Prospectus: Diploma in Applied Statistics (IASSL)€¦ · Prospectus: Diploma in Applied Statistics (IASSL) 8 | P a g e 3.1 Exemptions i. The Council upon the recommendation of the

Prospectus: Diploma in Applied Statistics (IASSL)

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Table of Contents

1. Introduction ........................................................................................................... 4

1.1 Institute of Applied Statistics Sri Lanka (IASSL) ................................................... 4

1.2 The General Objectives of the Institute .............................................................. 4

2. Diploma in Applied Statistics ................................................................................... 6

2.1 General.............................................................................................................. 6

2.2 Credit Rating ..................................................................................................... 6

2.3 Description of Course Code................................................................................ 6

2.4 Course Units ...................................................................................................... 7

3. Admission Requirements ......................................................................................... 7

3.1 Exemptions ........................................................................................................ 8

5. Evaluation ............................................................................................................... 8

5.1 Methods of Evaluation....................................................................................... 8

5.2 Grading System ................................................................................................. 9

5.3 Grade Point Average ........................................................................................ 10

5.4 Referred Students............................................................................................ 10

6. Diploma Awarding Criteria..................................................................................... 10

6.1 Diploma in Applied Statistics ............................................................................ 10

6.2 Award of the Diploma in Applied Statistics....................................................... 11

Appendix ................................................................................................................... 12

Course Curriculum and Modules............................................................................ 12


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1. Introduction

1.1 Institute of Applied Statistics Sri Lanka (IASSL) Institute of Applied Statistics Sri Lanka has been incorporated by the Parliament of the Democratic Socialists Republic of Sri Lanka on 20th September 2011 by the Institute of Applied Statistics, Sri Lanka (Incorporation) Act No. 38 of 2011. This is the successor of the former Applied Statistics Association, Sri Lanka (ASASL) established in 1999. IASSL is a member of the Organization of Professional Association (OPA) of Sri Lanka. The IASSL is situated in the Professional Centre, 275/75 Prof. Stanley Wijesundera Mawatha, Colombo 7, Sri Lanka.

1.2 The General Objectives of the Institute The general objectives of the Institute are to promote and assist the advancement of Applied Statistics by

furtherance of research, development, education, training and extension;

to undertake or collaborate in the preparation, publication

and dissemination of useful information pertaining to Applied Statistics by means of seminars and lectures and the publication of articles on Applied Statistics in the Journal titled the “Sri Lankan Journal of Applied Statistics” to foster the training of research workers;

to undertake research in the area of Statistics with the object

of improving Experimental Techniques, Statistical Methods and Data Analysis;

to co-operate with governmental and non-governmental

organizations and national and international institutes engaged in work related to Statistics in order to promote research, development, education, training and extension;

to promote professional interests of the members of the

Institute.


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2. Diploma in Applied Statistics (DAS)

2.1 General

An academic year consists of two semesters and a semester consists of 15 weeks that may be spread over six calendar-months. The Diploma in Applied Statistics is a one-academic year programme and equivalent to the Level 1 of a B. Sc. Degree. The successful students would be able to register for Higher Diploma in Applied Statistics which ultimately leading to a qualification equivalent to a B. Sc. Degree. The Diploma in Applied Statistics consists of a combination of course units indicated in Section 2.4

2.2 Credit Rating

The credit rating is an expression used to denote the “academic value” of a course unit. The credit ratings are as follows: 15 hours of lectures/tutorials = 1 credit. 30 – 45 hours of laboratory work = 1 credit.

2.3 Description of Course Code

The Diploma programme consists of a combination of course units, each of which is a unit of study normally completed within a semester. Each course unit is assigned a course code which consists of eight alphanumeric characters as follows:

Discipline Code Mathematics Statistics

MATH STAT

First four letters: Area/Discipline of study. First digit (5th character): Year of study within the

Diploma Program. Second digit (6th character): The credit rating. Last two digits: The serial number of the course unit. eg. The course code STAT 1201 would mean:

STAT = Statistics, 1 = Year 1, 2 = Credit rating of two, 01 = Serial number of the course unit.


