Time Series & Cross Sectional Analysis Guide

TIME SERIES & CROSS SECTIONAL ANALYSIS
QUANTITATIVE TECHNIQUES (5564)
ASSIGNMENT # 2
HUMA WASEEM
ROLL # BR564185
COL MBA / MPA
SPRING SEMESTER 2018
DEPARTMENT OF BUSINESS ADMINISTRATION
ALLAMA IQBAL OPEN UNIVERSITY ISLAMABAD
HUMA MALIK
2018

CONTENTS
1. INTRODUCTION . .... 1
1.1. Definitions .... 1
1.1.1.Time Series Design/Analysis .. 1
1.1.2.Cross Sectional Design/Analysis .. . ......... 1
2. TIME SERIES ANALYSIS / RESEARCH . .. 1
2.1. Goals of Time Series Analysis . ... 2
2.2. Types of Time Series ... 2
2.3. Components of a Time Series . 4
2.4. Techniques Used in Time Series Analysis .. 8
2.5. Advantages & Disadvantages ..... 9
2.5.1.Advantages of Time Series Analysis . ... 9
2.5.2.Disadvantages of Time Series Analysis ... . . 9
3. CROSS SECTIONAL ANALYSIS / RESEARCH 10
3.1. Types of Cross-Sectional Surveys . 11
3.2. Advantages & Disadvantages . 11
3.2.1.Advantages of Cross-Sectional Study . 11
3.2.2.Disadvantages of Cross-Sectional Study . . 11
4. COMPARISON BETWEEN TIME SERIES AND CROSS SECTIONAL
ANALYSIS . ......
12
4.1. Time-Series Data . .... . ... 12
4.2. Cross-Sectional Data . .... 12
PRESTON UNIVERSITY KOHAT- ISLAMABAD CAMPUS-
LIBRARY STATISTICAL STUDY
5. INTRODUCTION TO PRESTON UNIVERSITY 15
5.1. Charter / NOC 15
5.2. Mission Statement 15
5.3. Campuses 15
5.4. Islamabad Campus - Facilities . 15

5.5. LIBRARY 16
5.5.1.Library Overview 16
5.5.1.1. Books Collection . 16
5.5.1.2. Library Website 16
5.5.1.3. Research Journals 16
5.5.1.4. Journals / Magazines 16
5.5.1.5. Newspapers .. 17
5.5.1.6. HEC National Digital Library Resources 17
5.5.1.7. Reports & Theses 17
5.5.1.8. CDs/DVDs (Books / Lectures/ Events) 17
5.5.2.LIBRARY STATISTICS . . 17
5.5.2.1. Books Statistics 19
5.5.2.2. Research Journals . 20
5.5.2.3. Circulation Statistics . 21
6. CONCLUSION . 22
7. RECOMMENDATIONS 23
8. . . 23

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1. INTRODUCTION
Time is a dimension of every study. Time is incorporated in two ways, cross-sectionally and
longitudinally. Cross-sectional research gathers data at one time point and creates a kind of
Longitudinal research gathers data at multiple time points and
general, longitudinal studies are more difficult to conduct and require more resources.
Researchers may collect data on many units at many time points and then look for patterns
across the units or cases. There are three types of longitudinal research: time series, panel,
and cohort.(Neuman, 2013).
1.1 Definitions:
1.1.1 TIME SERIES DESIGN/ANALYSIS
A research design in which measurements of the same variables are taken at
different points in time, often with a view to studying social trends. For this reason
such designs are sometimes also known as trend designs and are distinguishable
-sectional designs in which measurements are taken only
once. (Jupp, ed., 2016)
A quasi-experimental design involving one group that is repeatedly pretested,
exposed to an experimental treatment, and repeatedly post-tested. (Gay, Mills &
Airasian, 2012)
Longitudinal research in which information can be about different cases or people
in each of several time periods. (Neuman, 2013).
1.1.2 CROSS SECTIONAL DESIGN/ANALYSIS
Any collection of data from a sample of individuals (or groups) at a particular
point in time as a basis for inferring the characteristics of the population from which
the sample comes. A cross-sectional survey of a population can be one-off or
repeated at
the population in response to societal and policy change. A population census is in
sample. (Jupp, ed., 2016)
A survey in which data are collected from selected individuals in a single time
period. (Gay, Mills &Airasian, 2012)
Any research that examines information on many cases at one point in time.
(Neuman, 2013).
2. TIME SERIES DESIGN/ANALYSIS
A time series is a set of observations ordered by time. In the very simplest case, a time series
is a sequence of recorded values of one variable taken at equally spaced time points. For
example, the (time ordered) sequence of daily closing prices of the Apple Inc. stock is a
time series. Time series can be found in the fields of engineering, science, sociology and
economics. Time series analysis is a branch of statistics which deals with techniques
developed for drawing inferences from time series. The first step in the analysis of a time
series is the selection of a suitable model (or class of models) for the data. To allow for the

