Brief Introduction
Learn business statistics from basic to expert level through a practical course with R statistical software.Description
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.
Requirements
- Requirements
- R statistical software is required. Downloading instructions included.
- RStudio Integrated Development Environment (IDE) is recommended. Downloading instructions included.
- Practical example data and R script code files provided with the course.
- Prior basic R statistical software knowledge is useful but not required.
Knowledge
- 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.
Diego Fernandez
3.7 Raiting
Spring 5.0 Microservices
- 3.5
-
![]()
![]()
![]()
![]()
![]()
A practical and user-friendly approach to building microservices in Spring This course is designed to help you implement microservice architecture in Spring Framework, Spring Boot, and Spring Cloud....
Market Basket Analysis & Linear Discriminant Analysis with R
- 4.1
-
![]()
![]()
![]()
![]()
![]()
Master: Association rules (MBA) & it's usage, Linear Discriminant Analysis (LDA) for classification & variable selection This course has two parts....
Investing In Stocks The Complete Course! (13 Hour)
- 4.4
-
![]()
![]()
![]()
![]()
![]()
Master Stock Market Investing & Trading in the Stock Market. Top Instructor & Millionaire Investor. Investing & Trading Here is an example of some:...
Business Strategy
- 4.8
-
![]()
![]()
![]()
![]()
![]()
In this course you will learn how organizations create, capture, and maintain value. g. , five forces analysis) and the firm-level (e....
