This course provides a basic introduction to statistics and the use of R a popular programming language. During the course we look at many fundamental ideas in statistics within the framework of analysis in R Studio. For students and those who want exposure to statistical analysis this is a course for you.

In this course students will learn how to apply the paired t - test, the t - test for independent samples and the f - test for equality of variances to their day to day decision making, data analytics and data science practices. This course will enable you to leverage the powerful techniques of Statistical Inference in you day to day work as a data scientist, data analyst, business manager and business analyst.

In this course, students will gain a comprehensive introduction to the concepts of Population and sample in statistics. This course covers identifying samples and also various sampling techniques. With easy-to-understand examples from real-world and case studies and assignment to test your knowledge, you will polish your understanding further. Mind maps are also the part of this course to help you retain and revise your learning.

What is the course about? This course promises that students will Learn the statistics in a simple and interesting way Know the business scenarios, where it is applied See the demonstration of important concepts (simulations) in MS Excel Practice it in MS Excel to cement the learning Get confidence to answer questions on statistics Be ready to do more advance course like logistic regression etc. Course Material The course comprises of primarily video lectures. All Excel file used in the course are available for download. The complete content of the course is available to download in PDF format. How long the course should take? It should take approximately 25 hours for good grasp on the subject. Why take the course To understand statistics with ease Get crystal clear understanding of applicability Understand the subject with the context See the simulation before learning the theory

This course contains: · Introduction to design of experiments with illustrated and animated videos · How to create statistically designed experiment and analyse it · How to calculate main effects and interaction effects · Structure of Full Factorial Designs · Fractional Factorial Designs: confounding, resolution and selection of designs · Creating and Analysing Design using Minitab as well as SigmaXL software · Quizzes for the videos to test comprehension

Learn business statistics through a practical course with R statistical software using S&P 500® Index ETF prices historical data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business statistics research. All of this while exploring the wisdom of best academics and practitioners in the field. Become a Business Statistics Expert in this Practical Course with R Read S&P 500® Index ETF prices data and perform business statistics operations by installing related packages and running script code on RStudio IDE. Chart absolute frequency, relative frequency, cumulative absolute frequency and cumulative relative frequency histograms. Approximate sample mean, sample median central tendency measures and sample standard deviation, sample variance, sample mean absolute deviation dispersion measures. Estimate sample skewness, sample kurtosis frequency distribution shape measures and samples correlation, samples covariance association measures. Define normal probability distribution, standard normal probability distribution and Student’s t probability distribution for several degrees of freedom alternatives. Evaluate probability distribution goodness of fit through Kolmogorov-Smirnov, Cramer-von Mises and Anderson Darling tests. Approximate population mean, population proportion and bootstrap population mean point estimations. Estimate population mean, population proportion and bootstrap population mean confidence intervals assuming known or unknown population variance. Calculate population mean sample size assuming known or unknown population variance for specific margin of error. Approximate population mean two tails, right tail and population proportion left tail statistical inference tests probability values. Estimate paired populations means assuming equal population variances two tail statistical inference test probability value. Assess population mean two tails statistical inference test power for several levels of statistical significance or confidence alternatives. Become a Business Statistics Expert and Put Your Knowledge in Practice Learning business statistics is indispensable for data science applications in areas such as consumer analytics, finance, banking, health care, e-commerce or social media. It is also essential for academic careers in applied statistics or quantitative finance. And it is necessary for business statistics research. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for business statistics analysis to achieve greater effectiveness. Content and Overview This practical course contains 36 lectures and 4 hours of content. It’s designed for all business statistics knowledge levels and a basic understanding of R statistical software is useful but not required. At first, you’ll learn how to read S&P 500® Index ETF prices historical data to perform business statistics operations by installing related packages and running script code on RStudio IDE. Then, you’ll define descriptive statistics. Next, you’ll define quantitative data, data population and data sample. After that, you’ll define absolute frequency distribution and relative frequency distribution or empirical probability. For frequency distributions, you’ll do frequency, density, cumulative frequency and cumulative density histograms. Later, you’ll define central tendency measures. For central tendency measures, you’ll estimate sample mean and sample median. Then, you’ll define dispersion measures. For dispersion measures, you’ll estimate sample standard deviation, sample variance and sample mean absolute deviation or sample average deviation. Next, you’ll define frequency distribution shape measures. For frequency distribution shape measures, you’ll estimate sample skewness and sample kurtosis. Then, you’ll define association measures. For association measures, you’ll estimate samples correlation and samples covariance. Next, you’ll define probability distributions. Then, you’ll define theoretical and empirical probability distributions. After that, you’ll define continuous random variable and continuous probability distribution. Later, you’ll define normal probability distribution, standard normal probability distribution and Student’s t probability distribution for several degrees of freedom alternatives. Then, you’ll define probability distribution goodness of fit testing. For probability distribution goodness of fit testing, you’ll do Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling evaluations. After that, you’ll define parameters estimation statistical inference. Next, you’ll define theoretical and bootstrap mean probability distributions. Then, you’ll define point estimation. For point estimation, you’ll do population mean, population proportion and bootstrap population mean point estimations. After that, you’ll define confidence interval estimation. For confidence interval estimation, you’ll do population mean, population proportion and bootstrap population mean confidence intervals estimation assuming known and unknown population variance. Later, you’ll define sample size estimation. For sample size estimation, you’ll do population mean sample size estimation assuming known and unknown population variance for specific margin of error. Later, you’ll define parameters hypothesis testing statistical inference. Next, you’ll define probability value. For probability value, you’ll do population mean two tails and right tail tests. Also, for probability value, you’ll do population proportion left tail test. Additionally, for probability value, you’ll do paired populations means two tails test assuming equal populations variances. Finally, you’ll define statistical power, type I error, type II error, type I error probability and type II error probability. For statistical power, you’ll do population mean two tails tests for several statistical significance or confidence levels.