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2.4 Course Units

Course Code

Course Title Credit Rating

SEMESTER 1 MATH1301 Mathematics for Statistics 3 STAT1301 Descriptive Statistics & Probability 3 STAT1302 Data Collecting Techniques 3 STAT1303 Elementary Data Analysis 3 STAT1204 Introduction to Statistical Packages 2 STAT1105 Report Writing & Presentation Skills 1

Total 15 SEMESTER 2

STAT1306 Introduction to Statistical Distributions 3

STAT1307 Statistical Inference 3 STAT1308 Regression Analysis 3 STAT1309 Surveys & Sampling 3 STAT1310 Project 3

Total 15 Grand Total 30

3. Admission Requirements

To eligible for the admission to the Diploma Programme in Applied Statistics a candidate should have one of the following:

i. A minimum of three passes at G.C.E.(A/L) examination in any stream with Mathematics/ Higher Mathematics/ Combined Mathematics/ Business Statistics as a subject,

or ii. A minimum of three passes at G.C.E.(A/L) examination in any

stream and a minimum of ‘B’ pass for Mathematics at G.C.E.(O/L),

or iii. Secured an equivalent qualification acceptable to the

Executive Council.


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3.1 Exemptions

i. The Council upon the recommendation of the ATC, may grant specific credit exemptions in recognition qualification(s) obtained previously

ii. The total credit exemptions so granted shall not exceed ten (10) credits out of the total of thirty (30) credits of the program required for the award of the Diploma.

iii. The grade that shall be awarded for a exempted course unit

should be grade C.

5. Evaluation

5.1 Methods of Evaluation

The knowledge and skills of the student in a course unit will be assessed throughout the semester as well as at the end of each semester by means of

Continuous assessments, Evaluation of Reports, Submission of Dissertations and

Presentations, End of semester Examination.

The content, nature and weightage for each of these components will be in accordance with the Code of Procedures relating to the methods of evaluation as determined by the Council upon the recommendation of the ATC. Continuous assessment Marks obtained for mid semester examination tutorials/ spot test/ practical/ assignments/ quizzes record/ reports/ presentations will be taken into account in the determination of the final grade, depending on the nature of each course unit.


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End of Semester Examination: A Course unit will be evaluated at the end of corresponding semester either by a theory paper or a practical examination or both, depending on the nature of the unit. The duration of each end of semester theory examination will be as follows: For a one credit unit a minimum of one hour paper. For a two credit unit a two-hour paper. For a unit of three credits a two and half or three - hour

paper. For a unit of more than

three credit a three hour paper or two

papers of two hours duration.

The duration of each end of semester practical examination will be notified by the ATC with the approval of the Council at the beginning of the course unit.

IMPORTANT A student will be eligible to sit the end of semester examination in respect of any course unit, only if he/she has obtained the minimum mark specified by the ATC for the continuous assessment component in respect of the course unit.

5.2 Grading System

Marks Range Grade Grade Point Value

85 – 100 A+ 4.0 70 – 84 A 4.0 65 – 69 A- 3.7 60 – 64 B+ 3.3 55 – 59 B 3.0 50 – 54 B- 2.7 45 – 49 C+ 2.3 40 – 44 C 2.0 35 – 39 C- 1.7 30 – 34 D+ 1.3 25 – 29 D 1.0 00 – 24 E 0.0


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5.3 Grade Point Average

Grade Point Average (GPA) is the credit-weighted arithmetic mean of the Grade Point Values, i.e. the GPA is determined by dividing the total credit-weighted Grade Point Value by the total number of credits. For example, a student who completed three course units of three credits each, two course units of two credits each and two course units of one credit each with grades A+, B, D, C+, E, B+ and C respectively would have a GPA of

1 1 2 2 3 3 3

2.0 1 3.31 0.0 2 2.3 2 1.0 3 3.0 3 4.0 3

15

2.0 3.3 4.6 3.0 9.0 12.0

26.215

9.33 (Correct to the second decimal place)

5.4 Referred Students

A student who obtains a grade below C in a particular course unit may re-sit the examination in respect of that course unit for the purpose of improving the grade; the best grade obtainable in this instance is C. In the event a student obtains a lower grade while attempting to better the grade, he/she will be entitled to the higher grade previously obtained.

6. Diploma Awarding Criteria

6.1 Diploma in Applied Statistics

To be eligible for the Diploma in Applied Statistics, a student should offer a minimum of 30 credits, with at least 12 credits per each semester.

