presents quantitative subjects like statistics, mathematics of finance, and operations research in such a way that interaction guides the users through the subject by allowing them a hands on experience problem solving in,  and visual exposition of, the theory involved.  It is software that is framed in Excel workbooks -- making the doing of quantitative methods a bit easier, and the learning more elicited by the doing.  It provides true technology enhanced learning.

   The workbooks can be used in a lecture/lab setting, or by students autonomously in a remote e-learning environment.  Concepts are conveyed via visual expositions that interact with the student.  The student develops problem solving skills by using the same tools that create the expositions - everything is self contained within the workbooks.  These tools are simply built by me in Excel, and integrate with the standard Excel tools that the students will use for the rest of their lives when working in the subject being studied.  The courseware is completely modifiable by the instructor - so that it can be used interdisciplinarily as well as internationally.

   QIWCourseware is designed to expose quantitative subjects via visual interactions that illustrate theory.  It is not meaningful to have students rote learn formulas so that they can give back the formulas during an exam.  This is not education.  Instead, students must learn concepts, and tie them to their underlying mathematical formulas.   The best way to learn concepts is thru visualization.  QIWCourseware provides visual tools that interact with the students, and in doing so, the interaction provides the student with a picture of the concept at hand, and binds it with the formulas that are used to make the visuals.  This binding brings the formulas to mind during problem solving - the student makes a visualization of a problem that matches a visualization already studied, the formulas are then remembered and appear to the student as the roots of the solution.

   The courseware is written in relatively simple English, for an international audience.  Many pages include a glossary that gives the definitions of words used on the page that might not be familiar to a non-native English speaking student.  The courseware is easily modified by the teacher to include the teacher’s own slant on the subject and localization to the learners' countries.  New worksheets can easily be created to replace or augment existing ones; and the new sheets can use the embedded tools that I have created that provide the interaction.  Videos and slideshows can be embedded.

   Three modules are available: Statistics, Linear Programming, and Finance.  You can download the first chapter of each module from
this website.  By downloading, you are placed on a mailing list that is used by me to provide you with updates, hints at modifications, and success stories about implementations.  I am currently implementing the Statistics and Finance modules at an international  business school in Paris.  The LP module is several years old, and has been used many times by myself, and has been adopted by several instructors in Europe and Australia.

 There's a lot that's not right. If you let any of it disturb you, you miss the point of it.
 
The bright side relies on your recognition of it; not your submission to it.
 

 Pickwick’s Umbrella
 

 In London, half of the days have some rain. The weather forecaster is correct 2/3 of the
   time, i.e., the probability that it rains, given
  that she has predicted rain, and the
    probability that it does not rain, given that she has predicted that it won’t rain, are both
  equal to 2/3. When rain is forecast, Mr.
       Pickwick takes his umbrella. When rain is not forecast, he takes it with probability 1/3.

Can you find
 
(a) the probability that Pickwick has no umbrella, given that it rains.
 
     (b) the probability it doesn’t rain, given that he brings his umbrella. 

This problem appears in
K. L. Chung, Elementary Probability Theory With Stochastic Processes, 3rd ed. (New York:
Springer-Verlag, 1979), p. 152 
 
NOTE:   You may not assume that the probability that she predicts rain is 1/2 or that the probability that she predicted rain given that it rains is 2/3; however, you need that, so prove it first.  

HINT:  Show that for any two events R & F such that   a = P(R|F) = P(~R|~F)
 
 P(F)  =   1 - (a +P(R))  ,   
     1 - 2a
 
a ≠ ½ and under the further constraints that place the RHS between 0 and 1 inclusive.
 
When P(R) = ½  a drops out of the RHS and  P(F) = ½ .

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