BLM 2011 • Midterm • Statistics and Probability
Bu ders ile BLM 2011 sınavı için temel konseptleri çok iyi anlamakla kalmayıp sınava girmeye de tamamen hazır olacaksın.
Dersin içeriğinde yer alan Combinatory Analysis, Rules of Probability, Conditional Probability, Independence, Bayes Theorem, Discrete Random Variables, Special Discrete Variables, Continuous Random Variables, Special Continuous Random Variables kavramlarını çok iyi öğreneceksin ve hepsine dair örnek sorular ve geçmiş sınav soruları göreceksin.
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
🎓 Yıldız Teknik Üniversitesinde öğrencilerin %92'si tüm paketi alarak çalışıyor.
BLM 2011 • Final
Statistics and Probability
İhsan Altundağ
1199 TL
BLM 2011 • Midterm
Statistics and Probability
Ömer Faruk Altun
1199 TL
Konular
Combinatorial Analysis
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, Bayes' Rule and Independence
Conditioning Events
Total Probability Rule
Example 1
Example 2
Example 3
Example 4
Example 5
Example 6
Bayes' Rule
Bayes' Rule Example 1
Bayes' Rule Example 2
Independence
Independence Example 1
Independence Example 2
Sample Midterm Part I
Combinatorial Analysis 1
Combinatorial Analysis 2
Combinatorial Analysis 3
Combinatorial Analysis 4
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
Random Variables
Probability Mass Function
PMF Example 1
PMF Example 2
Cumulative Distribution Function
CDF Example 1
Expected Value
Expected Value Example 1
Expected Value Example 2
Variance
Variance Example 1
Variance Example 2
Special Discrete Probability Distributions
Discrete Uniform Distribution Part 1
Discrete Uniform Distribution Part 2
Example 1
Example 2
Bernoulli Distribution Part 1
Bernoulli Distribution Part 2
Example 3
Example 4
Binomial Distribution Part 1
Binomial Distribution Part 2
Example 5
Example 6
Poisson Distribution Part 1
Poisson Distribution Part 2
Example 7
Example 8
Poisson Approximation to Binomial Distribution
Example 9
Geometric Distribution Part 1
Geometric Distribution Part 2
Example 10
Example 11
Hypergeometric Distribution
Example 12
Negative Binomial Distribution Part 1
Negative Binomial Distribution Part 2
Example 13
Example 14
Sample Midterm Part II
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
Discrete Random Variables 10
Discrete Random Variables 11
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Poisson distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Hypergeometric Distribution 1
Hypergeometric Distribution 2
Hypergeometric Distribution 3
Hypergeometric Distribution 4
Discrete Uniform Distribution
Negative Binomial Distribution 1
Negative Binomial - Geometric Distribution
Geometric Distribution 1
Geometric Distribution 2
Geometric Distribution 3
Continuous Random Variables
Probability Density Function - PDF
Example 1
Cumulative Distribution Function - CDF
Example 2
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
Gamma Distribution
Example 7
Relation of Gamma Distribution with Others
Sample Midterm Part III
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
Normal Distribution 1
Normal Distribution 2
Normal Distribution 3
Normal Distribution 4
Normal Distribution 5
Normal Distribution 6
Normal Distribution 7
Central Limit Theorem
Introduction
Example 1
Example 2
Example 3
Example 4
Example 5
Normal Approximation to Binomial Distribution
Normal Approximation to Poisson Distribution
Summary Statistics - Descriptive Statistics
Introduction
Frequency Distribution
Example 1
Relative Frequency Distribution
Example 2
Cumulative Frequency Distribution
Example 3
Frequency Histogram
Measures of Central Tendancy
Example 4
Measures of Dispersion
Example 5
Example 6
Example 7
Sample Midterm Part IV
Central Limit Theorem 1
Central Limit Theorem 2
Central Limit Theorem 3
Normal Approximation to Binomial 1
Normal Approximation to Binomial 2
Descriptive Statistics 1
Descriptive Statistics 2
Descriptive Statistics 3
Descriptive Statistics 4
Değerlendirmeler
Henüz hiç değerlendirme yok.
Ders İçeriği
Combinatorial Analysis
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, Bayes' Rule and Independence
Conditioning Events
Total Probability Rule
Example 1
Example 2
Example 3
Example 4
Example 5
Example 6
Bayes' Rule
Bayes' Rule Example 1
Bayes' Rule Example 2
Independence
Independence Example 1
Independence Example 2
Sample Midterm Part I
Combinatorial Analysis 1
Combinatorial Analysis 2
Combinatorial Analysis 3
Combinatorial Analysis 4
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
Random Variables
Probability Mass Function
PMF Example 1
PMF Example 2
Cumulative Distribution Function
CDF Example 1
Expected Value
Expected Value Example 1
Expected Value Example 2
Variance
Variance Example 1
Variance Example 2
Special Discrete Probability Distributions
Discrete Uniform Distribution Part 1
Discrete Uniform Distribution Part 2
Example 1
Example 2
Bernoulli Distribution Part 1
Bernoulli Distribution Part 2
Example 3
Example 4
Binomial Distribution Part 1
Binomial Distribution Part 2
Example 5
Example 6
Poisson Distribution Part 1
Poisson Distribution Part 2
Example 7
Example 8
Poisson Approximation to Binomial Distribution
Example 9
Geometric Distribution Part 1
Geometric Distribution Part 2
Example 10
Example 11
Hypergeometric Distribution
Example 12
Negative Binomial Distribution Part 1
Negative Binomial Distribution Part 2
Example 13
Example 14
Sample Midterm Part II
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
Discrete Random Variables 10
Discrete Random Variables 11
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Poisson distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Hypergeometric Distribution 1
Hypergeometric Distribution 2
Hypergeometric Distribution 3
Hypergeometric Distribution 4
Discrete Uniform Distribution
Negative Binomial Distribution 1
Negative Binomial - Geometric Distribution
Geometric Distribution 1
Geometric Distribution 2
Geometric Distribution 3
Continuous Random Variables
Probability Density Function - PDF
Example 1
Cumulative Distribution Function - CDF
Example 2
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
Gamma Distribution
Example 7
Relation of Gamma Distribution with Others
Sample Midterm Part III
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
Normal Distribution 1
Normal Distribution 2
Normal Distribution 3
Normal Distribution 4
Normal Distribution 5
Normal Distribution 6
Normal Distribution 7
Central Limit Theorem
Introduction
Example 1
Example 2
Example 3
Example 4
Example 5
Normal Approximation to Binomial Distribution
Normal Approximation to Poisson Distribution
Summary Statistics - Descriptive Statistics
Introduction
Frequency Distribution
Example 1
Relative Frequency Distribution
Example 2
Cumulative Frequency Distribution
Example 3
Frequency Histogram
Measures of Central Tendancy
Example 4
Measures of Dispersion
Example 5
Example 6
Example 7
Sample Midterm Part IV
Central Limit Theorem 1
Central Limit Theorem 2
Central Limit Theorem 3
Normal Approximation to Binomial 1
Normal Approximation to Binomial 2
Descriptive Statistics 1
Descriptive Statistics 2
Descriptive Statistics 3
Descriptive Statistics 4
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.