Furthermore a student should i. have a minimum Grade Point Average (GPA) of 2.0,


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ii. obtain grades of C or better in the specified course units in Section 2.4 aggregating to a minimum of 24 credits, with at least 12 credits per each semester, and at least grades of D in the remaining course units,

and iii. complete the relevant requirements within a period of three

academic years.

Award of Merit Pass

A student has to complete all the requirements given in Section 6.1. to be considered for the award of Merit Pass. A student may be awarded Merit Pass provided he/she,

i. obtains a minimum Grade Point Average of 3.30

and ii. obtains grades of C or better at first attempt in the specified

course units in Section 2.4 aggregating to 30 credits within three academic years.

Award of Distinction

A student has to complete all the requirements given in Section 6.1 to be considered for the award of Distinction.

A student may be awarded Distinction provided he/she,

i. obtains a minimum Grade Point Average of 3.70

and ii. obtains grades of C or better at first attempt in the specified

course units in Section 2.4 aggregating to 30 credits within three academic years.

6.2 Award of the Diploma in Applied Statistics

The award in respect of each student shall be recommended to the Executive Council by the Board of Examiners constituted as follows:

President/IASSL (Chairperson), Chairperson of the ATC, Examiners, The Secretary/IASSL shall function as the Secretary to the Board of Examiners.


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Appendix

Course Curriculum and Modules

Course Code

Course Title Credit Rating

SEMESTER 1

MATH1301 Mathematics for Statistics 3

STAT1301 Descriptive Statistics & Probability 3

STAT1302 Data Collecting Techniques 3

STAT1303 Elementary Data Analysis 3

STAT1204 Introduction to Statistical Packages 2

STAT1105 Report Writing & Presentation Skills 1

Total 15

SEMESTER 2

STAT1306 Introduction to Statistical Distributions 3

STAT1307 Statistical Inference 3

STAT1308 Regression Analysis 3

STAT1309 Surveys & Sampling 3

STAT1310 Project 3

Total 15

Grand Total 30


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Course

Title

Mathematics for

Statistics

Course

Code MATH 1301

Level 1 Semester I Credits 03 Theory (hrs) 45

Practical (hrs) -

Learning Outcomes:

On the completion of this course unit student will be able to:

Describe sets and subsets and apply rules in Set Algebra,

Use functions and their properties, and draw graphs of them,

Apply different techniques in counting and summation to calculate

probability,

Apply coordinate geometry of straight lines,

Compute the limit of a given function, Differentiate and integrate

functions.

Course Contents:

Number Line: Integers, Rational numbers, Natural numbers,

Irrational Numbers, Prime Numbers, Surds.

Set Theory: Description of a set and set notations, Subsets, Set

operations, Rules of set Algebra.

Functions: Definition, Domain, co-domain and range of a function,

Types of functions and their properties, Graphs of functions.

Techniques of Counting: Permutation and Combination, Binomial

expansion and binomial theorem.

Techniques of Summation: Series, Sum of a series, Arithmetic and

geometric series, Methods of summation.

Coordinate Geometry: Cartesian coordinate systems, Coordinate

geometry of straight lines.

Calculus: Limits, Derivatives, Differentiation and integration.

Matrix: Basic Matrix Algibra

Mode of Delivery:

Lectures and Tutorials

Mode of Evaluation:

Tutorials/Assignments/Quizzes, Mid-semester test and End of semester

examination

Note:

Weightage given to each component will be announced by the Lecturer

at the beginning of the course unit.

References:

1. Thomas W. Judson, Stephen F. Austin (2008), ‘Abstract Algebra Theory

and Applications’, State University.

2. R.S Aggarwal (2013), ‘Part I - Senior Secondary school Mathematics for

Class 12’. Bharati Bhawan


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Course

Title

Descriptive Statistics

and Probability

Course

Code STAT 1301


Practical (hrs) -

Learning Outcomes:


Differentiate the types of variables and data used in many

applications,

Analyse data using desciptive statistics, present and interpret findings

in a statistical way,

Look at data more logically, analytically, critically and creatively,

Apply probability theory in solving real-life situations.

