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• Category: Advanced Statistics ST2133
  • The magic of the distribution function
  • Final exam in Advanced Statistics ST2133, 2022
  • A problem to do once and never come back
  • Marginal probabilities and densities
  • Midterm Spring 2022
  • Estimation of parameters of a normal distribution
  • Sufficiency and minimal sufficiency
  • Chi-squared distribution
  • Gamma distribution
  • Gamma function
  • Sum of random variables and convolution
  • Leibniz integral rule
• Category: Advanced Statistics ST2134
  • Distribution of the estimator of the error variance
• Category: Agresti & Franklin
  • Conditional variance properties
  • Multiple regression through the prism of dummy variables
  • Testing for structural changes: a topic suitable for AP Stats
  • It’s time to modernize the AP Stats curriculum
  • Ditch statistical tables if you have a computer
  • Nonparametric estimation for AP Stats
  • Properties of correlation
  • The pearls of AP Statistics 36
  • Statistical measures and their geometric roots
  • Properties of standard deviation
  • The pearls of AP Statistics 35
  • The pearls of AP Statistics 34
  • Properties of variance
  • The pearls of AP Statistics 33
  • Properties of means
  • The pearls of AP Statistics 32
  • The pearls of AP Statistics 31
  • The pearls of AP Statistics 30
  • The pearls of AP Statistics 29
  • The pearls of AP Statistics 28
  • The pearls of AP Statistics 27
  • The pearls of AP Statistics 26
  • The pearls of AP Statistics 25
  • The pearls of AP Statistics 24
  • The pearls of AP Statistics 23
  • The pearls of AP Statistics 22
  • The pearls of AP Statistics 21
  • The pearls of AP Statistics 20
  • The pearls of AP Statistics 19
  • The pearls of AP Statistics 18
  • The pearls of AP Statistics 17
  • The pearls of AP Statistics 16
  • The pearls of AP Statistics 15
  • The pearls of AP Statistics 14
  • The pearls of AP Statistics 12
  • The pearls of AP Statistics 11
  • The pearls of AP Statistics 10
  • The pearls of AP Statistics 9
  • The pearls of AP Statistics 8
  • The pearls of AP Statistics 7
  • The pearls of AP Statistics 6
  • The pearls of AP Statistics 5
  • The pearls of AP Statistics 4
  • The pearls of AP Statistics 3
  • The pearls of AP Statistics 2
  • The pearls of AP Statistics 1
  • Properties of conditional expectation
  • What is a p value?
• Category: Agresti and Franklin
  • Definitions of chi-square, t statistic and F statistic
  • The pearls of AP Statistics 13
• Category: AP Stats and Business Stats
  • Analysis of problems with conditioning
  • My book is gaining international recognition
  • My book in Basic Statistics
  • Statistical calculator
  • My book milestone
  • Music for work and pleasure
  • A singer you shouldn't miss
  • AP Statistics the Genghis Khan way 2
  • AP Statistics the Genghis Khan way 1
  • Little tricks for AP Statistics
  • Law of total probability - you could have invented this
  • Distribution function estimation
  • Intro to option greeks: delta and its determinants
  • Interest rate - the puppetmaster behind option prices
  • How to study mt3042 Optimisation: a guide to a guide
  • Finite Horizon Dynamic Programming
  • Reevaluating probabilities based on piece of evidence
  • Significance level and power of test
  • Violations of classical assumptions 2
  • Alternatives to simple regression in Stata
  • Running simple regression in Stata
  • Examples of distribution functions
  • Density function properties
  • The pearls of AP Statistics 37
  • Gauss-Markov theorem
  • Regressions with stochastic regressors 2
  • The law of large numbers proved
  • Inductive introduction to Chebyshev inequality
  • Regressions with stochastic regressors 1
  • OLS estimator variance
  • Properties of covariance
  • All you need to know about the law of large numbers
  • Proving unbiasedness of OLS estimators
  • How to save on my book
  • Derivation of OLS estimators: the do's and don'ts
  • What is cointegration?
  • What is a mean value - all means in one place
  • Summation sign rules: identities for simple regression
  • Simple regression - before and after estimation
  • OLS estimator for multiple regression - simplified derivation
  • Teaching methodology dilemma: Is lecturing good or bad?
