Linear Programming (LP) is a fundamental branch of OR that deals with optimizing the allocation of limited resources to achieve specific goals. This course, "Operations Research - Linear Programming," provides an in-depth exploration of the theory, methods, and applications of linear programming in real-world scenarios.

This comprehensive course is designed to provide you with a solid foundation in the exciting world of foreign exchange (forex) trading. Whether you're an aspiring trader seeking to enter the financial markets or a seasoned investor looking to diversify your portfolio, this course will equip you with the essential knowledge and skills to understand and navigate the forex market effectively.

By the end of this course, you will have a solid understanding of the foundations of forex market trading. Armed with this knowledge, you'll be ready to embark on your forex trading journey with confidence and a solid foundation for further advanced studies in the field. Let's begin the exciting journey of Forex Trading 101!

This is an advanced course that delves into specialized topics within linear programming, offering students a deeper understanding of unique scenarios and challenges that arise in real-world optimization problems. This course explores various special cases of linear programming, providing students with the tools to formulate, analyze, and solve complex optimization problems with specific constraints. Through a combination of theoretical concepts and practical applications, students will learn to navigate intricate decision-making scenarios efficiently.

In this module, we delve into Analysis of Variance (ANOVA), a statistical method used to compare the means of three or more groups to determine if there are any statistically significant differences between them. The session covers the fundamental concepts of ANOVA, including its purpose and application in hypothesis testing. We explore the key components of the ANOVA model, such as between-group and within-group variability, and discuss the assumptions required for valid results. The module includes practical examples to illustrate the use of ANOVA in various research scenarios and concludes with a summary of the key techniques and interpretations.

In this module, we explore the concepts of correlation and regression analysis, essential tools for understanding and modeling the relationships between variables. The lesson begins with an introduction to correlation, explaining how it measures the strength and direction of the linear relationship between two variables. We then delve into regression analysis, which allows us to predict the value of one variable based on the value of another. You'll learn about the assumptions underlying regression, how to interpret regression coefficients, and how to assess the goodness of fit. Practical examples and applications are provided to solidify your understanding, concluding with a summary of key concepts and techniques.

This online course provides a comprehensive introduction to hypothesis testing, a fundamental concept in statistical inference. Students will learn the principles and steps involved in formulating and testing hypotheses, including defining null and alternative hypotheses, selecting appropriate test statistics, and determining significance levels (p-values). The course will cover various types of hypothesis tests, such as t-tests, chi-square tests, and ANOVA, along with their applications. Through interactive lessons, real-world examples, and hands-on exercises, participants will gain the skills to conduct hypothesis tests, interpret results, and make data-driven decisions with confidence.