Course Contents:

Technical terms used in Statistics: Population, Objects, Category,

Attribute, Sample, Parameter, Statistic, Estimate, Variables; Nominal,

ordinal, interval and ratio variables, Discrete and continuous

variables,

Presentation of data in tabular and graphical form: Ungrouped and

grouped frequency tables, Cumulative frequency tables, Cumulative

frequency polygon, Histograms, Frequency polygon and ogives, Pie

charts, Bar charts; Pareto diagrams, Stem and leaf plot.

Measures of central tendency: Mean, Median, Mode, Quartiles,

deciles and percentiles, Weighted mean, Harmonic mean and

Geometric mean, Compound mean

Measures of dispersion or spread: Range, Quartile deviations, Semi-

interquartile range, Mean deviation, Variance and standard deviation,

Coefficient of dispersion, Compound variance.

Measures of skewness: Skewness, Shape of a distribution, Karl

Pearson’s measure of skewness, Pearsonian coefficient of skewness.

Kurtosis: Convexity of a curve, Measure of kurtosis, Mesokurtic,

platykurtic and leptokurtic curves.

Probability: Axiomatic definition of probability, Elementary results of

probability,

Conditional probability: Definition, Related results, Chain rule,

Independence of events, Partition of a sample space, Total

probability, Bayes’ theorem, Computation of probability.

Mode of Delivery:


Mode of Evaluation:


examination


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Note:



References:

1. Mark L. Berenson, David M. Levine, Timothy C. Krehbiel; 8th Edition,

2001 ‘Basic Business Statistics’, Prentice Hall.

2. Terry Sincich, Pearson College Division, 5th Edition, 1995 ‘Business

Statistics by Examples’.

3. Frank H. Dietrich, Nancy J. Shafer; 1984 ‘Business Statistics: An

Inferential Approach’, Dellen Pub. Co.

4. Alexander M. Mood, Franklin A. Graybill, Duane C. Boss; 3rd Edition,

1984 ‘Introduction to the Theory of Statistics’, McGraw-Hill.


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Course

Title

Data Collecting

Techniques

Course

Code STAT 1302


Practical (hrs) -

Learning Outcomes:


Classify studies under different categories,

Use data collecting techniques for quantitative, qualitative,

Design Basic Experiments,

Develop questionnaires for data collection in surveys.

Course Contents:

Brief outline of the terminology: Population, Sample, Sampling

unit/experimental unit, Sampling error, Non-sampling error, Random

variation, Bias.

Classifications of studies: Observational vs experimental,

Exploratory vs confirmatory, Cross-sectional vs retrospective.

Data Collecting Techniques for survey: Non probability and

probability sampling techniques)

Data Collecting Techniques for Experimental Studies: Brief

introduction the terminology [Completely Randomized Design (CRD),

Randomized Complete Block (RCBD), Lattin Square Design (LSD)].

Mode of Delivery:


Mode of Evaluation:


examination

Note:

Weightage given to each component will be announced by the Lecturer at

the beginning of the course unit.

References:

1. Douglus C Montgomery; 8th Edition, 2013, ‘Designs and Analysis of

Experiments’, Wiley.

2. William G Cochran; 3th Edition, 1977, ‘Sampling Techniques’, Wiley.


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Course

Title

Elementary Data

Analysis

Course

Code STAT 1303


Practical (hrs) 30

Learning Outcomes:


Apply meaningful graphical and numerical descriptive measures in

practices,

Correctly identify and quantify the association between two variables

with different scales of measure,

Use the least squares method in simple regression analysis.

Course Contents:

Instruction to modelling using SPSS: Simple Linear Regression.

Measures of Association between two variables using SPSS: Two

qualitative variables using contingency tables, One quantitative

variable and one qualitative variable, Two quantitative variables,

Covariance and correlation between two variables.

Tabular data summaries using SPSS: Frequency and contingency

table.

Graphical data summaries using SPSS: Bar and pie charts, boxplots,

histograms, scatter plots.

Numerical data summaries using SPSS: Explanatory data analysis

Mode of Delivery:


Mode of Evaluation:


examination

Note:



References:

1. Perm S. Mann; High School Edition, 8th Edition, 2013 ‘Introductory

Statistics’, Wiley,.

2. Roger E. Kirk;, 1999, ‘Statistics: Introduction’, Harcourt Brace

College.


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Course

Title

Introduction to

Statistical Packages

Course

Code STAT 1304


Practical (hrs) 30

Learning Outcomes:


Write data into the SPSS /MINITAB data files,

Construct tables and draw graphs using SPSS/MINITAB,

Perform data management using SPSS/MINITAB,

Conduct statistical tests using SPSS/MINITAB.

