lines of best fit in econometrics
in this video i explain the underlying purpose for drawing lines of best fit on sample data; principally as a way to estimate population parameters. 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
in this video i explain the underlying purpose for drawing lines of best fit on sample data; principally as a way to estimate population parameters. 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 econometrics vs hard science natural experiments in econometrics populations and samples in econometrics estimators the basics 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 econometrics vs hard science natural experiments in econometrics populations and samples in econometrics estimators the basics 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 econometrics vs hard science natural experiments in econometrics populations and samples in econometrics estimators the basics 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 in this video i explain the underlying purpose for drawing lines of best fit on sample data; principally as a way to estimate population parameters. 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