MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_NextPart_01CA2B8E.3092FC10" This document is a Single File Web Page, also known as a Web Archive file. If you are seeing this message, your browser or editor doesn't support Web Archive files. Please download a browser that supports Web Archive, such as Microsoft Internet Explorer. ------=_NextPart_01CA2B8E.3092FC10 Content-Location: file:///C:/1EF21713/RumbosMath152Fall09Syllabus.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Syllabus for Math 152

Department of Mathematics

Pomona= College

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Math 152.  Statistical Theory        &= nbsp;         &= nbsp; Fall 2009

 

Course Outline <= /p>

 

Time and Place:     =     MWF  9:00 am - 9:50 am    Millikan  218

Instructor:<= span style=3D'mso-spacerun:yes'>        =           Dr. Adolfo J. Rumbos

Office: =         =                 Andre= w 259

Phone/e-mail:<= span style=3D'mso-tab-count:2'>        &= nbsp;    ext.  18713 / arumbos@pomona.edu

Office Hours:<= span style=3D'mso-spacerun:yes'>             MWF   10:00 am – 11:00 am; or by appointment

Text:<= span style=3D'mso-spacerun:yes'>        =                    Introduction to Mathematical Statistics, = Sixth Edition

by Robert V. Hogg, Joseph W. McKean an= d Allen T. Craig.  <= /p>

Course Website: =        http://pages.pomona.edu/~ajr047= 47/

Prerequisites:             Probability (Math 151 PO or equivalent c= ourse)

 

Course Description.    This is a course i= n statistical inference.  Loosely speaking, statistical infe= rence is the process of going from information gained from a sample to inferences = about a population from which the sample is taken.  There are two aspects of statistic= al inference that we'll be studying in this course: estimation and hypothesis testing. In estimation, we try to determine parameters from a population based on quantities, referred to as statistics, calculated from data in a sample.  The degree to which the estimates resemble the parameters being estimated can be measured by ascertaining the probability that a certain range of values around the estimate will contain= the actual parameter.  The use of probability is at the core of statistical inference; it involves the postulation of a certain probability model underlying the situation being studied and calculations based on that model.  The same procedure can in turn be = used to determine the degree to which the data in the sample support the underly= ing model; this is the essence of hypothesis testing. A solid knowledge of probability is therefore essential for understanding statistical inference. 

The course topics are listed in the attached tentative schedule of lectures and examinations.=

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Assigned Readings= and Problems.   Readings and problem sets will be assigned at e= very lecture and collected on an alternate basis.  Students are strongly encouraged t= o work on every assigned problem.  Late homework assignments will not be graded.

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Grading Policy.   Grades will be based on the homework, three 50-minute examinations, plus a comprehensive final examination.  The overall scor= e will be computed as follows:

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        &= nbsp;           &nbs= p;   homework        &= nbsp;           &nbs= p;            &= nbsp;                 &= nbsp;   20%

        &= nbsp;           &nbs= p;   three 50-minute exams    &nbs= p;            &= nbsp;        50%

        &= nbsp;           &nbs= p;   final examination     &n= bsp;            = ;            &n= bsp;            = ;    30%

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Final Examination.

Time:    Thur= sday, December 17, 2009     &nbs= p; 9:00 am - 11:00 am.

Place:   Millikan = 218

Math 152= .  Statistical Theory        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            Fall 2009

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