Course Contents:

Reading and Defining Data: Datasets, Copy Data and Properties, Data

Dictionary, Setting, Reading Data, Database, Data Dictionary, Labels,

Measurement Level, Value Labels, Variable Types, Variable

Properties, Variable Sets, Define Missing Values

Data Understanding / Descriptive: Cross Tabs, Graphs, Results,

Descriptive Statistics, Dispersion, Box plots, Explore Procedure,

Frequencies, Means Procedure, Statistics Cross Tabs, Summary

Statistics, Univariate Statistics.

Data Management: Adding Cases, Aggregation, Duplicate Cases,

Select Cases, Split File, Data Transformations, Categorical Variables,

Logical, Functions, Strings, Using Functions, Counting Values Across

Variables, Counting Variables Across Cases, If Conditions, Recoding,

Computing New Variables, Missing Values Recode.

Editing and Exploring: Charts, Chart Builder, Chart Editor, Chart

Templates, Exporting Results, Navigating Using, Pivot Tables, Output

Viewer, Pivot Tables Editor, Scale Data: Box Plots, Scale Variable

Charts, Scatter plots, Table Looks

Basic Inferential Statistics: Chi-Square, Correlations, Regression, T-

Tests,

Mode of Delivery:


Mode of Evaluation:


examination

Note:



References:

1. Statistical Analysis using SPSS (2012) by Karuthan Chinna,

Krishnakumari Karuthan, Choo Wan Yuen, Publisher - Pearson Malaysia


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Sdn Bhd (ISBN 978-967-349-218-3)

2. How to use SPSS Statistics, A step by step guide to analysis and

interpretation – 7th edition by Bran C. Cronk

3. The Student Guide to MINITAB 14 by John De Makenzie


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Course

Title

Report Writing and

Presentation Skills

Course

Code STAT 1305


Practical (hrs) 20

Learning Outcomes:


Write an effective statistical reports,

Perform an effective and a standard presentation on a given topic.

Course Contents:

Report Writing: Definition of a report, Types of reports, Initial

preparation, Planning and research, Report structure.

Presentation Skills: Preparation and delivering an effective

presentation.

Mode of Delivery:

Lectures, Practical and Group discussion

Mode of Evaluation:

Repot writing /Assignments,

Note:



References:

1. www. ssdd.bcu.ac.uk/learner/writing guides; ‘Birmingham City

University Writing Guides’.

2. www.linkageinc.com; ‘A guide to Effective Presentation Preparation’.


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Course

Title

Introduction to

Statistical Distribution

Course

Code STAT 1306


Practical (hrs) -

Learning Outcomes:


Describe the probability distribution corresponding to a given real-

life situation involving one variable,

Compute Expected Value and Variance and application of them in

different situations

Apply principles of Probability and Statistics to real-life situations,

State the importance of Central Limit Theorem.

Course Contents:

Random variables: Rationale for the introduction of random

variables, Definition of a random variable, Types of random variable.

Distributions: Discrete/Continuous probability distribution,

Cumulative probability distribution.

Properties of a random variable: Expected values, Variance,

Standers deviation, Moment.

Discrete Probability Distributions: Discrete uniform, Bernoulli,

Binomial, Poisson.

Continuous Probability Distributions: Uniform, Normal and

Exponential

Central limit theorem: Introduction to sampling distribution and

central limit theorem

Mode of Delivery:


Mode of Evaluation:


examination

Note:



References:

1. Mark L Berenson, David M Levine and Timothy C Krehbiel; (8th Edition), 2001

‘Basic Business Statistics – Chapters 5 – 7’, Prentice Hall.

2. Alexander M. Mood., Franklin A. Graybill, Pittenger Duane C. Boes; 3rd

edition, Reprint (2005), ‘Introduction to the Theory of Statistics’, McGraw-

Hill.

3. Hoel Paul G., Port Sidney C., Stone Charles J.; 1st edition, (1971),

‘Introduction to Probability Theory’, Houghton Mifflin Company.


