The purpose of this page is to provide resources in the rapidly growing area of computer-based statistical data analysis. Topics include questionnaire design and survey sampling, forecasting techniques, computational mcgraw hill understanding psychology pdf and demonstrations.
This site offers information on statistical data analysis. It describes time series analysis, popular distributions, and other topics. It examines the use of computers in statistical data analysis. It also lists related books and links to related Web sites. Enter a word or phrase in the dialogue box, e. Why Is Every Thing Priced One Penny Off the Dollar?
What is Statistical Data Analysis? What Is a Geometric Mean? What Is Central Limit Theorem? What Is a Sampling Distribution?
You Must Look at Your Scattergrams! What is the Effect Size? What is the Benford’s Law? What About the Zipf’s Law? When to Use Nonparametric Technique? How to determine if Two Regression Lines Are Parallel?
What Is a Systematic Review? What Is the Black-Sholes Model? What Is a Classification Tree? What Is a Regression Tree? What is Intelligent Numerical Computation? Developments in the field of statistical data analysis often parallel or follow advancements in other fields to which statistical methods are fruitfully applied. Because practitioners of the statistical analysis often address particular applied decision problems, methods developments is consequently motivated by the search to a better decision making under uncertainties.
Decision making process under uncertainty is largely based on application of statistical data analysis for probabilistic risk assessment of your decision. Managers need to understand variation for two key reasons. Therefore, it is a course in statistical thinking via a data-oriented approach. Statistical models are currently used in various fields of business and science.
Employees waste time scouring multiple sources for a database. The decision-makers are frustrated because they cannot get business-critical data exactly when they need it. Many opportunities are also missed, if they are even noticed at all. Knowledge is what we know well. Information is the communication of knowledge. In every knowledge exchange, there is a sender and a receiver.
The sender make common what is private, does the informing, the communicating. The explicit information can be explained in structured form, while tacit information is inconsistent and fuzzy to explain. Know that data are only crude information and not knowledge by themselves. Data is known to be crude information and not knowledge by itself.
Data becomes information, when it becomes relevant to your decision problem. Information becomes fact, when the data can support it. Facts are what the data reveals. Once you have a massive amount of facts integrated as knowledge, then your mind will be superhuman in the same sense that mankind with writing is superhuman compared to mankind before writing. The following figure illustrates the statistical thinking process based on data in constructing statistical models for decision making under uncertainties.