  • What is an OLS estimator - simplified derivation
  • What is a Pareto chart?
  • What is a binomial random variable - analogy with market demand
  • What is a z score: the scientific explanation
  • Scaling a distribution
  • Mean plus deviation-from-mean decomposition
  • Active learning - away from boredom of lectures
  • Population mean versus sample mean
  • Modeling a pair of random variables and scatterplot definition - Exercise 2.5
  • Histogram versus time series plot - Example 2.2
  • Modeling a sample from a normal distribution in Excel - Exercise 2.4
  • Creating frequency table and histogram and using Excel macros - Exercise 2.3
  • Simulating the binomial variable - Exercise 2.2
  • Generating Bernoulli random variable (coin) in Excel - Exercise 2.1
  • First message
• Category: Book reviews
  • Review of mt3042 Optimization Guide by M. Baltovic
  • Review of Dougherty "Introduction to Econometrics"
  • Review of Hinders "5 steps"
  • Review of Bock, Velleman, De Veaux
  • Review of Newbold, Carlson and Thorne
  • Review of Albert and Rossman
  • Review of Agresti and Franklin
• Category: Dimash Kudaibergen
  • Another opus from Dimash
• Category: Dougherty Introduction to Econometrics
  • Distributions derived from normal variables
  • Application: Ordinary Least Squares estimator
  • Violations of classical assumptions 1
  • Nonlinear least squares: idea, geometry and implementation in Stata
  • Introduction to Stata
  • Autoregressive–moving-average (ARMA) models
  • Moving average processes
  • Autoregressive processes
  • Stationary processes 2
  • Stationary processes 1
  • Nonstationary processes 2
  • Error correction model
  • Maximum likelihood: application to linear model
  • Distribution and density functions of a linear transformation
  • Maximum likelihood: idea and life of a bulb
  • Binary choice models: theoretical obstacles
  • Binary choice models
  • Distribution function properties
  • Durbin-Wu-Hausman test
  • Instrumental variables estimator
  • What is a stationary process?
• Category: EC2020
  • Application: distribution of sigma squared estimator
• Category: EC2020 Elements of econometrics
  • My presentation at Kazakh National University
  • Application: estimating sigma squared
  • Different faces of vector variance: again visualization helps
  • Variance of a vector: motivation and visualization
• Category: Econometrics
  • Nonstationary processes 1
• Category: FN3142 Quantitative Finance
  • Strategies for the crashing market
  • Vector autoregression (VAR)
  • Vector autoregressions: preliminaries
  • Solution to Question 1 from UoL exam 2020
  • Put debit spread
  • Call debit spread
  • Solution to Question 2 from UoL exam 2018, Zone B
  • Solution to Question 2 from UoL exam 2019, zone B
  • Solution to Question 3 from UoL exam 2019, zone A
  • FN3142 Chapter 14 Risk management and Value-at-Risk: Backtesting
  • FN3142 Chapter 13. Risk management and Value-at-Risk: Models
  • FN3142 Chapter 12. Forecast comparison and combining
  • Leverage effect: the right definition and explanation
  • Question 1 from UoL exam 2016, Zone B, Post 2
  • Question 1 from UoL exam 2016, Zone B, Post 1
  • Solution to Question 3b) from UoL exam 2018, Zone A
  • Checklist for Quantitative Finance FN3142
  • Law of iterated expectations: geometric aspect
  • Law of iterated expectations: informational aspect
  • Portfolio analysis: return on portfolio
  • Applications of the diagonal representation IV
  • Efficient market hypothesis is subject to interpretation
  • The Newey-West estimator: uncorrelated and correlated data
  • Solution to Question 2 from UoL exam 2016, zone A
  • Sampling from uniform distribution - example of convolution
  • Density of a sum of independent variables is given by convolution
  • Solution to Question 1 from UoL exam 2017, Zone B
  • Solution to Question 1 from UoL exam 2016, Zone B
  • Solution to Question 1 from UoL exam 2016, Zone A
  • Expected shortfall is next after Value at Risk
  • Conditional expectation generalized to continuous random variables
  • Value at Risk and its calculation for a normal distribution
  • Visualization of payoffs on calls and puts
  • Intro to option greeks: theta, intrinsic value and time value
  • Option chain and efficient market hypothesis
  • Volatility - king among option price determinants
  • Call options and probabilistic intuition - dependence on time
  • Call options and probabilistic intuition - dependence on stock price