If you are aiming for a career as a Data Scientist or Business Analyst then brushing up on your statistics skills is something you need to do. But it's just hard to get started... Learning / re-learning ALL of stats just seems like a daunting task. That's exactly why I have created this course! Here you will quickly get the absolutely essential stats knowledge for a Data Scientist or Analyst. This is not just another boring course on stats. This course is very practical. I have specifically included real-world examples of business challenges to show you how you could apply this knowledge to boost YOUR career. At the same time you will master topics such as distributions, the z-test, the Central Limit Theorem, hypothesis testing, confidence intervals, statistical significance and many more! So what are you waiting for? Enroll now and empower your career!

This is not another boring stats course. We'll teach you the fundamental statistical tools to be successful in analytics...without boring you with complex formulas and theory. Statistical analysis can benefit almost anyone in any industry. We live in a world flooded with data. Having the tools to analyze and synthesize that data will help you stand out on your team. In a few short hours, you'll have the fundamental skills to help you immediately start applying sophisticated statistical analyses to your data. Our course is: Very easy to understand - There is not memorizing complex formulas (we have Excel to do that for us) or learning abstract theories. Just real, applicable knowledge. Fun - We keep the course light-hearted with fun examples To the point - We removed all the fluff so you're just left with the most essential knowledge What you'll be able to do by the end of the course Create visualizations such as histograms and scatter plots to visually show your data Apply basic descriptive statistics to your past data to gain greater insights Combine descriptive and inferential statistics to analyze and forecast your data Utilize a regression analysis to spot trends in your data and build a robust forecasting model Let's start learning!

