An introduction to the analysis of deductive reasoning

Print this page Expressions. An expression is a record of a computation with numbers, symbols that represent numbers, arithmetic operations, exponentiation, and, at more advanced levels, the operation of evaluating a function. Conventions about the use of parentheses and the order of operations assure that each expression is unambiguous. Creating an expression that describes a computation involving a general quantity requires the ability to express the computation in general terms, abstracting from specific instances.

An introduction to the analysis of deductive reasoning

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. First, so that they can lead others to apply statistical thinking in day to day activities and secondly, to apply the concept for the purpose of continuous improvement.

In logic, we often refer to the two broad methods of reasoning as the deductive and inductive approaches.. Deductive reasoning works from the more general to the more specific. Sometimes this is informally called a "top-down" approach. Deductive reasoning is the kind of reasoning in which, roughly, the truth of the input propositions (the premises) logically guarantees the truth of the output proposition (the conclusion), provided that no mistake has been made in the reasoning. Analysis: Deductive & Inductive Arguments. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads.

This course will provide you with hands-on experience to promote the use of statistical thinking and techniques to apply them to make educated decisions whenever there is variation in business data. 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. However, the terminology differs from field to field. For example, the fitting of models to data, called calibration, history matching, and data assimilation, are all synonymous with parameter estimation.

Your organization database contains a wealth of information, yet the decision technology group members tap a fraction of it. 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. Therefore, too many decisions are based on guesswork, not facts. 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.

Information can be classified as explicit and tacit forms. 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.

Deirdre McCloskey: The Trouble with Mathematics and Statistics in Economics

The sequence from data to knowledge is: 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. However the decisive instrumental i.

Expressions.

Fact becomes knowledge, when it is used in the successful completion of a decision process. 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. The above figure depicts the fact that as the exactness of a statistical model increases, the level of improvements in decision-making increases.

Equations and inequalities.

That's why we need statistical data analysis. Statistical data analysis arose from the need to place knowledge on a systematic evidence base. This required a study of the laws of probability, the development of measures of data properties and relationships, and so on.

Statistical inference aims at determining whether any statistical significance can be attached that results after due allowance is made for any random variation as a source of error. Intelligent and critical inferences cannot be made by those who do not understand the purpose, the conditions, and applicability of the various techniques for judging significance.

Considering the uncertain environment, the chance that "good decisions" are made increases with the availability of "good information. The above figure also illustrates the fact that as the exactness of a statistical model increases, the level of improvements in decision-making increases.

Knowledge is more than knowing something technical. Wisdom is the power to put our time and our knowledge to the proper use. Wisdom comes with age and experience.

Wisdom is the accurate application of accurate knowledge and its key component is to knowing the limits of your knowledge.High School: Algebra┬╗ Introduction Print this page Expressions. An expression is a record of a computation with numbers, symbols that represent numbers, arithmetic operations, exponentiation, and, at more advanced levels, the operation of evaluating a function.

Deductive reasoning, also deductive logic, logical deduction is the process of reasoning from one or more statements (premises) to reach a logically certain conclusion.

Deductive reasoning goes in the same direction as that of the conditionals, and links premises with conclusions. Methods of Economic Analysis: An economic theory derives laws or generalizations through two methods: (1) Deductive Method and (2) Inductive Method.

An introduction to the analysis of deductive reasoning

These two ways of deriving economic generalizations are now explained in brief. Inductive reasoning (as opposed to deductive reasoning or abductive reasoning) is a method of reasoning in which the premises are viewed as supplying some evidence for the truth of the conclusion. While the conclusion of a deductive argument is certain, the truth of the conclusion of an inductive argument may be probable, based upon the evidence given.

Unlike deductive reasoning, inductive reasoning begins with specific observations or real examples of events, trends, or social processes. Using this data, researchers then progress analytically to broader generalizations and theories that help explain the observed cases.

In logic, we often refer to the two broad methods of reasoning as the deductive and inductive approaches.. Deductive reasoning works from the more general to the more specific.

Sometimes this is informally called a "top-down" approach.

Inductive reasoning - Wikipedia