal Approximation – Poisson Distribution

  1. Class 61Sampling and statistical inference
  2. Class 62Sampling distribution
  3. Class 63The student t distribution
  4. Class 64Difference between two sample means
  5. Class 65F distribution - variance ratio
  6. Class 66F result – sample variance

  1. Class 71Method of estimation
  2. Class 72Method of maximum likelihood estimate (MLE)
  3. Class 73Maximum likelihood estimation - two parameter case
  4. Class 74Maximum likelihood estimation – Incomplete sample - censored
  5. Class 741Maximum likelihood estimation – Incomplete sample - truncated
  6. Class 742Problem solving - maximum likelihood estimation – truncated samples
  7. Class 743Problem solving - maximum likelihood estimation – truncated samples
  8. Class 75Maximum likelihood estimation – Independent samples
  9. Class 76Unbiasedness
  10. Class 77Means Square Error (MSE)
  11. Class 78Cramer – Rao Lower Bound (CRLB)
  12. Class 781Problem solving 1 - CRLB
  13. Class 782Problem solving 2 - CRLB
  14. Class 783Problem solving 3 - CRLB
  15. Class 79Exam type question and answer
  16. Class 791Exam type question and answer

  1. Class 81Confidence interval
  2. Class 82Sample size
  3. Class 83Confidence interval for the Normal distribution – mean and std dev - unknown
  4. Class 84Confidence interval for the variance
  5. Class 85Confidence interval – Binomial Distribution
  6. Class 86Confidence interval – Poisson distribution
  7. Class 87Confidence interval – Two Normal Means
  8. Class 871Confidence interval – Two population variance
  9. Class 872Confidence interval – Two Population Proportions
  10. Class 88Confidence interval – Two Poisson Parameters
  11. Class 89Confidence interval – Paired data
Module 2 exam-type questions and solutions

  1. Class 91Introduction of hypothesis testing – null hypothesis – test statistics – P- values
  2. Class 92Testing the value of population mean
  3. Class 921Testing the value of population variance
  4. Class 93Testing the value of population proportion – Binomial trials
  5. Class 931Testing the value of mean od a Poisson distribution
  6. Class 94Testing the value of the difference between two population mean
  7. Class 941Testing the value of the ratio of two population variances
  8. Class 95Testing the value of the difference between two population proportions - Binomial trials
  9. Class 951Testing the value of the difference between two poisson mean
  10. Class 96Test – Paired data
  11. Class 97Chi – square tests
  12. Class 971Problem Solving 1 - Chi – square tests
  13. Class 98Contingency table - Chi – square tests
  14. Class 981Problem Solving 1- Contingency table - Chi – square tests
  15. Class 99Fisher’s exact test
  16. Class 911Type 1 and II error
  17. Class 912Problem solving type I and II error

  1. Class 101Bivariate correlation analysis – Linear correlation
  2. Class 102Pearsons correlation coefficient
  3. Class 103Spearmans rank correlation coefficient
  4. Class 104The Kendall rank correlation coefficient
  5. Class 105Inference under Pearsons correlation
  6. Class 106Fisher’s transformation of r
  7. Class 107Inference under Spearmans and Kendall rank correlation
  8. Class 108Problem solving pearson, spearman, kendall correlation coefficient
  9. Class 109Exam type question and answer

  1. Class 11A1 Simple Bivariate Linear model – fitted regression line
  2. Class 11A2 Least square estimates
  3. Class 11A3 Problem solving – Fitted regression line
  4. Class 11A4 Residual Sum of Squares and Coefficient of determination
  5. Class 11A5 slope Parameter beta
  6. Class 11A6 Analysis of Variance (ANOVA)
  7. Class 11A7 Mean response and Individual response
  8. Class 11A8 Checking Model – residual analysis
  9. Class 11A9 Problem Solving 2 – linear regression analysis
  10. Class 11A91 exam type question and answer
  11. Class 11A92 exam type question and answer

  1. Class 11B1 Multiple Linear Regression – Fitted model and least square method
  2. Class 11B2 Adjusted R^2 and ANOVA table
  3. Class 11B3 Mean response and Individual response and residual analysis
  4. Class 11B4 problem solving - Multiple Linear Regression
  5. Class 11B5 Forward selection – adjusted R2
Module 3 exam-type questions and solutions

  1. Class 121Generalised linear model – introduction
  2. Class 122Exponential family of distribution
  3. Class 1221Exponential family of distribution – Normal Distribution
  4. Class 1222Exponential family of distribution – Binomial Distribution
  5. Class 1223Exponential family of distribution – Gamma distribution
  6. Class 123Linear predictor – covariates – Variables - interaction between variables
  7. Class 1231Linear predictor – covariates – factor – interaction between variables and factor
  8. Class 124GLM - model fitting – maximum likelihood estimation – Poisson distribution
  9. Class 1241GLM – model fitting – maximum likelihood estimation - exponential distribution
  10. Class 125GLM - saturated model
  11. Class 126GLM - scaled deviance – log likelihood ratio equation
  12. Class 1261GLM - scaled deviance – model comparison – improvement
  13. Class 127GLM – Akaike’s information centre AIC
  14. Class 128GLM - Residual Analysis
  15. Class 129GLM - problem solving 1- Pearson residuals and deviance residuals
  16. Class 1291Exam type question and answer

  1. Class 131Bayes theorem
  2. Class 132Prior and posterior distribution
  3. Class 133Conjugate prior
  4. Class 134Uninformative prior distributions
  5. Class 135Discrete Prior distribution
  6. Class 136Loss function – quadratic loss – MEAN
  7. Class 137Loss Function – Absolute error loss – MEDIAN
  8. Class 138Loss Function – All or Nothing Loss - MODE
  9. Class 139Exam type question and answer

  1. Class 141Credibility theory and credibility factor
  2. Class 142Bayesian credibility – Poisson and Gamma model
  3. Class 143Bayesian credibility – Problem solving- Poisson and Gamma model
  4. Class 144Bayesian credibility – Normal and Normal Model
  5. Class 145Bayesian credibility – problem solving binomial and Beta
  6. Class 146Exam type question and answer
  7. Class 1461Exam type question and answer

  1. Class 151Empirical Bayes Credibility theory Model 1
  2. Class 152Model 1 - Problem Solving1
  3. Class 153Model 1 - Problem Solving2
  4. Class 154Empirical Bayes Credibility theory model 2
  5. Class 155Model 2 - Problem solving1
  6. Class 156Problem Solving 3 – model1
  7. Class 157Problem Solving 2 – model2
Module 4 exam-type questions and solutions

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What’s included

  • Four Module covers entire 16 chapters
  • Each Module have 15 hours classes
  • Explaining the theory and problems step by step
  • Each sessions notes available bottom of class
  • Practice of problems available end of the Chapters
  • Evaluating the progress of the subject by assessment

Other subjects

CS2Risk Modeling And Survival Analysis160 classes
CM1Actuarial mathematics247 classes
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