Normal distribution is a fundamental concept in descriptive statistics. It is a symmetric probability distribution that is often used to model real-world phenomena. In these lectures, we will explore the basics of normal distribution with simple applications. Educational Goals: 1. Identify distributions as symmetric or skewed.  2. Identify the properties of a normal distribution.  3. Find the area under the standard normal distribution, given various z values. 4. Find probabilities for a normally distributed variable by transforming it into a standard normal variable.  5. Find specific data values for given percentages, using the standard normal distribution.

Revit's massing and site tools provide architects and designers with the capability to create conceptual designs, study the overall form and layout of a building, and integrate it with its surrounding environment. The component tool in Revit empowers users to efficiently add, customize, and manage building elements and equipment, facilitating the creation of comprehensive and coordinated BIM (Building Information Modeling) models for architectural, engineering, and construction projects.

Apply routing rules to create a network plan A practical lab demonstrating how to use networking concepts to connect multiple routers and enable communication between different LANs.

This is introduction about how to start in C++ programming Explain how to start you first C++ program, how to use output statement in C++, and how to deal with variable and data types.

This course builds students’ skills of computer aided architectural design systems. The Students will enhance their computation skills to produce technically clear 2D drawings and focus on using computer presentations and rendering techniques.

In these lecture segments, we will cover various topics related to random variables. The topics include the concept of random variables, discrete and continuous random variables, We will discuss probability mass and density functions (PMFs and PDFs) and cumulative distribution functions (CDFs). Jointly Distributed Random Variables, Conditional PMFs and PDFs, Independence of Random Variables and  Functions of Random Variables.

Educational Goal 1. Learning Unit Conversions. 2. Solving Simple Linear Equations with One Variable. Resource Description: This course covers the fundamental concepts of mathematics. Throughout the lessons, we will focus on learning how to convert units, such as kilograms to grams and kilometers to meters. Additionally, we will explore solving simple linear equations with one variable.

This lecture sequence begins with a discussion of what probability theory is and what the word probability means. Probability theory is, in the most basic sense, a branch of mathematics. We begin with some axioms. We consider models that satisfy these axioms and derive some consequences, which serve as the theorems of this theory. Probability, at the very least, provides us with some guidelines for thinking systematically about uncertain situations. And if our probability model, has some relationship with the real world, then probability theory can be a very useful tool for making real-world predictions and decisions.