STAT 301 • Midterm • Statistics and Probability Theory
Mühendisliğin pek çok dersinde karşına çıkacak Olasılık ve İstatistik kavramlarını tanımak için eşsiz bir fırsat karşında! Bu derste Olasılık kavramını hayatın içerisinden çıkardığımızı ve yavaş yavaş bir bilim haline getirdiğimize tanıklık edeceksin.
Eğitmenler
Ömer Faruk Altun
Co-founder & Head of Education
2011 yılında Endüstri Mühendisliği okumak için başladığım Sabancı Üniversitesi'nden 2018 yılında Bilgisayar Mühendisi olarak mezun oldum. 13 yıldır Altun ismiyle başta Sabancı Üniversitesi olmak üzere çeşitli okullarda Endüstri ve Bilgisayar Mühendisliği alanlarında ders vermekteyim. Unicourse'ta sunduğum derslerin yanında eğitim departmanının da sorumluluğunu üstlenmekteyim.
İhsan Altundağ
Eğitmen
2007 yılında Galatasaray Üniversitesi Bilgisayar Mühendisliği bölümünden birincilikle mezun olduktan sonra Fransa'da Kriptoloji üzerine Fransa hükümeti tarafından verilen bursla yüksek lisans yaptım. Devamında ikinci kez sınava girerek Boğaziçi Matematik bölümünü de bitirdim. Yaklaşık 15 yıldır üniversite öğrencilerine dersler vermekteyim.
Memnuniyet Garantisi
Bu dersi alma kararını senin için kolaylaştıralım. Eğer memnun kalmazsan 30 gün içinde bize ulaş, 3'ten fazla içerik tamamlamadıysan iade alabilirsin.
Tüm koşullarPaketi Tamamla
🎓 Türk Hava Kurumu Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
STAT 301 • Final
Statistics and Probability Theory
Ömer Faruk Altun
1199 TL
STAT 301 • Midterm
Statistics and Probability Theory
Ömer Faruk Altun
1199 TL
Konular
Permutation & Combination & Counting Techniques
Basic Principles of Counting
Counting Examples
Permutations
Permutations Example
Groups and Circular Permutation Example
Identical Objects Example 1
Identical Objects Example 2
Identical Objects Example 3
Combination
n choose r
Committee Example 1
Committee Example 2
Ball Example 1
Ball Example 2
Axioms of Probability
Sample Space and Events
Probability
Axioms of Probability
Some Rules
Coin Example
Dice Example
Card Example 1
Card Example 2
Ball Example
Set Example
Birthday Example
Conditional Probability
Conditioning Events
Total Probability Rule
Example 1
Example 2
Example 3
Example 4
Example 5
Example 6
Bayes' Rule
Bayes' Rule
Bayes' Rule Example 1
Bayes' Rule Example 2
Independence
Independence
Independence Example 1
Independence Example 2
Discrete Random Variables
Random Variables
Probability Mass Function
PMF Example 1
PMF Example 2
Cumulative Distribution Function
CDF Example 1
Expected Value and Variance for Discrete Random Variables
Expected Value
Expected Value Example 1
Expected Value Example 2
Variance
Variance Example 1
Variance Example 2
Special Discrete Probability Distributions
Bernoulli Distribution Part 1
Bernoulli Distribution Part 2
Example 1
Example 2
Binomial Distribution Part 1
Binomial Distribution Part 2
Example 3
Example 4
Poisson Distribution Part 1
Poisson Distribution Part 2
Example 5
Example 6
Poisson Approximation to Binomial Distribution
Example 7
Hypergeometric Distribution
Example 8
Geometric Distribution Part 1
Geometric Distribution Part 2
Example 9
Example 10
Negative Binomial Distribution Part 1
Negative Binomial Distribution Part 2
Example 11
Example 12
Continuous Random Variables
Probability Density Function - PDF
Example 1
Cumulative Distribution Function - CDF
Example 2
Expected Value and Variance for Continuous Random Variables
Expected Value
Expected Value - Example 1
Expected Value - Example 2
Variance
Variance - Example 1
Special Continuous Probability Distributions
Uniform Distribution
Example 1
Example 2
Exponential Distribution
Example 3
Example 4
Memoryless Property
Example 5
Normal Distribution
Standard Normal Distribution
Reading Z Table - Option 1
Reading Z Table - Option 2
Example 6
Sample Midterm Problems
Counting 1
Counting 2
Counting 3
Counting 4
Binomial Theorem
Axioms of Probability 1
Axioms of Probability 2
Axioms of Probability 3
Axioms of Probability 4
Conditional Probability 1
Conditional Probability 2
Conditional Probability 3
Conditional Probability 4
Independence 1
Independence 2
Conditional Probability and Independence
Bayes' Rule 1
Bayes' Rule 2
Bayes' Rule 3
Bayes' Rule 4
Bayes' Rule 5
Bayes' Rule 6
Bayes' Rule 7
Bayes' Rule 8
Bayes' Rule 9
Discrete Random Variables 1
Discrete Random Variables 2
Discrete Random Variables 3
Discrete Random Variables 4
Discrete Random Variables 5
Discrete Random Variables 6
Discrete Random Variables 7
Discrete Random Variables 8
Discrete Random Variables 9
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Binomial Distribution 5
Binomial Distribution 6
Poisson Distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Poisson Approximation to Binomial 1
Hypergeometric Distribution 1
Hypergeometric Distribution 2
Negative Binomial Distribution 1
Negative Binomial - Geometric Distribution
Geometric Distribution 1
Geometric Distribution 2
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Continuous Random Variables 4
Continuous Random Variables 5
Continuous Random Variables 6
Uniform Distribution 1
Uniform Distribution 2
Uniform Distribution 3
Exponential Distribution 1
Exponential Distribution 2
Exponential Distribution 3
Exponential Distribution 4
Exponential Distribution 5
Normal Distribution 1
Normal Distribution 2
Normal Distribution 3
Normal Distribution 4
Normal Distribution 5
Normal Distribution 6
Normal Distribution 7
Değerlendirmeler
Henüz hiç değerlendirme yok.
