Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions

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Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions

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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

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Course Description

The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers. Different categories of descriptive measures are introduced and discussed along with the Excel functions to calculate them. The notion of probability or uncertainty is introduced along with the concept of a sample and population data using relevant business examples. This leads us to various statistical distributions along with their Excel functions which are then used to model or approximate business processes. You get to apply these descri... The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers. Different categories of descriptive measures are introduced and discussed along with the Excel functions to calculate them. The notion of probability or uncertainty is introduced along with the concept of a sample and population data using relevant business examples. This leads us to various statistical distributions along with their Excel functions which are then used to model or approximate business processes. You get to apply these descriptive measures of data and various statistical distributions using easy-to-follow Excel based examples which are demonstrated throughout the course. To successfully complete course assignments, students must have access to Microsoft Excel. ________________________________________ WEEK 1 Module 1: Basic Data Descriptors In this module you will get to understand, calculate and interpret various descriptive or summary measures of data. These descriptive measures summarize and present data using a few numbers. Appropriate Excel functions to do these calculations are introduced and demonstrated. Topics covered include: • Categories of descriptive data • Measures of central tendency, the mean, median, mode, and their interpretations and calculations • Measures of spread-in-data, the range, interquartile-range, standard deviation and variance • Box plots • Interpreting the standard deviation measure using the rule-of-thumb and Chebyshev’s theorem ________________________________________ WEEK 2 Module 2: Descriptive Measures of Association, Probability, and Statistical Distributions This module presents the covariance and correlation measures and their respective Excel functions. You get to understand the notion of causation versus correlation. The module then introduces the notion of probability and random variables and starts introducing statistical distributions. Topics covered include: • Measures of association, the covariance and correlation measures; causation versus correlation • Probability and random variables; discrete versus continuous data • Introduction to statistical distributions ________________________________________ WEEK 3 Module 3: The Normal Distribution This module introduces the Normal distribution and the Excel function to calculate probabilities and various outcomes from the distribution. Topics covered include: • Probability density function and area under the curve as a measure of probability • The Normal distribution (bell curve), NORM.DIST, NORM.INV functions in Excel ________________________________________ WEEK 4 Module 4: Working with Distributions, Normal, Binomial, Poisson In this module, you'll see various applications of the Normal distribution. You will also get introduced to the Binomial and Poisson distributions. The Central Limit Theorem is introduced and explained in the context of understanding sample data versus population data and the link between the two. Topics covered include: • Various applications of the Normal distribution • The Binomial and Poisson distributions • Sample versus population data; the Central Limit Theorem
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