  • Call options and probabilistic intuition - dependence on strike
  • Old versus new tools in Quantitative Finance
  • Canonical form for time series
  • Conditional-mean-plus-remainder representation
• Category: Matrix algebra
  • Sylvester's criterion
  • Gaussian elimination method
  • Elementary transformations
  • Main theorem: Jordan normal form
  • Playing with bases
  • Chipping off root subspaces
  • Action of a matrix in its root subspace
  • Properties of root subspaces
  • Direct sums of subspaces
  • Correctness of the space dimension definition
  • Determinants: questions for repetition
  • Laplace expansion
  • Cramer's rule and invertibility criterion
  • Determinant of a transpose
  • Multilinearity in columns
  • Determinant of a product
  • Leibniz formula for determinants
  • Properties of permutation matrices
  • Permutation matrices
  • Properties IV-VI
  • Axioms 1-3 and Properties I-III
  • Determinants: starting simple
  • Questions for repetition
  • Eigenvalues and eigenvectors of a projector
  • Constructing a projector onto a given subspace
  • Geometry and algebra of projectors
  • Questions for repetition
  • Questions for repetition
  • Applications of the diagonal representation III
  • Applications of the diagonal representation II
  • Applications of the diagonal representation I
  • Diagonalization of symmetric matrices
  • General properties of symmetric matrices
  • Eigenvalues and eigenvectors
  • Orthogonal matrices
  • Matrix similarity
  • Final touches on linear independence
  • Law and order in the set of matrices
  • Complex numbers: time to turn on the beacon
  • Summary and questions for repetition
  • Rank of a matrix and the rank-nullity theorem
  • Basis and dimension
  • Linear dependence of vectors: definition and principal result
  • Solvability of an equation with a square matrix
  • Is the inverse of a linear mapping linear?
  • Geometry of linear equations: questions for repetition
  • Geometry of linear equations: orthogonal complement and equation solvability
  • Geometry of linear equations: structure of image and null space
  • Geometry of linear equations: linear spaces and subspaces
  • Geometry of linear equations: matrix as a mapping
  • Euclidean space geometry: questions for repetition
  • Euclidean space geometry: Cauchy-Schwarz inequality
  • Euclidean space geometry: scalar product, norm and distance
  • Euclidean space geometry: vector operations
  • Matrix algebra: questions for repetition
  • Matrix transposition: continuing learning by doing
  • From invertibility to determinants: argument is more important than result
  • Matrix inversion: doing some housekeeping at elementary level
  • Roadmap for studying matrix multiplication
  • Vector and matrix multiplication
  • Matrix notation and summation
• Category: MT2175 Further linear algebra
• Category: MT3042 Optimisation theory
  • From minimum to infimum: Math is just a logical game
  • See if this definition of a function is better than others
  • Logical structure of definitions
  • Solution to exercise 6.1: how to use homogeneity
  • The right solution to Example 6.5
  • The economical way to use the Kuhn-Tucker theorem
  • Cauchy-Schwarz inequality and optimization 2
  • Cauchy-Schwarz inequality and optimization 1
  • Consumption-Savings Problem
  • Using convexity in optimization
  • The Kuhn-Tucker theorem ins and outs
  • The Kuhn-Tucker theorem: a first look
  • The Lagrangian multiplier interpretation
  • Lagrange method: case of many equality constraints
  • Lagrange method: sufficient conditions
  • Lagrange method: necessary condition
  • Importance of implicit function theorem for optimization
  • Optimization with constraints: economic and financial examples
  • Unconstrained optimization on the plane 2
  • Unconstrained optimization on the plane 1
  • Taylor decomposition for unconstrained optimization
  • The Cobb-Douglas function and level sets
  • Geometry behind optimization
  • Geometry related to derivatives
• Category: Optimisation theory
• Category: Option properties
• Category: Quantitative Finance
• Category: ST104A
• Category: ST104B
• Category: Statistics 1
• Category: Statistics 2
• Category: The College Board
• Category: Uncategorized
  • Ode to a living Teacher
  • Order Relations
  • People need real knowledge
  • Equivalence Relations
  • Microsoft Teams is a jail

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