Business Online Course by Udemy, On Sale Here
Learn business statistics from basic to expert level through a practical course with Excel.
An excellent training about Business Analytics & Intelligence
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 ExcelChart 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 Students 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 PracticeLearning 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 OverviewThis practical course contains 34 lectures and 4.5 hours of content. Its designed for all business statistics knowledge levels and a basic understanding of Microsoft Excel is useful but not required. At first, youll learn how to perform business statistics operations using built-in functions and array calculations. Next, youll learn how to do histogram calculation using Microsoft Excel Add-in. Then, youll define descriptive statistics. Next, youll define quantitative data, data population and data sample. After that, youll define absolute frequency distribution and relative frequency distribution or empirical probability. For frequency distributions, youll do frequency, density, cumulative frequency and cumulative density histograms. Later, youll define central tendency measures. For central tendency measures, youll estimate sample mean and sample median. Then, youll define dispersion measures. For dispersion measures, youll estimate sample standard deviation, sample variance and sample mean absolute deviation or sample average deviation. Next, youll define frequency distribution shape measures. For frequency distribution shape measures, youll estimate sample skewness and sample kurtosis. Then, youll define association measures. For association measures, youll estimate samples correlation and samples covariance. Next, youll define probability distributions. Then, youll define theoretical and empirical probability distributions. After that, youll define continuous random variable and continuous probability distribution. Later, youll define normal probability distribution, standard normal probability distribution and Students t probability distribution for several degrees of freedom alternatives. Then, youll define probability distribution goodness of fit testing. For probability distribution goodness of fit testing, youll do quantile-quantile plots and Jarque-Bera normality test evaluations. After that, youll define parameters estimation statistical inference. Then, youll define point estimation. For point estimation, youll do population mean and population proportion point estimations. After that, youll define confidence interval estimation. For confidence interval estimation, youll do population mean and population proportion confidence intervals estimation assuming known and unknown population variance. Later, youll define sample size estimation. For sample size estimation, youll do population mean and population proportion sample sizes estimation assuming known population variance for specific margin of error. Later,
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