ENGR 201 • Midterm II • Statistics for Engineers
Bu ders ile ENGR 201 Midterm 2 sınavı için temel konular olan : Discrete Joint Probability, Continuous Joint Probability, Joint Statistics, Sampling Distributions, Central Limit Theorem, Point Estimation of Parameters gib, kavramlarını çok iyi öğreneceksin ve her konu için bolca çıkmış sınav sorusuyla antreman yapacaksın.
Eğitmenler
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.
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.
Paketi Tamamla
🎓 Koç Üniversitesi öğrencilerinin %92'si tüm paketi alarak çalışıyor.
Konular
Special Discrete Probability Distributions (Midterm Review)
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
Geometric Distribution Part 1
Geometric Distribution Part 2
Example 8
Example 9
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 I
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Poisson distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Geometric Distribution 1
Geometric Distribution 2
Geometric Distribution 3
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
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 and Correlation Coefficient
Covariance 1
Covariance 2
Covariance 3
Covariance 4
Variance of Sums
Example 1
Correlation
Example 2
Propagation of Error
Introduction
Computing Uncertainities 1
Example 1
Computing Uncertainties 2
Uncertainties for Functions of One Measurement
Example 2
Uncertainties for Functions of Several Measurements
Example 3
Sample Midterm Problems II
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 and Correlation 1
Covariance and Correlation 2
Covariance and Correlation 3
Covariance and Correlation 4
Covariance and Correlation 5
Covariance and Correlation 6
Central Limit Theorem
Introduction
Example 1
Example 2
Example 3
Example 4
Example 5
Normal Approximation to Binomial Distribution
Normal Approximation to Poisson Distribution
Point Estimation
Introduction
Unbiased Estimators 1
Unbiased Estimators 2
Example 1
Example 2
Example 3
Example 4
Example 5
Efficient Estimators
Example 6
Consistent Estimators 1
Consistent Estimators 2
Consistent Estimators 3
Example 7
Method of Maximum Likelihood
Example 8
Example 9
Example 10
Example 11
Sample Midterm Problems III
Central Limit Theorem 1
Central Limit Theorem 2
Central Limit Theorem 3
Central Limit Theorem 4
Central Limit Theorem 5
Central Limit Theorem 6
Normal Approximation to Binomial 1
Normal Approximation to Binomial 2
Unbiased Estimator 1
Unbiased Estimator 2
Consistent Estimator 1
Consistent Estimator 2
Unbiased and Consistent Estimator 1
Unbiased and Consistent Estimator 2
Maximum Likelihood Estimators 1
Maximum Likelihood Estimators 2
Maximum Likelihood Estimators 3
PAST EXAM QUESTIONS
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Question 8
Değerlendirmeler
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
Special Discrete Probability Distributions (Midterm Review)
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
Geometric Distribution Part 1
Geometric Distribution Part 2
Example 8
Example 9
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 I
Binomial Distribution 1
Binomial Distribution 2
Binomial Distribution 3
Binomial Distribution 4
Poisson distribution 1
Poisson Distribution 2
Poisson Distribution 3
Poisson distribution 4
Geometric Distribution 1
Geometric Distribution 2
Geometric Distribution 3
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
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 and Correlation Coefficient
Covariance 1
Covariance 2
Covariance 3
Covariance 4
Variance of Sums
Example 1
Correlation
Example 2
Propagation of Error
Introduction
Computing Uncertainities 1
Example 1
Computing Uncertainties 2
Uncertainties for Functions of One Measurement
Example 2
Uncertainties for Functions of Several Measurements
Example 3
Sample Midterm Problems II
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 and Correlation 1
Covariance and Correlation 2
Covariance and Correlation 3
Covariance and Correlation 4
Covariance and Correlation 5
Covariance and Correlation 6
Central Limit Theorem
Introduction
Example 1
Example 2
Example 3
Example 4
Example 5
Normal Approximation to Binomial Distribution
Normal Approximation to Poisson Distribution
Point Estimation
Introduction
Unbiased Estimators 1
Unbiased Estimators 2
Example 1
Example 2
Example 3
Example 4
Example 5
Efficient Estimators
Example 6
Consistent Estimators 1
Consistent Estimators 2
Consistent Estimators 3
Example 7
Method of Maximum Likelihood
Example 8
Example 9
Example 10
Example 11
Sample Midterm Problems III
Central Limit Theorem 1
Central Limit Theorem 2
Central Limit Theorem 3
Central Limit Theorem 4
Central Limit Theorem 5
Central Limit Theorem 6
Normal Approximation to Binomial 1
Normal Approximation to Binomial 2
Unbiased Estimator 1
Unbiased Estimator 2
Consistent Estimator 1
Consistent Estimator 2
Unbiased and Consistent Estimator 1
Unbiased and Consistent Estimator 2
Maximum Likelihood Estimators 1
Maximum Likelihood Estimators 2
Maximum Likelihood Estimators 3
PAST EXAM QUESTIONS
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Question 8