Ders İçeriği
Permutation & Combination & Counting Techniques
Basic Principles of Counting
Counting Examples
Permutations
Permutations Example
Groups and Circular Permutation Example
Identical Objects Example 1
Identical Objects Example 2
Identical Objects Example 3
Combination
n choose r
Committee Example 1
Committee Example 2
Ball Example 1
Ball Example 2
Axioms of Probability
Sample Space and Events
Probability
Axioms of Probability
Some Rules
Coin Example
Dice Example
Card Example 1
Card Example 2
Ball Example
Set Example
Birthday Example
Conditional Probability
Conditioning Events
Total Probability Rule
Example 1
Example 2
Example 3
Example 4
Example 5
Example 6
Bayes' Rule
Bayes' Rule
Bayes' Rule Example 1
Bayes' Rule Example 2
Independence
Independence
Independence Example 1
Independence Example 2
Discrete Random Variables
Random Variables
Probability Mass Function
PMF Example 1
PMF Example 2
Cumulative Distribution Function
CDF Example 1
Expected Value and Variance for Discrete Random Variables
Expected Value
Expected Value Example 1
Expected Value Example 2
Variance
Variance Example 1
Variance Example 2
Special Discrete Probability Distributions
Bernoulli Distribution Part 1
Bernoulli Distribution Part 2
Example 1
Example 2
Binomial Distribution Part 1
Binomial Distribution Part 2
Example 3
Example 4
Poisson Distribution Part 1
Poisson Distribution Part 2
Example 5
Example 6
Poisson Approximation to Binomial Distribution
Example 7
Hypergeometric Distribution
Example 8
Geometric Distribution Part 1
Geometric Distribution Part 2
Example 9
Example 10
Negative Binomial Distribution Part 1
Negative Binomial Distribution Part 2
Example 11
Example 12
Continuous Random Variables
Probability Density Function - PDF
Example 1
Cumulative Distribution Function - CDF
Example 2
Expected Value and Variance for Continuous Random Variables
Expected Value
Expected Value - Example 1
Expected Value - Example 2
Variance
Variance - Example 1
Special Continuous Probability Distributions
Uniform Distribution
Example 1
Example 2
Exponential Distribution
Example 3
Example 4
Memoryless Property
Example 5
Normal Distribution
Standard Normal Distribution
Reading Z Table - Option 1
Reading Z Table - Option 2
Example 6
Sample Midterm Problems
Counting 1
Counting 2
Counting 3
Counting 4
Binomial Theorem
Axioms of Probability 1
Axioms of Probability 2
Axioms of Probability 3
Axioms of Probability 4
Conditional Probability 1
Conditional Probability 2
Conditional Probability 3
Conditional Probability 4
Independence 1
Independence 2
Conditional Probability and Independence
Bayes' Rule 1
Bayes' Rule 2
Bayes' Rule 3
Bayes' Rule 4
Bayes' Rule 5
Bayes' Rule 6
Bayes' Rule 7
Bayes' Rule 8
Bayes' Rule 9
Discrete Random Variables 1
Discrete Random Variables 2
Discrete Random Variables 3
Discrete Random Variables 4
Discrete Random Variables 5
Discrete Random Variables 6
Discrete Random Variables 7
Discrete Random Variables 8
Discrete Random Variables 9
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Binomial Distribution 5
Binomial Distribution 6
Poisson Distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Poisson Approximation to Binomial 1
Hypergeometric Distribution 1
Hypergeometric Distribution 2
Negative Binomial Distribution 1
Negative Binomial - Geometric Distribution
Geometric Distribution 1
Geometric Distribution 2
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Continuous Random Variables 4
Continuous Random Variables 5
Continuous Random Variables 6
Uniform Distribution 1
Uniform Distribution 2
Uniform Distribution 3
Exponential Distribution 1
Exponential Distribution 2
Exponential Distribution 3
Exponential Distribution 4
Exponential Distribution 5
Normal Distribution 1
Normal Distribution 2
Normal Distribution 3
Normal Distribution 4
Normal Distribution 5
Normal Distribution 6
Normal Distribution 7
Memnuniyet Garantisi
Bu dersi alma kararını senin için kolaylaştıralım. Eğer memnun kalmazsan 30 gün içinde bize ulaş, 3'ten fazla içerik tamamlamadıysan iade alabilirsin.
Tüm koşullarSıkça Sorulan Sorular
Örneğin, Koç Üniversitesi - MATH 101 (Calculus) veya başka bir okulun benzer dersi olsun, paketlerimiz tam da o derse göre tasarlanır. Böylece nokta atışı çalışır, zaman kazanırsın.
Sınava özel videolar —konu anlatımları, çıkmış sorular ve çözümleri, özet notlar—içerir. Sınavda sıkça çıkan soruları hedefler. Eğitmenlerimiz, üniversitenin akademik takvimini takip ederek paketleri sürekli günceller. Böylece, gereksiz detaylarla vakit kaybetmeden başarını artırmaya odaklanabilirsin.