Introduction to Probability and Statistics
Introduction to Probability and Statistics, available at $19.99, with 31 lectures, 3 quizzes.
You will learn about Probability theory: Students will gain a solid foundation in probability theory, enabling them to analyze and quantify uncertainty in various scenarios. Statistical inference: Students will learn how to draw conclusions and make predictions based on data. S Data analysis: Essential techniques for analyzing and interpreting data. Students will learn about exploratory data analysis, descriptive statis Probability distributions: Students will delve into the characteristics and applications of various probability distributions. This course is ideal for individuals who are Designed for a broad range of students and professionals who seek to develop a solid understanding of probability and statistics. It is commonly taken by undergraduate students pursuing degrees in mathematics, statistics, engineering, computer science, economics, social sciences, or related fields. Additionally, professionals in fields such as data analysis, finance, market research, and quality control may also benefit from this course to enhance their analytical skills and decision-making abilities. It is particularly useful for Designed for a broad range of students and professionals who seek to develop a solid understanding of probability and statistics. It is commonly taken by undergraduate students pursuing degrees in mathematics, statistics, engineering, computer science, economics, social sciences, or related fields. Additionally, professionals in fields such as data analysis, finance, market research, and quality control may also benefit from this course to enhance their analytical skills and decision-making abilities.
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Summary
Title: Introduction to Probability and Statistics
Price: $19.99
Number of Lectures: 31
Number of Quizzes: 3
Number of Published Lectures: 31
Number of Published Quizzes: 3
Number of Curriculum Items: 38
Number of Published Curriculum Objects: 38
Original Price: $22.99
Quality Status: approved
Status: Live
What You Will Learn
- Probability theory: Students will gain a solid foundation in probability theory, enabling them to analyze and quantify uncertainty in various scenarios.
- Statistical inference: Students will learn how to draw conclusions and make predictions based on data. S
- Data analysis: Essential techniques for analyzing and interpreting data. Students will learn about exploratory data analysis, descriptive statis
- Probability distributions: Students will delve into the characteristics and applications of various probability distributions.
Who Should Attend
- Designed for a broad range of students and professionals who seek to develop a solid understanding of probability and statistics. It is commonly taken by undergraduate students pursuing degrees in mathematics, statistics, engineering, computer science, economics, social sciences, or related fields. Additionally, professionals in fields such as data analysis, finance, market research, and quality control may also benefit from this course to enhance their analytical skills and decision-making abilities.
Target Audiences
- Designed for a broad range of students and professionals who seek to develop a solid understanding of probability and statistics. It is commonly taken by undergraduate students pursuing degrees in mathematics, statistics, engineering, computer science, economics, social sciences, or related fields. Additionally, professionals in fields such as data analysis, finance, market research, and quality control may also benefit from this course to enhance their analytical skills and decision-making abilities.
The Statistics and Probability course is designed to provide students with a comprehensive understanding of fundamental statistical concepts and probabilistic principles. This course equips students with the necessary skills to analyze data, make informed decisions, and draw meaningful conclusions in various fields and real-world scenarios.
The course covers a wide range of topics, starting with the basics of descriptive statistics, where students learn to organize, summarize, and present data using graphical and numerical methods. They gain proficiency in calculating measures of central tendency, such as mean, median, and mode, as well as measures of dispersion, including variance and standard deviation.
Moving on to inferential statistics, students delve into probability theory, understanding the foundations of probability and its applications in uncertainty and randomness. They learn to calculate probabilities of events, study probability distributions, and explore the concepts of independence and conditional probability.
The course further explores discrete probability distributions, focusing on the binomial and Poisson distributions. Students learn how to apply these distributions to model the occurrence of events and understand real-world phenomena, such as the number of successes in a fixed number of trials and the occurrence of rare events in continuous time.
In addition, the course introduces students to the Poisson process, a powerful tool for modeling events that happen randomly and independently over continuous time. Students grasp the process’s key characteristics, including independent increments and varying intensity, and apply it to various fields, including queuing theory, telecommunications, and reliability engineering.
Throughout the course, practical applications and real-world examples are incorporated to help students develop a deep understanding of statistical concepts and their relevance in different domains. Hands-on exercises and projects enable students to gain proficiency in using statistical software and data analysis tools to extract meaningful insights from real datasets.
By the end of the Statistics and Probability course, students will have honed their ability to critically analyze data, perform statistical tests, and draw meaningful conclusions. They will be equipped with a solid foundation in statistical thinking, preparing them to make informed decisions and contribute to various fields, such as business, medicine, social sciences, and engineering.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Examples of Statistical Practice
Lecture 3: Final Words
Chapter 2: Descriptive Statistics
Lecture 1: What You'll Learn
Lecture 2: Dotplots
Lecture 3: Histograms
Lecture 4: Univariate Numerical Statistics
Lecture 5: Boxplots
Lecture 6: Paired Data
Chapter 3: Probability
Lecture 1: Framework
Lecture 2: Set Theory
Lecture 3: Three Definitions of Probability
Lecture 4: Conditional Probability
Lecture 5: Some Counting Rules
Chapter 4: Discrete Distributions
Lecture 1: Random Variables
Lecture 2: Example
Lecture 3: Expectations
Lecture 4: The Binomial Distribution
Lecture 5: The Poisson Distribution
Lecture 6: The Poisson Process
Chapter 5: Continuous Distributions
Lecture 1: CDF
Lecture 2: Expectations for Continuous RVs
Lecture 3: Examples
Lecture 4: The Normal Distribution
Lecture 5: The Normal Approximation to the Binomial
Lecture 6: The Gamma Distribution
Lecture 7: The Exponential Distribution
Lecture 8: Jointly Distributed Random Variables
Lecture 9: Statistics and their Distributions
Lecture 10: The Central Limit Theorem
Chapter 6: Video Review
Lecture 1: Video that encompasses the entire course
Instructors
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Theo TT
Instructor at Udemy
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Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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