Learn business statistics through a practical course with Microsoft Excel® using S&P 500® Index ETF prices historical data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business statistics research. All of this while exploring the wisdom of best academics and practitioners in the field. Become a Business Statistics Expert in this Practical Course with Excel Chart absolute frequency, relative frequency, cumulative absolute frequency and cumulative relative frequency histograms. Approximate sample mean, sample median central tendency measures and sample standard deviation, sample variance, sample mean absolute deviation dispersion measures. Estimate sample skewness, sample kurtosis frequency distribution shape measures and samples correlation, samples covariance association measures. Define normal probability distribution, standard normal probability distribution and Student’s t probability distribution for several degrees of freedom alternatives. Evaluate probability distribution goodness of fit through quantile-quantile plots and Jarque-Bera normality test. Approximate population mean and population proportion point estimations. Estimate population mean and population proportion confidence intervals assuming known or unknown population variance. Calculate population mean and population proportion sample sizes assuming known population variance for specific margin of error. Approximate population mean two tails, right tail and population proportion left tail statistical inference tests probability values. Estimate paired populations means two tails statistical inference test probability value. Assess population mean two tails statistical inference test power for several levels of statistical significance or confidence alternatives. Become a Business Statistics Expert and Put Your Knowledge in Practice Learning business statistics is indispensable for data science applications in areas such as consumer analytics, finance, banking, health care, e-commerce or social media. It is also essential for academic careers in applied statistics or quantitative finance. And it is necessary for business statistics research. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for business statistics analysis to achieve greater effectiveness. Content and Overview This practical course contains 34 lectures and 4.5 hours of content. It’s designed for all business statistics knowledge levels and a basic understanding of Microsoft Excel® is useful but not required. At first, you’ll learn how to perform business statistics operations using built-in functions and array calculations. Next, you’ll learn how to do histogram calculation using Microsoft Excel® Add-in. Then, you’ll define descriptive statistics. Next, you’ll define quantitative data, data population and data sample. After that, you’ll define absolute frequency distribution and relative frequency distribution or empirical probability. For frequency distributions, you’ll do frequency, density, cumulative frequency and cumulative density histograms. Later, you’ll define central tendency measures. For central tendency measures, you’ll estimate sample mean and sample median. Then, you’ll define dispersion measures. For dispersion measures, you’ll estimate sample standard deviation, sample variance and sample mean absolute deviation or sample average deviation. Next, you’ll define frequency distribution shape measures. For frequency distribution shape measures, you’ll estimate sample skewness and sample kurtosis. Then, you’ll define association measures. For association measures, you’ll estimate samples correlation and samples covariance. Next, you’ll define probability distributions. Then, you’ll define theoretical and empirical probability distributions. After that, you’ll define continuous random variable and continuous probability distribution. Later, you’ll define normal probability distribution, standard normal probability distribution and Student’s t probability distribution for several degrees of freedom alternatives. Then, you’ll define probability distribution goodness of fit testing. For probability distribution goodness of fit testing, you’ll do quantile-quantile plots and Jarque-Bera normality test evaluations. After that, you’ll define parameters estimation statistical inference. Then, you’ll define point estimation. For point estimation, you’ll do population mean and population proportion point estimations. After that, you’ll define confidence interval estimation. For confidence interval estimation, you’ll do population mean and population proportion confidence intervals estimation assuming known and unknown population variance. Later, you’ll define sample size estimation. For sample size estimation, you’ll do population mean and population proportion sample sizes estimation assuming known population variance for specific margin of error. Later, you’ll define parameters hypothesis testing statistical inference. Next, you’ll define probability value. For probability value, you’ll do population mean two tails and right tail tests. Also, for probability value, you’ll do population proportion left tail test. Additionally, for probability value, you’ll do paired populations means two tails test. Finally, you’ll define statistical power, type I error, type II error, type I error probability and type II error probability. For statistical power, you’ll do population mean two tails tests for several statistical significance or confidence levels.

Are you aiming for a career in Data Science or Data Analytics? Good news, you don't need a Maths degree - this course is equipping you with the practical knowledge needed to master the necessary statistics. It is very important if you want to become a Data Scientist or a Data Analyst to have a good knowledge in statistics & probability theory. Sure, there is more to Data Science than only statistics. But still it plays an essential role to know these fundamentals ins statistics. I know it is very hard to gain a strong foothold in these concepts just by yourself. Therefore I have created this course. Why should you take this course? This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data This course is taught by an actual mathematician that is in the same time also working as a data scientist. This course is balancing both: theory & practical real-life example. After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis. What is in this course? This course is giving you the chance to systematically master the core concepts in statistics & probability , descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions , decision trees and more. In real-life examples you will learn the stats knowledge needed in a data scientist's or data analyst's career very quickly.