Judul Buku: Teori Dan Soal Statistika Dan Probabilitas Penulis: Luluk Kholisoh Penerbit: Gunadarma Tahun: 1994 Bahasa: Bahasa Indonesia Halaman: 136 Format File: pdf Ukuran File: 3,49 MB Teori dan Soal: Statistika dan Probabilitas. Buku Teori Dan Soal - Statistka Dan Probabilitas ini terdiri dari 5 Bab. Membahas tentang: Teknik Penarikan Sampel; Regresi Dan Korelasi Sederhana; Regresi Dan Korelasi Berganda; Analisis Varians; serta Statistik Non Parametrik. Daftar Isi: DAFTAR ISI BAB 1 TEKNIK PENARIKAN SAMPEL 1.1. PENDAHULUAN 1.2. KONSEP DASAR TEKNIK PENARIKAN SAMPEL 1.3. SAMPEL ACAK SEDERHANA & LATIHAN SOAL 1.4.

SAMPEL ACAK BERSTRATA & LATIHAN SOAL 1.5. SAMPEL ACAK BERKELOMPOK & LATIHAN SOAL 1.6. SAMPEL ACAK SISTEMATIK, LATIHAN SOAL & SOAL-SOAL MULTIPLE CHOICE BAB 2 REGRESI DAN KORELASI SEDERHANA 2.1. PENDAHULUAN 2.2. METODE KUADRAT MINIMUM (LEAST SQUARE) 2.3. STANDARD ERROR OF ESTIMASI 2.4. INRFERENSI MENGENAI KEMIRINGAN (B) DARI SUATU GARIS 2.5.

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KORELASI DAN KOEFISIEN KORELASI 2.6. GARIS REGRESI X, TERHADAP Y LATIHAN SOAL DAN MULTIPLE CHOICE BAB 3 REGRESI DAN KORELASI BERGANDA 3.1.

PENDAHULUAN 3.2. PERSAMAAN GARIS LINIER REGRESI 3.3.

COEFISIENTS ESTIMATE 3.4. STANDARD DEVIASI GARIS REGRESI BERGANDA 3.5. KOEFISIEN KORELASI BERGANDA LINIER 3.6. PENDUGA PARAMETER KOEFISIEN REGRESI BERGANDA 3.7.

PENGUJIAN PARAMETER KOEFISIEN REGRESI BERGANDA LATIHAN SOAL DAN MULTIPLE CHOICE BAB 4 ANALISIS VARIANS 4.1. PENDAHULUAN 4.2.

ANALISIS VARIANS SATU ARAH (ANOVA SEDERHANA) 4.3. ANALISIS VARIANS DUA ARAH (RANCANGAN BLOK ACAK) LATIHN SOAL DAN MULTIPLE CHOICE DAFTAR PUSTAKA LAMPIRAN TABEL Link Download.

By, KDnuggets. Note that, while there are numerous machine learning ebooks available for free online, including many which are very well-known, I have opted to move past these 'regulars' and seek out lesser-known and more niche options for readers. Interested in a career in machine learning? Don't know where to start? Well, there's always, a collection of tutorials on pursuing machine learning in the Python ecosystem. If you are looking for something more, you could look for an overview of MOOCs and online lectures from freely-available university lectures. Of course, nothing substitutes rigorous formal education, but let's say that isn't in the cards for whatever reason.

Not all machine learning positions require a PhD; it really depends where on the machine learning spectrum one wants to fit in. Check out, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year. Looking to strike a balance between what you would learn in an introductory graduate school machine learning regimen and what you can get from online tutorials? As they have been for hundreds of years, books are a great place to turn.:) Of course, today we have instant access to freely-available digital books, which makes this a very attractive alternative. Have a look at the following free ebooks, all of which are appropriate for an introductory level of understanding, but which also cover a variety of different concepts and material.

Nilsson of Stanford put these notes together in the mid 1990s. Before you turn up your nopse at the thought of learning from something from the 90s, remember that foundation is foundation, regardless of when it was written about. More voices for balabolka android studio. Sure, many important advancements have been made in machine learning since this was put together, as Nilsson himself says, but these notes cover much of what is still considered relevant elementary material in a straightforward and focused manner. There are no diversions related to advancements of the past few decades, which authors often want to cover tangentially even in introductory texts. There is, however, a lot of information about statistical learning, learning theory, classification, and a variety of algorithms to whet your appetite.