Business Statistics with Excel

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.