this video is the second in a series of videos where i derive the least squares estimators from first principles. check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. quite excitingly (for me at least), i am about to publish a whole series of new videos on bayesian statistics on youtube. see here for information: https://ben-lambert.com/bayesian/ accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

this video is the second in a series of videos where i derive the least squares estimators from first principles. check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. quite excitingly (for me at least), i am about to publish a whole series of new videos on bayesian statistics on youtube. see here for information: https://ben-lambert.com/bayesian/ accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti estimator properties unbiasedness and consistency unbiasedness vs consistency of estimators an example efficiency of estimators good estimator properties summary lines of best fit in econometrics the mathematics behind drawing a line of best fit least squares estimators as blue deriving least squares estimators part 1 deriving least squares estimators part 2 deriving least squares estimators part 3 deriving least squares estimators part 4 deriving least squares estimators part 5 least squares estimators in summary taking expectations of a random variable moments of a random variable central moments of a random variable kurtosis skewness expectations and variance properties covariance and correlation population vs sample quantities the population regression function problem set 1 estimators introduction gauss markov assumptions part 1 gauss markov assumptions part 2 zero conditional mean of errors gauss markov assumption omitted variable bias example 1 omitted variable bias example 2 omitted variable bias example 3 omitted variable bias proof part 1 omitted variable bias proof part 2 reverse causality part 1 reverse causality part 2 measurement error in independent variable part 1 measurement error in independent variable part 2 functional misspecification 1 functional misspecification 2 linearity in parameters gauss markov random sample summary gauss markov gauss markov explanation of random sampling and serial correlation serial correlation summary serial correlation as a symptom of omitted variable bias serial correlation as a symptom of functional misspecification motion introduction class 9 physics states motion class 9 physics physical quantities motion class 9 physics distance and displacement motion class 9 physics speed motion class 9 physics uniform and non uniform motion motion class 9 physics average speed and average velocity motion class 9 physics motion ncert numericals part 1 class 9 physics motion ncert numericals part 2 class 9 physics motion ncert numericals part 3 class 9 physics motion ncert numericals part 4 class 9 physics uniform velocity and acceleration motion class 9 physics uniform acceleration motion class 9 physics motion ncert questions class 9 physics motion ncert numericals part 6 class 9 physics graph motion class 9 physics estimator properties unbiasedness and consistency unbiasedness vs consistency of estimators an example efficiency of estimators good estimator properties summary lines of best fit in econometrics the mathematics behind drawing a line of best fit least squares estimators as blue deriving least squares estimators part 1 deriving least squares estimators part 2 deriving least squares estimators part 3 deriving least squares estimators part 4 deriving least squares estimators part 5 least squares estimators in summary taking expectations of a random variable moments of a random variable central moments of a random variable kurtosis skewness expectations and variance properties covariance and correlation population vs sample quantities the population regression function problem set 1 estimators introduction gauss markov assumptions part 1 gauss markov assumptions part 2 zero conditional mean of errors gauss markov assumption omitted variable bias example 1 omitted variable bias example 2 omitted variable bias example 3 omitted variable bias proof part 1 omitted variable bias proof part 2 reverse causality part 1 reverse causality part 2 measurement error in independent variable part 1 measurement error in independent variable part 2 functional misspecification 1 functional misspecification 2 linearity in parameters gauss markov random sample summary gauss markov gauss markov explanation of random sampling and serial correlation serial correlation summary serial correlation as a symptom of omitted variable bias serial correlation as a symptom of functional misspecification motion introduction class 9 physics states motion class 9 physics physical quantities motion class 9 physics distance and displacement motion class 9 physics speed motion class 9 physics uniform and non uniform motion motion class 9 physics average speed and average velocity motion class 9 physics motion ncert numericals part 1 class 9 physics motion ncert numericals part 2 class 9 physics motion ncert numericals part 3 class 9 physics motion ncert numericals part 4 class 9 physics uniform velocity and acceleration motion class 9 physics uniform acceleration motion class 9 physics motion ncert questions class 9 physics motion ncert numericals part 6 class 9 physics graph motion class 9 physics estimator properties unbiasedness and consistency unbiasedness vs consistency of estimators an example efficiency of estimators good estimator properties summary lines of best fit in econometrics the mathematics behind drawing a line of best fit least squares estimators as blue deriving least squares estimators part 1 deriving least squares estimators part 2 deriving least squares estimators part 3 deriving least squares estimators part 4 deriving least squares estimators part 5 least squares estimators in summary taking expectations of a random variable moments of a random variable central moments of a random variable kurtosis skewness expectations and variance properties covariance and correlation population vs sample quantities the population regression function problem set 1 estimators introduction gauss markov assumptions part 1 gauss markov assumptions part 2 zero conditional mean of errors gauss markov assumption omitted variable bias example 1 omitted variable bias example 2 omitted variable bias example 3 omitted variable bias proof part 1 omitted variable bias proof part 2 reverse causality part 1 reverse causality part 2 measurement error in independent variable part 1 measurement error in independent variable part 2 functional misspecification 1 functional misspecification 2 linearity in parameters gauss markov random sample summary gauss markov gauss markov explanation of random sampling and serial correlation serial correlation summary serial correlation as a symptom of omitted variable bias serial correlation as a symptom of functional misspecification motion introduction class 9 physics states motion class 9 physics physical quantities motion class 9 physics distance and displacement motion class 9 physics speed motion class 9 physics uniform and non uniform motion motion class 9 physics average speed and average velocity motion class 9 physics motion ncert numericals part 1 class 9 physics motion ncert numericals part 2 class 9 physics motion ncert numericals part 3 class 9 physics motion ncert numericals part 4 class 9 physics uniform velocity and acceleration motion class 9 physics uniform acceleration motion class 9 physics motion ncert questions class 9 physics motion ncert numericals part 6 class 9 physics graph motion class 9 physics this video is the second in a series of videos where i derive the least squares estimators from first principles. check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. quite excitingly (for me at least), i am about to publish a whole series of new videos on bayesian statistics on youtube. see here for information: https://ben-lambert.com/bayesian/ accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti