MATH 203 • Tüm SınavlarIntroduction to Probability

Bu ders ile MATH 203 sınavı için temel konseptleri çok iyi anlamakla kalmayıp sınava girmeye de tamamen hazır olacaksın.

Dersin içeriğinde yer alan Permutation, Combination, Binomial Theorem, Rules of Probability, Conditional Probability, Independence, Bayes Theorem, Random Variable, PDF, CDF, Expected Value, Variance, Binomial Distribution ve Poisson Distribution kavramlarını çok iyi öğreneceksin ve hepsine dair en az birer örnek soru göreceksin.

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119 soru çözümü
172 konu anlatımı · 29 sa 44 dk

Eğitmenler

Ömer Faruk Altun

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

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.

Konular

Ders Tanıtımı

Basic Principles of Counting

Ücretsiz

Counting Examples

Permutations

Permutations Example

Groups and Circular Permutation Example

Identical Objects Example 1

Combination

n choose r

Committee Example

Ball Example

Sample Space and Events

Ücretsiz

Probability

Axioms of Probability

Some Rules

Coin Example

Dice Example

Card Example 1

Card Example 2

Ball Example

Set Example

Conditioning Events

Total Probability Rule

Example 1

Example 2

Example 3

Example 4

Bayes' Rule

Bayes' Rule Example 1

Bayes' Rule Example 2

Independence

Independence Example 1

Independence Example 2

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

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

Probability Density Function - PDF

Ücretsiz

Example 1

Cumulative Distribution Function - CDF

Example 2

Expected Value

Expected Value - Example 1

Expected Value - Example 2

Variance

Variance - Example 1

Introduction

Formulas

Properties

Example 1

Example 2

Example 3

Example 4

Axioms of Probability 1 (Spring 2022)

Axioms of Probability 2 (Fall 2022)

Axioms of Probability 3 (Spring 2022)

Conditional Probability (Spring 2023)

Ücretsiz

Conditional Probability and Independence 1 (Fall 2022)

Conditional Probability and Independence 2 (Spring 2022)

Bayes' Rule 1 (Fall 2022)

Bayes' Rule 2 (Spring 2022)

Bayes' Rule 3 (Spring 2023)

Discrete Random Variables 1 (Fall 2022)

Discrete Random Variables 2 (Spring 2023)

Continuous Random Variables 1 (Fall 2022)

Continuous Random Variables 2 (Spring 2023)

Poisson Distribution 1 (Spring 2022)

Poisson Distribution 2 (Fall 2022)

Ücretsiz

Binomial Distribution (Fall 2022)

Poisson Approximation to Binomial (Spring 2023)

Moment Generating Function 1 (Fall 2022)

Moment Generating Function 2 (Spring 2022)

Moment Generating Function 3 (Spring 2023)

Bayes' Rule

Discrete Random Variables 1

Discrete Random Variables 2

Binomial Distribution

Continuous Random Variables 1 (Midterm I'de yok!)

Continuous Random Variables 2 (Midterm I'de yok!)

Counting 1

Ücretsiz

Counting 2

Bayes' Rule 1

Bayes' Rule 2

Discrete Random Variable 1

Discrete Random Variable 2

Continuous Random Variables 1

Continuous Random Variables 2

Binomial Distribution 1

Binomial Distribution 2

Poisson Distribution

Ücretsiz

Negative Binomial & Geometric Distribution

Negative Binomial Distribution

Uniform Distribution

Ücretsiz

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

Normal Approximation to Binomial Distribution

Example 7

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

Introduction

Marginal PDF and CDF

Expected Value and Variance

Conditional PDF and CDF

Conditional Expectation

Example 1

Example 2

Example 3

Covariance

Example 1 (Discrete)

Example 2 (Continuous)

Example 3 (Continuous)

Variance of Sums

Example 4

Correlation

Example 5

Exam Like Question 3

Multinomial Distribution

Example 1

Example 2

Multivariate Hypergeometric Distribution

Example 3

Example 4

Bivariate Normal Distribution

Example 5

Example 6

Normal Distribution 1 (Spring 2022)

Normal Distribution 2 (Fall 2022)

Normal Distribution 3 (Spring 2023)

Discrete Joint Probability 1 (Spring 2022)

Discrete Joint Probability 2 (Fall 2022)

Continuous Joint Probability 1 (Spring 2022)

Ücretsiz

Continuous Joint Probability 2 (Fall 2022)

Continuous Joint Probability 3 (Spring 2023)

Continuous Joint Probability 4 (Spring 2023)

Bivariate Normal Distribution (Fall 2022)

Joint Statistics 1 (Fall 2022)

Ücretsiz

Joint Statistics 2 (Fall 2022)

Joint Statistics 3 (Fall 2022)

Ücretsiz

Joint Statistics 4 (Spring 2023)

Exponential Distribution

Continuous Joint Probability 1

Continuous Joint Probability 2

Discrete Joint Probability

Normal Approximation to Binomial

Multinomial Distribution

Exponential Distribution

Discrete Joint Probability

Covariance & Continuous Joint Probability

Discrete Joint Probability & Multinomial Distribution

Continuous Joint Probability

Continuous Joint Probability

Covariance & Discrete Joint Probability

Linear Combination of Normal Random Variables

Multinomial Distribution

Continuous Joint Probability

Uniform Distribution 1

Uniform Distribution 2

Exponential Distribution 1

Exponential Distribution 2

Normal Distribution 1

Normal Distribution 2

Discrete Joint Probability 1

Ücretsiz

Discrete Joint Probability 2

Continuous Joint Probability 1

Continuous Joint Probability 2

Joint Statistics

Distribution Function Techniques

Example 1

Example 2

Example 3

Example 4

Example 5

Reading Z Table Option 1

Reading Z Table Option 2

Sample Mean

Sample Variance

Example 1

Ücretsiz

Law of Large Numbers

Central Limit Theorem

Example 2

Example 3

Example 4

Example 5

Example 6

Example 7

Normal Approximation to Binomial Distribution

Normal Approximation to Poisson Distribution

Chapter Summary

Everything You Need to Know!

Example 1

Example 2

Example 3

Confidence Interval for Means(Sigma known)

Example 1

Example 2

Example 3

Confidence Interval for Means (sigma unknown)

Example 4

Example 5

What are we doing?

Ücretsiz

Terminology

Ücretsiz

Example 1

Ücretsiz

Example 2

Example 3

Question 1

Question 2

Question 3

Ücretsiz

Question 4

Question 5

Question 6

Question 7

Question 8

Question 1

Question 2

Question 3

Question 4

Question 5

Question 6

Question 7

Question 8

Ücretsiz

Bayes' Rule

Linear Combination of Normal Random Variables

Continuous Joint Probability

Distribution Function Tecnique

Central Limit Theorem

Confidence Interval

Distribution Function Technique 1

Distribution Function Technique 2

Distribution Function Technique 3

Distribution Function Technique 4

Distribution Function Technique 5

Distribution Function Technique 6

Central Limit Theorem 1

Central Limit Theorem 2

Central Limit Theorem 3

Central Limit Theorem 4

Normal Approximation to Binomial 1

Normal Approximation to Binomial 2

Normal Approximation to Binomial 3

Confidence Interval for Mean 1

Confidence Interval for Mean 2

Hypothesis Testing

MATH 203 Tüm Sınavlar Hakkında Sıkça Sorulan Sorular

Sıkça Sorulan Sorular

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