MATH 240 • Midterm • Probability and Statistics for Engineers and Scientists
Olasılıkla başlayıp, İstatistikle biten bu dersimizde özet ve uygulamaları konu anlatımlarıyla temelleri atıyor; sayısız çözümlü soru örneğiyle sınavlara hazır hale geliyoruz!
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.
Paketi Tamamla
🎓 Bilgi Üniversitesi öğrencilerinin %92'si tüm paketi alarak çalışıyor.
Konular
Descriptive Statistics
Introduction
Frequency Distribution
Example 1
Relative Frequency Distribution
Example 2
Cumulative Frequency Distribution
Example 3
Frequency Histogram
Measures of Central Tendancy
Example 7
Measures of Dispersion
Example 8
Example 9
Example 10
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 Problems I
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
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
Sample Midterm Problems 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
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Continuous Random Variables 4
Continuous Random Variables 5
Continuous Random Variables 6
Continuous Random Variables 7
Continuous Random Variables 8
Continuous Random Variables 9
Continuous Random Variable 10
Special Discrete 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
Special Continuous 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 III
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
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
Continuous Uniform Distribution 1
Continuous Uniform Distribution 2
Continuous 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
Discrete Joint Probability
Probability Mass Function
PMF Example
Marginal PMF and CDF
Expected Value
Variance
Expected Value and Variance Example
Conditional PMF and CDF
Conditional Expectation
End of Topic Example - Part I
End of Topic Example - Part II
Continuous Joint Probability
Introduction
Marginal PDF and CDF
Expected Value and Variance
Conditional PDF and CDF
Conditional Expectation
Example 1
Example 2
Example 3
Covariance
Covariance
Example 1 (Discrete)
Example 2 (Continuous)
Example 3 (Continuous)
Variance of Sums
Example 4
Sample Midterm Problems IV
Discrete Joint Probability 1
Discrete Joint Probability 2
Discrete Joint Probability 3
Discrete Joint Probability 4
Discrete Joint Probability 5
Continuous Joint Probability 1
Continuous Joint Probability 2
Continuous Joint Probability 3
Continuous Joint Probability 4
Continuous Joint Probability 5
Continuous Joint Probability 6
Continuous Joint Probability 7
Continuous Joint Probability 8
Continuous Joint Probability 9
Continuous Joint Probability 10
Covariance
Covariance
Covariance
Sample Midterm
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Değerlendirmeler
Henüz hiç değerlendirme yok.
Sıkça 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.
Ders İçeriği
Descriptive Statistics
Introduction
Frequency Distribution
Example 1
Relative Frequency Distribution
Example 2
Cumulative Frequency Distribution
Example 3
Frequency Histogram
Measures of Central Tendancy
Example 7
Measures of Dispersion
Example 8
Example 9
Example 10
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 Problems I
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
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
Sample Midterm Problems 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
Continuous Random Variables 1
Continuous Random Variables 2
Continuous Random Variables 3
Continuous Random Variables 4
Continuous Random Variables 5
Continuous Random Variables 6
Continuous Random Variables 7
Continuous Random Variables 8
Continuous Random Variables 9
Continuous Random Variable 10
Special Discrete 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
Special Continuous 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 III
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
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
Continuous Uniform Distribution 1
Continuous Uniform Distribution 2
Continuous 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
Discrete Joint Probability
Probability Mass Function
PMF Example
Marginal PMF and CDF
Expected Value
Variance
Expected Value and Variance Example
Conditional PMF and CDF
Conditional Expectation
End of Topic Example - Part I
End of Topic Example - Part II
Continuous Joint Probability
Introduction
Marginal PDF and CDF
Expected Value and Variance
Conditional PDF and CDF
Conditional Expectation
Example 1
Example 2
Example 3
Covariance
Covariance
Example 1 (Discrete)
Example 2 (Continuous)
Example 3 (Continuous)
Variance of Sums
Example 4
Sample Midterm Problems IV
Discrete Joint Probability 1
Discrete Joint Probability 2
Discrete Joint Probability 3
Discrete Joint Probability 4
Discrete Joint Probability 5
Continuous Joint Probability 1
Continuous Joint Probability 2
Continuous Joint Probability 3
Continuous Joint Probability 4
Continuous Joint Probability 5
Continuous Joint Probability 6
Continuous Joint Probability 7
Continuous Joint Probability 8
Continuous Joint Probability 9
Continuous Joint Probability 10
Covariance
Covariance
Covariance
Sample Midterm
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6