Download MAT 211 Probability and Statistics

Independent University, Bangladesh
Department of Physical Sciences
Semester
Course code
Course Title
Section
Spring
2016
MAT 212
Probability and Statistics
for Science and
Engineering
03
Dr. Shipra Banik
Associate Professor
Department of Physical Sciences
E-mail: [email protected]
Office: Rm. 6004-A, SECS
Office hours: ST: 11:30 a.m.-13:00 p.m
MW: 11:30 a.m-13:00 p.m or by appointment
Course objectives
Recently, statistics is becoming increasingly important in understanding physical
phenomenon. This is mainly because of two reasons: a) the problems are becoming extremely
complicated, so that application of physical laws is becoming increasingly difficult b) in
many cases, even the physical laws are not clearly defined or understood. In all these cases,
researchers rely upon statistical methods to obtain some insight into the problem. The course
‘MAT 212 Probability and Statistics' have been designed as a first course of Statistics for the
students who want to graduate from the department of Electrical and Electronic Engineering.
As such, no prior knowledge of statistics is needed. But knowledge of Differential and
Integral Calculus are required. It shall be assumed that all students have the necessary
background in these branches of mathematics. Students are advised to procure a scientific
calculator for use in the class. The calculator must have the function to calculate factorial of
an integer, exponential calculation, power calculation and preferably compute permutation
and combination calculations. Students may use programmable calculators in examinations.
Text Book: All students should collect:
Montgomery, D.C. and Runger G.C. (2011), Applied Statistics and Probability for Engineers
(5th edition), John Wiley & Sons, Inc.
Grading procedure
At the end of the semester a letter grade would be awarded to each student based upon their
performance throughout the semester. The final grade would be based upon five tests taken
throughout the semester. The percentage of each test in calculation of the final grade would
be as follows:
Class Attendance
5%
Test–1
10%
Test–2
25% Mid term exam
Test–3
10%
Test- 4
10%
Test–5
40% Final exam
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Home works would be regularly assigned to the students. These are for practice purpose of
students only. Students are not required to hand these back for correction. But if anyone
wishes to have these looked into, I would be glad to do so.
The final grade would be calculated according to following schedule:
Above 85%
80% to less than 85%
75 % to less than 80%
70% to less than 75%
65% to less than 70%
60% to less than 65%
A
A–
B+
B
B–
C+
55% to less than 50%
50% to less than 55%
45% to less than 50%
40% to less than 45%
less than 40%
C
C–
D+
D
F
General guidelines
a) Student must come to class on time. No one will be allowed to enter the class after five minutes
from the start of the class.
b) No assignments will be given to the students. Problems will be assigned to the students for
practice. Students are not required to hand those back for grading.
c) There would be severe penalty if any student is found using illegal means during exams.
d) Tests would be conducted only once. No make-up tests would be given.
e) If a student is unable to take a test on medical grounds, such reasons must be related to the
instructor before the test. An alternate arrangement could then be arranged.
f) Students must remember that class is a place of learning, not relaxing or eating.
g) No cellular communication devices are allowed in the class. Possession of communication devices
during tests will result in confiscation of the device as well as cancellation of the test.
Description of Course Material
Lecture #
Lecture 1
Topic
Review of set theory, review of
mathematics(factorial, permutation and
combination)
Textbook/Reference
Lecture sheet
Lecture 2
Probability: Event, sample space, definition of Lecture sheet
probability, axioms of probability, simple and Text, pp.17-27
compound events, complimentary event,
mutually exclusive events, equally likely events
Lecture 3
Solving real life problems
Lecture 4
Lecture 5
Lecture sheets
Text, pp.28-31, pp.35-37
Lecture sheets
Text , pp.28-31, pp.35-37
Syllabus: Lecture 1- Lecture 4
Solving real life problems
Class test 1 (10%)
Lecture 6
Rules of probability, addition rule, complement Text, pp.37-46
rule, conditional rule, solving real life problems
Lecture 7
multiplication rule, independent events, solving Text, pp.47-55
real life problems
Lecture 8
Bayes rule, solving real life problems
Text, pp.55-57
Lecture 9
Random variable: Concept, types of random
Text, pp.57-73
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variables, discrete random variable, cumulative
distribution function
Lecture 10
Expectation, properties of expectation, variance,
standard deviation, moment generating function
Lecture 11
Mid-term test (25%)
Lecture sheets
Text, pp.73-77
Syllabus: Lecture 6–Lecture 10
Lecture 12
Special discrete random variables:
binomial distribution, Poisson distribution,
solving real life problems
Text, pp.77-86, pp.97-105
Lecture 13
Geometric distribution, uniform distribution,
negative binomial distribution, solving real life
problems
Review Lecture 13, hypergeometric distribution,
solving real life problems
Text, pp.86-92
Lecture 15
Lecture 16
Class test 2 (10%)
Continuous random variables: cumulative
distribution function, expectation, properties of
expectation, variance, standard deviation,
moment generating function
Syllabus:Lecture 12- Lecture 14
Text, pp.108-115
Lecture 17
Special continuous random variables: uniform
distribution, exponential distribution and normal
distribution, solving real life problems
Class test 3 (10%)
Jointly distributed random variables, marginal
distribution, independent random variables,
expectation, variance, standard deviation,
conditional distributions
Lecture sheets
Text, pp.116-127, pp.132-138
Lecture 14
Lecture 18
Lecture 19
Text, pp.92-97
Syllabus: Lecture 16 – Lecture 17
Text, pp.152-162
Lecture 20
Target population, random sample, variable, Text, pp.191-203
classification of variables, drawing random
samples from target population, estimation of
mean, median, mode, percentiles, variance,
standard deviation
Lecture 21
Frequency table and graph, relative frequency
table and graph, stem and leaf plot, grouped data
(mean and standard deviation calculation),
histogram, Chebyshev’s inequality.
Inference: confidence intervals of mean, variance
and standard deviation
Determination of sample size, paired data sets,
sample correlation coefficient
Linear regression, least squares approach,
coefficient of determination, prediction.
Lecture 22
Lecture 23
Lecture 24
Lecture sheets
Text, pp.203-208
Text, pp.223-225, pp.251-270
Lecture sheets
Text, pp.405-414, p.428
Final (40%): Lecture 19 – Lecture 24 (Date will be followed from the IUB green booklet)
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