• views: 210
• date: 23 July 2022
• posted by: voska89

An easy-to-understand course covering the fundamentals of statistics and their applications to business
What you'll learn
The Representation of Data in order to paint a clear picture of what a dataset is telling us (stem-and-leaf diagrams, box-and-whisker plots, histograms and cumulative frequency graphs);
Measures of Location in order to estimate the most likely outcome of an experiment (mean, median, mode);
Measures of Spread in order to understand how volatile or variable a dataset is (range, quartiles, interquartile range, standard deviation);
Sigma Notation and summation
Counting Methods: Learn efficient methods for counting the number of arrangements of a set number of objects using permutation and combination functions;
Probability: Learn how to determine the likelihood of an event occurring (Venn diagrams, unions, intersections, conditional probabilities, mutually exclusive outcomes, and independent trials);
Solving probability problems involving permutations, combinations, and tree diagrams;
The Binomial Distribution: theory and application;
Discrete Probability Distributions: theory and applications, as well as understanding of expectation, variance, and standard deviation;
The Normal Distribution: theory and application;
Using the Normal Distribution to Approximate the Binomial Distribution
Hypothesis Testing
Linear Regression Analysis
Requirements
Basic algebra and mathematical techniques
Description
Are you battling to understand statistics? Are you confused by intimidating formulae and jargon? Do you want to know what metrics analyze in your business or in your job, that will allow you to produce insightful reports in order to make better decisions?Well if so, this course is perfect for you. I designed this statistics course combining years of teaching, investment banking, and entrepreneurial experience. The result is an easy-to-understand and real-world applicable course with detailed explanations and worked examples, which will not only help you to understand statistical concepts, but will also help you to see how statistics is applied in real-world business scenarios. This "learn-by-doing" approach, will empower you to master the concepts being taught quickly, via direct application. The video format of the course accelerates learning, and provides an engaging delivery mechanism for the educational content. In addition to this, the practice questions at the end of each learning section, provide students with a large body of practice material to reinforce the learning of the concepts being taught. In this preview course a conceptual overview of statistics is provided, covering representation of data, measures of spread, and measures of location. In the full course a comprehensive list of concepts would be expanded upon to include producing powerful reports, hypothesis testing and regression.
Overview
Section 1: Data Representation
Lecture 1 Introduction
Lecture 2 Chapter 1 Part 2 - Stem-and-Leaf Diagram
Lecture 3 Chapter 1 Part 3 - Bar charts and histograms
Lecture 4 Chapter 1 Part 4 - Cumulative Frequency Curves
Section 2: Measures of Location
Lecture 5 Chapter 2 Part 1 - Measures of Location
Lecture 6 Chapter 2 Part 2 - The Mean and Sigma Notation
Lecture 7 Chapter 2 Part 3 - The Mode
Section 3: Measures of Spread
Lecture 8 Chapter 3 Part 1 - The Range and 5 No. Summary
Lecture 9 Chapter 3 Part 2 - Sigma Notation Revisited
Lecture 10 Chapter 3 Part 3 - Variance and Std. Deviation
Lecture 11 Chapter 3 Part 4 - Variance and Std. Deviation For Frequency Tables
Lecture 12 Part 1 Summary
Section 4: Probability
Lecture 13 Chapter 4 - Part 1 Probability General Terms and Concepts
Lecture 14 Chapter 4 - Part 2 Mutually Exclusive Outcomes
Lecture 15 Unions and Intersections
Lecture 16 Conditional Probability
Lecture 17 Tree Diagrams
Lecture 18 Independence
Section 5: Permutations and Combinations
Lecture 19 Permutations
Lecture 20 Permutations with Repeated Items
Lecture 21 Combinations
Lecture 22 Combinations with Repeated Items
Lecture 23 Complex Problems
Section 6: Discrete Probability Distributions
Lecture 24 Discrete Probability Distributions
Section 7: The Binomial Distribution
Lecture 25 The Binomial Distribution
Lecture 26 The Parameters of the Binomial Distribution
Lecture 27 The Geometric Distribution
Lecture 28 The Mode of the Geometric Distribution
Section 8: Expectation and Variance of a Random Variable
Lecture 29 Expectation and Variance of a Random Variable
Lecture 30 Expectation and Variance of a Binomial Random Variable
Lecture 31 Expectation of a Geometrically Distributed Random Variable
Section 9: The Normal Distribution
Lecture 32 The Normal Distribution Introduction
Lecture 33 The Normal Distribution Worked Examples
Lecture 34 Standardizing The Normal Distribution
Lecture 35 The Normal Approximation to the Binomial Distribution
Lecture 36 Normal Approximation to Binomial Worked Examples
Business analysts, data analysts and any professional in a research or marketing role, who uses statistics on a daily basis

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https://www.udemy.com/course/business_stats/

   
   
   

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