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Course

Title Statistical Inference

Course

Code STAT 1307


Practical (hrs) -

Learning Outcomes:


Compute point estimates of mean and variance of a population,

Compute interval estimates of mean, variance and proportion of a

population,

Explain Chi-square distribution and Student’s t distribution,

Use the test of hypothesis in making statistical decisions.

Course Contents:

Point estimation: Methods of finding Point estimates for mean,

variance and proportion.

Introduction to distribution: Student’s t distribution, F- distribution,

Chi-square distribution,

Interval estimation: Level of significance of an interval estimate,

Interval estimates for mean when variance is known/unknown,

Interval estimates for variance and proportion.

Test of Hypothesis: Null and alternative hypotheses, Type I and Type

II errors, Power of Test, Test of hypothesis for one sample for mean,

proportion and variance, Test of hypothesis for two sample tests for

mean, proportion and variance.

Mode of Delivery:


Mode of Evaluation:


examination

Note:



References:

1. Mark L. Berenson, David M. Levine, Timothy C. Krehbiel; 8th Edition,

2001 ‘Basic Business Statistics’, Prentice Hall.

2. Terry Sincich, Pearson College Division, 5th Edition, 1995 ‘Business

Statistics by Examples’.

3. Frank H. Dietrich, Nancy J. Shafer; 1984 ‘Business Statistics: An

Inferential Approach’, Dellen Pub. Co.

4. Alexander M. Mood, Franklin A. Graybill, Duane C. Boss; 3rd Edition,

1984 ‘Introduction to the Theory of Statistics’, McGraw-Hill.


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Course

Title Regression Analysis

Course

Code STAT 1308

Level 1 Semester II Credits 03 Theory (hrs) 30

Practical (hrs) 30

Learning Outcomes:


Students will be able to understand method and concept of simple

and multiple regression and correlation

Develop an understanding of the theoretical basis for regression

analysis

Enable students to write simple and multiple linear regression

models in matrix format

Students will be able to build regression models

Students will be able to present the results using available statistical

software

Course Contents:

Basic ideas of Applied Regression Analysis

Simple Linear Regression; Residual Analysis; Autocorrelation;

Multiple Regression;

Parameter Estimation and Testing

Model Selection Procedures; Polynomial Regression; Indicator

Variables; Multicollinearity; Outliers and Influential Observations.

Mode of Delivery:


Mode of Evaluation:


examination

Note:



References:

1. Classical and Modern Regression with Applications, Myers, second

edition, Duxbury Press, 1990

2. Introduction to Linear Regression Analysis, D. Montgomery and E.

Peck, Wiley, 2001.

3. Linear Statistical Models, John Neter, Kutner ,Nachtshemand

WassemanApplied, McGrraw‐Hill, 1996


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Course

Title Surveys and Samplings

Course

Code STAT 1309


Practical (hrs) 30

Learning Outcomes:


Construct a questioner for a survey

Plan and execute a sample survey

Analyze sample survey results

Minimize errors and biases of sample surveys

Estimate population parameters under different sampling techniques

Course Contents:

Planning a sample survey

Mode of Questioner: Face to face interview, Mail Questioner,

Internet survey, Telephone survey

Questioner Construction: Content of a questioner, Sequence of

Questioner, Question type

Sample size calculation: For homogenous /heterogeneous

population

Analysis of survey dada: Qualitative and Quantitative data analytical

methods

Errors and biases of sample surveys

Mode of Delivery:


Mode of Evaluation:


examination

Note:



References:

1. William G Cochran; 3th Edition, 1977 ‘Sampling Techniques’,

Wiley.


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Course

Title Project

Course

Code STAT 1310

Level 1 Semester I Credits 03 Theory (hrs) -

Practical (hrs) 90

Learning Outcomes:


Carry out a study using statistical procedure,

Do correct statistical analysis based on the data collected under the

study and draw conclusions based on the analysis,

Write a report on the project,

Present the outcomes of the study.

Course Contents:

Each student should select a project with the consultation of a

Supervisor assigned by the ATC. Under the guidance of the Supervisor

student should carry out the study. Repot should be prepared in the

format approved by the Council on the recommendation of ATC.

Mode of Evaluation:

Presentations and project report

Note:




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Documents

Prospectus: Diploma in Applied Statistics (IASSL)€¦ · Prospectus: Diploma in Applied Statistics (IASSL) 8 | P a g e 3.1 Exemptions i. The Council upon the recommendation of the