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# Statistics for Data Science and Business Analysis [Video]

 Learn Understand the fundamentals of statistics Work with different types of data How to plot different types of data Calculate the measures of central tendency, asymmetry, and variability Calculate correlation and covariance Distinguish and work with different types of distribution Estimate confidence intervals Perform hypothesis testing Make data-driven decisions Understand the mechanics of regression analysis Carry out regression analysis Use and understand dummy variables Understand the concepts needed for data science even with Python and R! This course will teach you fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. Modern software packages and programming languages are now automating most of these activities, but this course gives you something more valuableâ€”critical thinking abilities. This course will help you understand the fundamentals of statistics, learn how to work with different types of data, calculate correlation and covariance, and more. Careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow Style and Approach A complete course (packed with extensive case studies, complete training, an HD video, and animations) that covers major statistical topics and helps you become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist. No prior experience is required for the course. Learn and understand the fundamentals of statistics for Data Science and Business Analysis. A practical tutorial with case studies for people interested in Data Science and Business Analysis. 4 hours 19 minutes 9781789803259 11 Oct 2018
 What is R- squared and how does it help us? The ordinary least squares setting and its practical applications Studying regression tables The multiple linear regression model Adjusted R-squared What does the F-statistic show us and why we need to understand it?
 A1. Linearity A2. No endogeneity A3. Normality and homoscedasticity A4. No autocorrelation A5. No multicollinearity