You should take this course! • If you need a complete and comprehensive package that covers SAS programming, intuitive statistics interpretation, data analysis, and predictive modeling, and • If you would like to learn by doing various practical use cases fitting in the positions in different business portfolios, and • Whether you are a job seeker or beginner intending to start a data science career Then this around 18 hours course is right for you! This complete SAS course includes more than 150 lectures and contains 11 real world case studies/projects in different applied areas such as banking and marketing. After this intensive training, you will be equipped with a powerful tool for the most sexy data analytics career path!

Included in this course is an e-book and a set of slides. The course is divided into two parts. In the first part, students are introduced to the theory behind count models. The theory is explained in an intuitive way while keeping the math at a minimum. The course starts with an introduction to count tables, where students learn how to calculate the incidence-rate ratio. From there, the course moves on to Poisson regression where students learn how to include continuous, binary, and categorical variables. Students are then introduced to the concept of overdispersion and the use of negative binomial models to address this issue. Other count models such as truncated models and zero-inflated models are discussed. In the second part of the course, students learn how to apply what they have learned using Stata. In this part, students will walk through a large project in order to fit Poisson, negative binomial, and zero-inflated models. The tools used to compare these models are also introduced.

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!

The course was updated recently to include a Real Word Applications section using EXCEL to analyze descriptive statistics data. I am using hands on REAL WORLD data sets in EXCEL to illustrate how to analyze data in the various concepts that are covered. Even if you never used EXCEL before, you will be able to follow my steps to load the data and select the appropriate tabs to easily analyze your data. This section is very handy for professionals and college students who need to analyze data and make interpretations. This course presents sound college level material about descriptive statistics . It is intended for college students and professionals interested in learning and applying the concepts of descriptive statistics. The course is presented in a way that helps students to understand and be able to also apply the concepts the concepts themselves and to succeed. All the topics are treated extensively with a wealth of solved problems to help the students understand how to pratically solve similar problems. Descriptive statistics is one area of statistical applications that uses numerical and graphical techniques to summarize the data, to look for patterns and to present the information in a useful and convenient way. Detailed and fully solved exercises are provided in the videos with great comments to help students understand the material. In addition, a wealth of quizzes and a test is provided at the end of the course for students who want to test their mastery of the material. The course will require around  three hours to complete. A test is available to allow the student to demonstrate a mastery of the subject matter.

In this course students will learn how to apply the paired t - test, the t - test for independent samples and the f - test for equality of variances to their day to day decision making, data analytics and data science practices. This course will enable you to leverage the powerful techniques of Statistical Inference in you day to day work as a data scientist, data analyst, business manager and business analyst.

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.

Are you aiming for a career in Data Science or Data Analytics? Good news, you don't need a Maths degree - this course is equipping you with the practical knowledge needed to master the necessary statistics. It is very important if you want to become a Data Scientist or a Data Analyst to have a good knowledge in statistics & probability theory. Sure, there is more to Data Science than only statistics. But still it plays an essential role to know these fundamentals ins statistics. I know it is very hard to gain a strong foothold in these concepts just by yourself. Therefore I have created this course. Why should you take this course? This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data This course is taught by an actual mathematician that is in the same time also working as a data scientist. This course is balancing both: theory & practical real-life example. After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis. What is in this course? This course is giving you the chance to systematically master the core concepts in statistics & probability , descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions , decision trees and more. In real-life examples you will learn the stats knowledge needed in a data scientist's or data analyst's career very quickly.

Why should you learn statistics now? Jobs in Analytics – Data is everywhere, and with that is the need to have professionals who can interpret that data which requires a solid understanding of statistical concepts Demand is only going to go up – With more organizations realizing the potential of statistics for their business, data analysts knowing statistics will excel over the ones just knowing technical tools. For a good Data Analyst, it is THE backbone! Build your Career in Data Science, Artificial Intelligence and Data Analysis - Statistics remains the fundamental skill set of those pursuing a career in Data Science, Deep Learning, Artificial Intelligence, Management Consulting, Financial Analysis Improve your understanding of the world - Statistics is used in everyday life, to describe the stock market, house prices, economic performance, business and much much more.  Improve your data literacy and equip yourself with the ability to communicate in the language of the future. Why learn from us? Save your time and your money with us by using the exact training courses that have been used to equip over 1000 beginners with statistical skills that they can apply to their day-to-day jobs Leverage our real-world understanding and experience, with instruction from real-life analytics experts who have built their careers applying statistics to real business problems on a day-to-day basis Develop your skills in Data Analytics & Data Science with our expanding range of high quality programs, that equip you with the real-world skills needed to succeed in the workplace of the future What is Statistics all about? Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression. The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work. The third part of the course consists of an introduction to methods of inferential statistics - methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are interested in. We will discuss confidence intervals and significance tests. You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software. Statistics encompasses the collection, analysis, and interpretation of data and provides a framework for thinking about data. Statistics is used in many areas of scientific and social research, is critical to business and manufacturing, and provides the mathematical foundation for machine learning and data mining. In this course, students will gain a comprehensive introduction to the concepts and techniques of statistics as applied to a wide variety of disciplines. This course covers basic statistics, such as calculating averages, medians, modes, and standard deviations. With easy-to-understand examples combined with real-world applications from the worlds of business, sports, education, entertainment, and more. This course provides you with the skills and knowledge you need to start analyzing data. You'll explore how to use data and apply statistics to real-life problems and situations What will you learn? This course is your complete guide to the fundamentals of statistics and probability 1. Descriptive Statistics 2. Measures of Central Tendency 3. Correlation 4. Linear Regression 5. Probability Theory 6. Discrete and Continuous Probability Distributions 7. Sampling Distributions 8. Confidence Intervals 9. Significance Tests. The first chapter will focus on one variable analysis - we start with discussing the various descriptive statistics , how data can be represented using frequency tables and then move on to discussing measures of central tendency and their interpretation . The second module will focus on Correlation and Simple Linear Regression and show how these can be calculated in Excel using various formulae. We will then also discuss the interpretation of these results The next module will focus on introducing the fundamental concepts of Probability Theory like the sample space, events, randomness and basic set theory . We will then move onto discussing conditional probability and introduce the concept of mutually exclusive events .  You will also learn how to calculate probabilities within this section. In the fourth module, we will discuss Probability Distributions and show the difference between a continuous and discrete distributions, using the Normal and Binomial distributions as key examples. The next chapter will discuss Sampling Distributions and introduce the Central Limit Theorem and discuss why sampling is important . We will also link the Central Limit Theorem to the normal distribution and explain why this result is so critical to statistical analysis. The sixth module will focus on confidence intervals and the use of intervals to estimate the true average or proportion of a distribution. We will also discuss the relationship between the significance level (alpha) and the confidence level and teach you how to generate your own confidence intervals in Excel. The last module will introduce the concept of statistical Hypothesis Testing - what it can tell you and what it can't (i.e. you can't prove anything); how to define the null and alternate hypotheses correctly; getting the direction and 'tails' of your distribution correct and finally Type I and Type II errors with example and interpretations. This course is the go-to course for building a solid foundation in statistics and probability , not only teaching you the theory but giving you a comprehensive set of real examples you can work on which hasn't been seen in any other course on this platform. There are helpful quizzes included in all sections that will help you to cement the concepts learnt. Also, we are committed to add new practice activities so that you can practically apply the concepts learnt in this course.