MT 202S-Statistics & Probability

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BACHELOR OF EDUCATION

COURSE NAME : STATISTICS & PROBABILITY

COURSE CODE : MT 202S

NUMBER OF HOURS : 45 Hours

NUMBER OF CREDITS : 3 CREDITS

PRE-REUISITES : MT !00S: FOUNDATION MATH

COURSE DESCRIPTION

This course covers essential concepts and contents in descriptive statistics and probability.

Topics included are: Measures of central tendency, measures of dispersion, sampling, normal

distribution, and graphical representation of data, experimental and theoretical probabilities.

Everyday applications and a methodological approach to statistics and probability will be

emphasized.

LEARNIN" OUTCOMES :

COURSE CONTENT #o$$o%s:

UNIT ! : D' Co$$()'*o+ +, Pr(s(+''*o+ 3 %((.s/

NO OF HOURS:

INSTRUCTIONAL OB1ECTIES

Student should be able to:

. Explain the need for sampling.

!. Explain the distinction between a sample and a population and

". #ppreciate the necessity for randomness in choosing samples.

$. %dentify and use different sampling methods.

&. Explain in simple terms why a given sampling method may be unsatisfactory

'. (ecognize that the sample mean can be regarded as a random variable

). Ma*e appropriate decisions on sample size

+. Explain random sampling and non random sampling and give examples of each.

-. orrectly choose samples from a given population, whether accessible or inaccessible

/. ollect and organize data on current issues for ma*ing predictions

. ollect, classify and organize statistical data for graphical representations

!. Select a suitable method of presenting statistical data and *now the

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advantages2disadvantages that particular representation may have

". 3enerate graphical representations of numerical data by drawing appropriate graphs

$. 4se computer software programmes to simulate selected statistical analyses.

&. %nterpret and draw simple inferences from tables, bar graphs, proportional bar graphs,

pictographs, pie charts, tally charts, histograms, fre5uency polygons, ogives, scatter grams,

stem and leaf diagrams and box6and6whis*ers plots

COURSE CONTENT

. 7ata ollection

!. 8rganization 9 resentation of 7ata

". Sampling: techni5ues, size, and characteristics

$. Sampling Methods: robability vs. non6probability

UNIT 2: M(sur(s o# C(+'r$ T(+,(+) 3 %((.s/NO OF HOURS:


Student should be able to:

. alculate the statistical averages for grouped and ungrouped data

!. Ma*e decisions using the appropriate statistical average in a given situation

". #nalyze real life situations using statistical averages

$. Ma*e inferences and arguments based on data analysis

&. Ma*e application decisions using statistical averages

'. alculate the weighted, geometric and harmonic mean of a set of numbers, emphasizing

their applications in everyday life). alculate moving averages using real situations; e.g. rainfall

+. Examine trends using moving averages

COURSE CONTENT

. Statistical averages; mean, mode, median

!.


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". 4se the standard deviation in ma*ing statistical analyses

$. %dentify and explain what continuous random variable

&. 4se the normal distribution to calcuate standard scores

'. 7etermine the standard score for an item among a set of scores

). #pply statistical techni5ues to research situations.

COURSE CONTENT

. (ange,>uartiles, 7eciles, and ercentiles

!. Standard deviation, ?ariance

". @ormal 7istribution and standard scores

UNIT 4 : E(r*6(+'$ Pro77*$*' 2 %((.s/

NO OF HOURS:

INSTRUCTIONAL OB1ECTIESStudents should be able to:

. 4se probability as a measure of chance

!. Explain the terms events, total outcomes. sample space, probability.

". %dentify sample space for a given experiment.

$. %dentify given events from a sample space.

&. 7evise and carry out experiments to determine simple probability

'. alculate the probability of an event occurring or not occurring.

). 7escribe events as li*ely, unli*ely, impossible, biased etc... using probability values.

+. 4se probability models to compare experimental results with mathematical expectations.

-. Explain the difference between discrete and continuous random variables

COURSE CONTENT

. oncept of robability

!. Sample Space

". Simple experimental probability

$. (ange of a probability

UNIT 5 : T8(or('*)$ Pro77*$*' 4 %((.s/

NO OF HOURS:


Student should he able to:

. 4se the idea of sets to calculate the probability A#BCD and A#4CD

!. 4se the property that A#D F A#D

". 7evelop an understanding of conditional probability and solve related problems

$. 7istinguish between dependent, independent, mutually exclusive and complementary



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events

&. alculate and use addition and multiplication rules appropriately in simple cases

'. 4se the property that the sum of all probabilities, AxiD, i,!, G n, of sample points xi if

the sample space is

). onstruct and use tree and ?enn diagrams in solving simple problems in probability

+. #pply simple permutation and combination for finding sample space

-. 4se simple permutation and combination in finding the probability of an event

/. Ma*e predictions that are based on experimental or theoretical probabilities

. 7evelop an appreciation for the use of probability in the real world

!. Solve simple problems involving probability in a variety of real contexts

COURSE CONTENT

. Types of events

!. robability rules

". ermutation and combination$. onditional probability

&. Tree and ?enn diagrams

STUDENT ACTIITIES

ooperative learning, small and large group discussions, guided practice, coaching, and whole

class type presentations, some led by participants, will be the maHor teaching processes used in

this course. 4nit and cumulative wor*sheets will be one method of assessing students0 learning

and concretizing *nowledge learnt in class. # real life application proHect will be completed by

students in their cooperative learning groups. This proHect involves students

studying2investigating a problem2issue2concern at their school and design data collectioninstruments Ae.g. interviews, 5uestionnaire, etc.D to collect data, then to summarize data, present

findings Aincluding conclusions and recommendationsD. %t is expected that students will learn

s*ills of organization, cooperation, and delegating wor* to team members, and other learning

and social s*ills in completing proHect.

ASSESSMENT

The assessment strategies recommended emphasize variety in the types and nature of the

assessment that will utlize students talents and special abilities and also provide opportunities for

them to demonstrate their declarative *nowledge and s*ills.

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the attitudes, values and behaviour of the

programme.

4nits , ! 9 " R(s(r)8 +, Coo(r'*;( L(r+*+= S.*$$s: 7ata

collection 9 analysis of statistical data Pro()'/

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ollect data from &/ students at college on the high

school they attended and the number of subHects

passed in the K and the grades at which they

passed. #lso, data related to their age and parish of

residence. 8rganize the data and conduct

appropriate statistics that will ade5uately summarize

the data. Similar proHect can be designed by the

lecturer.

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3eneral Education and

rofessional ourse

Pr(s(+''*o+ S.*$$s: resentation of roHect

Iindings

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4nit 6& D()$r'*;( >+o%$(,=(: Two unit tests !/J

4nits 6& Iinal External Examination &/J

LEARNIN" RESOURCES

. Social and Economic Survey of 1amaica, published by the lanning %nstitute of 1amaica

!. @ewspaper clipings, magazines, calculators, and computers

". Electronic media and %nternet resources

$. Software programmes Ae.g. SSS programmeD


MT 202S-Statistics & Probability

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