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Learn business statistics from basic to expert level through a practical course with Excel.
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2 students enrolled
Created by Diego Fernandez
Last updated 6/2020
English
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Current price: \$14.99 Original price: \$24.99 Discount: 40% off
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This course includes
• 4.5 hours on-demand video
• 6 articles
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• 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.
Requirements
• Spreadsheet software such as Microsoft Excel® is required.
• Practical example spreadsheet provided with the course.
• Prior basic spreadsheet software knowledge is useful but not required.
Description

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.

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.

Who this course is for:
• Academic researchers who wish to deepen their knowledge in applied statistics or quantitative finance.
• Business data scientists who desire to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.
Course content
Expand all 34 lectures 04:30:34
+ Course Overview
6 lectures 41:26

In this lecture you will view course disclaimer and learn which are its objectives, how you will benefit from it, its previous requirements and my profile as instructor.

Preview 04:38

In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (course overview, descriptive statistics, probability distributions, parameters estimation and parameters hypothesis testing).

Preview 03:39

13:42

In this lecture you will learn business statistics data definition and Microsoft Excel® Add-in for histogram calculation (Analysis ToolPak).

19:21

Course File
00:03

Course Overview Slides
00:02
+ Descriptive Statistics
7 lectures 55:11

Descriptive Statistics Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to descriptive statistics (frequency distributions, central tendency measures, dispersion measures, frequency distribution shape and association measures).

Descriptive Statistics Overview
10:43

In this lecture you will learn frequency distribution definition and main calculations (STANDARDIZE(), AVERAGE(), STDEV.S(), SUM() functions, Histogram Data Analysis ToolPak Add-in).

Frequency Distributions
18:59

In this lecture you will learn central tendency measures definition and main calculations (AVERAGE(), MEDIAN() functions).

Preview 04:08

In this lecture you will learn dispersion measures definition and main calculations (STDEV.S(), VAR.S(), ABS(), ARRAY{} functions).

Dispersion Measures
07:16

In this lecture you will learn frequency distribution shape definition and main calculations (SKEW(), KURT() functions).

Frequency Distribution Shape
07:14

In this lecture you will learn association measures definition and main calculations (COVARIANCE.S(), CORREL() functions).

Association Measures
06:49
+ Probability Distributions
7 lectures 01:26:14

Probability Distributions Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to probability distributions (normal probability distribution, Student’s t probability distribution, quantile-quantile plots, Jarque-Bera normality test).

Probability Distributions Overview
12:24

In this lecture you will learn normal probability distribution definition and main calculations (NORM.S.DIST() function).

Normal Probability Distribution
12:35

In this lecture you will learn Student’s t probability distribution definition and main calculations (T.DIST() function).

Student’s t Probability Distribution
18:43

In this lecture you will learn normal probability distribution q-q plots definition and main calculations (NORM.S.INV() function).

Normal Probability Distribution Q-Q Plots
16:06

In this lecture you will learn Student’s t probability distribution q-q plots definition and main calculations (T.INV() function).

Student’s t Probability Distribution Q-Q Plots
18:55

In this lecture you will learn Jarque-Bera normality test definition and main calculations (COUNT(), SKEW(), KURT(), CHISQ.DIST.RT() functions).

Jarque-Bera Normality Test
07:29
+ Parameters Estimation
8 lectures 31:43

Parameters Estimation Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to parameters estimation (population mean point and interval estimates, population proportion point and interval estimates, population mean and population proportion samples sizes estimate).

Parameters Estimation Overview
05:02

In this lecture you will learn population mean point estimate definition and main calculations (AVERAGE() function).

Population Mean Point Estimation
02:28

In this lecture you will learn population proportion point estimate definition and main calculations (COUNTIF(), COUNT() functions).

Population Proportion Point Estimation
03:26

In this lecture you will learn population mean interval estimation definition and main calculations (STDEV.S(), COUNT(), SQRT(), NORM.S.INV(), T.INV.2T() functions).

Population Mean Interval Estimation
09:05

In this lecture you will learn population proportion interval estimation definition and main calculations (COUNT(), SQRT(), NORM.S.INV() functions).

Population Proportion Interval Estimation
05:56

In this lecture you will learn population mean sample size definition and main calculations (NORM.S.INV() function).

Population Mean Sample Size
02:52

In this lecture you will learn population proportion sample size definition and main calculations (NORM.S.INV() function).

Population Mean Sample Size
02:52
+ Parameters Hypothesis Testing
6 lectures 55:57

Parameters Hypothesis Testing Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to parameters hypothesis testing (population mean probability value, population proportion probability value, two populations means probability value and population mean statistical power).

Parameters Hypothesis Testing Overview
06:45

In this lecture you will learn population mean probability value definition and main calculations (AVERAGE(), STDEV.S(), COUNT(), SQRT(), T.DIST.2T(), T.INV.2T(), T.DIST.RT(), T.INV() functions).

Population Mean Probability Value
13:29

In this lecture you will learn population proportion probability value definition and main calculations (COUNTIF(), COUNT(), SQRT(), NORM.S.DIST(), NORM.S.INV() functions).

Population Proportion Probability Value
10:03

In this lecture you will learn two populations means probability value definition and main calculations (AVERAGE(), STDEV.S(), COUNT(), SQRT(), T.DIST.2T(), T.TEST(), T.INV.2T() functions).

Two Populations Means Probability Value
11:08

In this lecture you will learn population mean statistical power definition and main calculations (AVERAGE(), STDEV.S(), COUNT(), SQRT(), T.INV.2T(), T.DIST(), T.DIST.RT() functions).

Population Mean Statistical Power
14:30