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Proof Gauss Markov Theorem

Summary Gauss-Markov Theorem

The Gauss-Markov Theorem is telling us that in a regression model, where the expected value of our error terms is zero, and variance of the error terms is constant and finite and and are uncorrelated for all and the least squares estimator and are unbiased and have minimum variance among all unbiased linear estimators. 366 more words

Statistic

One Line Sunday- Muscle ache

Yesterday I had to run to the bus and now my ankle muscles ache and it’s difficult to walk.

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Multiple linear regression by weighted least squares on the Nspire

When performing data analysis, it is sometimes desirable to assign weights to selected data according to their perceived values. For example, data that are more reliable are assigned a higher weight, or weight value that is inversely proportional to variance of that data value. 233 more words

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Online Shop Perlengkapan Bayi di Indonesia

Selain berbelanja di Toko perlengkapan bayi yang ada di pusat perbelanjaan, Ibu juga bisa memilih berbelanja secara online. Selain bebas dari kemacetan dan rasa letih berkeliling dari satu tempat ke tempat lain, belanja online juga memungkinkan Ibu untuk lebih leluasa dalam hal membandingkan harga serta memperoleh lebih banyak pilihan dalam waktu singkat. 62 more words

Ols

Online Shop Baju Anak di Indonesia

Little Sachi à http://instagram.com/little_sachi

Memproduksi model tertentu dalam kurun waktu tertentu. Jumlah item per koleksi terbatas. Sekali koleksi di-upload, harus cepet-cepatan book melalui WA/Line (tidak melalui IG) agar tidak kehabisan. 36 more words

Ols

Derivation of the Least Squares Estimator for Beta in Matrix Notation

The following post is going to derive the least squares estimator for , which we will denote as . In general start by mathematically formalizing relationships we think are present in the real world and write it down in a formula. 404 more words

Statistic

One Line Sunday- Sunny

Such a sunny day I could go out and photograph but I just won’t be bothered to go.

OLS