Welcome to your course Statistics for Financial Analysis
In this course, you will be learning about various concepts in statistics which are now commonly used in the world of analytics and research.
You will be guided through concepts on the topics covering
Basics of Statistics
Data
Types of Statistics
This course is structured in self paced learning style. Please use your headset for effective listening. Also have your notepad and pen to take note of key points.
Go through the demo videos to get a feel of the course.
See you inside the course!

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.

Taught 3000+ students offline and now extending the course and experience to online students like you.
Winners don't do different things, they do things differently.
Training, quizzes, and practical steps you can follow - this is one of the most comprehensive Statisticscourses available. We'll cover Probability, Advance concept of Permutations & Combinations, Descriptive statistics, Inferential statistics, Hypothesis Testing, Correlation Analysis, Regression Analysis, Modelling, Ch- Squared Test, ANOVA, Business Forecasting, and many more. This course is a great "value for money".
By the end of this course, you will be confidently implementing techniques across the major situations in Statistics, Business, and Data Analysis.
You'll Also Get:
- Downloadable workout Notes for competitive exams and future reference purpose
- Lifetime Access to course updates
- Fast & Friendly Support in the Q&A section
- If you are a student or preparing for the competitive exam you may opt for education notes/ handouts
-Udemy Certificate of Completion Ready for Download
Don't Miss Out!
Every second you wait is costing you a valuable chance for learning and to outstand.
These courses come with a 30-day money-back guarantee - so there's no risk to get started.

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.

November, 2019.
Multivariate Analysis of Variance, a popular but frequently perplexing procedure in statistics, is used to test two or more groups on two or more dependent variables. Mindful of the frustration and confusion that is often experienced with this procedure, this course was carefully designed by a specialist in quantitative methods (statistics) who has successfully taught MANOVA to graduate students from a variety of different backgrounds. Several students who thought they couldn’t understand this procedure were later explaining how they not only understood it, but actually found it to be fun!
Specifically, this course takes the viewer step-by-step through running and interpreting a number of different multivariate analyses of variance (MANOVA) in SPSS. Several different examples of MANOVA are covered, including:
MANOVA with 2 Groups (Also Known as Hotelling’s T-Squared)
MANOVA with 3 Groups
Post-Hoc Tests for Problems with 3 or More Groups
Two-way MANOVA
Equal Covariance Matrix Assumption of MANOVA Explained Step-by-Step
All tests include a detailed, step-by-step explanation of results, including how to assess the results for significance, with written results provided for each test covered.
Enroll today and be confused by MANOVA no longer!

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

Our philosophy
Knowledge-based on rote learning is useless if it is not applied.
Our course
You enter a simulated company as an intern to follow your mentor and learn to apply descriptive statistics to collect, analyze, interpret, and develop a competitive business presentation.
We offer
A non-intimidating cartoon styled business atmosphere where learning is fun and concepts are easy to understand.
We provide tools to help you build data analysis skills so you can break through entry-level jobs, succeed in a new position, gain job security, or get a long-overdue promotion. Acquiring increasingly valuable data collection, interpretation, presentation, and reporting skills are critical in helping you achieve your career goals. Data is everywhere and on every level, such as the fast-food worker who always volunteered to help with weekly reports and is now the assistant manager. And the office workers, who carved out niches for data collection and reporting, are getting promoted while others are getting laid-off.
If you have spent any time in the workforce, you probably have noticed the worker that seems indispensable. When there are layoffs or furloughs, that person always manages to survive; and is often promoted when others are not. This person is always present at important meetings. And the boss will often stop in mid-sentence and say something like, “What percentage of sales in widgets did we have last quarter?” And this worker will pop up right away with the correct answer. This worker regularly provides reports for the boss and is often sent scrambling for answers when the boss needs quick information. We find people who are well established in the workplace because they have mastered the descriptive statistic skills required to collect, analyze, present, and report data that are proven assets for career advancement.
Our multicultural world is infused with statistics. We make many business decisions based on data, and in that respect, data controls much of what we do. There are many forms of data to include automated programs that are based on statistics to run operations. For example, in sales operations, people with the highest sales automatically receive the most sales leads from automated systems. Whereas people with lower average sales do not receive many sales leads from the system.
This course is unique.
Since data and statistics are vital to the business, we created a course that places you in a simulated business environment to learn to master data analysis and presentation. This course features fun and easy lessons to help you understand descriptive statistics as building blocks for data collection, analysis, and reporting in your workplace. We structured the course to answer 3 crucial questions that are often asked in statistics courses:
·
What is this?
·
Why do I need this?
·
How is this going to help me at work?
We presented descriptive statistics in a series of dramatic episodes in a fictional company to answer these questions. In each episode, the characters are either mentoring, training or learning to analyze company data. We also included an Excel segment featuring statistical analysis and data charting. You participate by becoming an intern to observe how the characters apply descriptive statistics and complete assignments that apply lesson content. A big take-away is that you will be able to apply descriptive statistics and discover opportunities to analyze data.
What this course covers:
· Understand the definition of descriptive statistics, and why the statistical application is a critical skill in the workplace.
· How to collect data and identify valid sources of professional data.
· Why you need to know the difference between a population and a sample when collecting data at work.
· Understand the different statistical operations that are used for population and sample data.
· Know why you need representative large random samples to produce bell-shaped curves or normal distributions.
· What happens when we do not use large random samples, and what is right or left-skewed distribution curves.
· Know descriptive statistics that showcase product advantages in competitive markets.
· Compute descriptive statistics for small samples on your calculator and explain how each measure is useful for identifying or describing data.
· Use Excel, especially for computing large data samples and creating charts for presentations.
· Explain how measures of central tendency and dispersion are used in data collection and analysis.
· How central tendency and dispersion affect your data and why it is important in managing data, planning, and creating strategies.
· Know what measures of central tendency let us determine how close data is to average.
· Understand statistics that measure dispersion or how data is spread out from the average.
Market Analysis
Target Market and Demographics
· Identify potential customers and demographics using population and sample data.
· Analyze statistics for large random samples of non-personalized data such as age, gender, income, education level from databases.
· Understand how descriptive statistics help identify the right customers for advertisement campaigns.
Competitive Analysis
· Research competitors to identify strengths and weaknesses.
· Discover valid sources of competitor data for analysis.
· Determine how close the competition’s prices are to the average price by looking at distribution curves.
· Identify statistics that highlight product advantages, strengths, and unique features in a competitive market.
· Know what descriptive statistics to use to gain the best advantage when marketing products.
Sales and Price Forecasting
· Based on descriptive statistic information, identify sales or price changes you expect during a specific operation period.
· How central tendency and dispersion help you in forecasting data such as sales and prices.
Excel
· Learn how to use EXCEL to compute statistics and make grafts or charts.
· Become proficient in preparing data for presentations using Excel.
Exercises
· The exercises located throughout the lessons help you practice using what you learned.
· The lessons are designed to be easy and intuitive.
· Answer sheets for the exercises and instructions are located at the end of the course.
This course is for
· People who are not employed and are seeking skills to interest potential employers
· Employees seeking advancement and promotions
· Entry-level employees needing opportunities to showcase skills
· Self-employed and needing data for advertising to attract customers
· People seeking job security in a turbulent job market
· Management seeking better ways to interpret and present data
· Companies needing a fresh outlook on data collection, analysis, and reporting
· Grant writers and fundraisers who need to report data
What you need to know to prepare for this course
· Basic high school level math
· How to use an ordinary or statistical calculator
· A readiness to learn descriptive statistics in an exciting new way
Welcome to your exciting adventure as a professional intern in our simulated company.

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.