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unpredictable nature of future observations it is assumed that each observation is a realized
value of a random variable.
Given a particular time series, the primary goals of time series analysis are:
i. to set up a hypothetical statistical model to represent the series in order to obtain
insights into the mechanism that generates the data, and
ii. once a satisfactory model has been formulated, to extrapolate from the model in
order to anticipate (forecast) the future values of the time series.
(Preve, 2008).
2.1. Goals of Time Series Analysis:
1. Descriptive: Identify patterns in correlated data-trends and seasonal variation
2. Explanation: understanding and modeling the data
3. Forecasting: prediction of short-term trends from previous patterns
4. Intervention analysis: how does a single event change the time series?
5. Quality control: deviations of a specified size indicate a problem
Time series are analyzed in order to understand the underlying structure and function that
produce the observations. Understanding the mechanisms of a time series allows a
mathematical model to be developed that explains the data in such a way that prediction,
monitoring, or control can occur. Examples include prediction/forecasting, which is widely
used in economics and business. Monitoring of ambient conditions, or of an input or an
output, is common in science and industry. Quality control is used in computer science,
communications, and industry.
It is assumed that a time series data set has at least one systematic pattern. The most
common patterns are trends and seasonality. Trends are generally linear or quadratic. To
find trends, moving averages or regression analysis is often used. Seasonality is a trend that
repeats itself systematically over time. A second assumption is that the data exhibits enough
of a random process so that it is hard to identify the systematic patterns within the
data. Time series analysis techniques often employ some type of filter to the data in order to
dampen the error. Other potential patterns have to do with lingering effects of earlier
observations or earlier random errors. (Senter, n.d.).
2.2. Types of Time Series
A time series is a stretch of values on the same scale indexed by a time-like parameter. The
basic data and parameters are functions. Time series take on a dazzling variety of shapes
and forms, indeed there are as many time series as there are functions of real numbers.
Some common examples of time series forms are provided in Figure5.1. One notes periods,
trends, wandering and integer-values. The time series such as those in the figure may be
contemporaneous and a goal may be to understand the inter relationships. Concepts and
fields related to time series include: longitudinal data, growth curves, repeated measures,
econometric models, multivariate analysis, signal processing, and systems analysis. The
field, time series analysis, consists of the techniques which when applied to time series lead
to improved knowledge. The purposes include summary, decision, description, prediction.
The field has a theoretical side and an applied side. The former is part of the theory of
stochastic processes(e.g., representations, prediction, information, limit theorems) while
applications often involve extensions of

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regression, analysis of variance, multivariate analysis, sampling. The field is renowned for
jargon and acronyms white noise, ARMA, ARCH. (Brillinger, 2001).
Figure 5.1 Some different types of Time Series
Examples of Time Series:
Typical examples of time series also include historical data on sales, inventory,
customer counts, interest rates, costs, etc. Time series data are also often seen
naturally in many application areas including:
Economics - e.g. monthly data for unemployment, hospital admissions, etc.

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Finance - e.g. daily exchange rate, share prices, etc.
Environmental - e.g. daily rainfall, air quality readings.
Medicine - e.g. ECG brain wave activity every 2-8 secs.
Budget Analysis
Financial Market Analysis
Census Analysis
Inventory Management
Marketing and Sales Forecasting
Yield Projections
Seismological Predictions
Workload Projections
Time series can be categorized into two major classes namely: univariate or
multivariate. A univariate time series is a sequence of measurements of the same
variable collected over time. Most often, the measurements are sequence of events
made at regular time intervals. An event is an ordered pair consisting of temporal
value and an associated list of metadata (attributes) also known as header or general
description. (Fawumi, 2015)
2.3. Components of a Time Series
Analyzing time series means breaking down past data into components and then
projecting them forward. A time series typically has four components:
a) Trend (T) is the gradual upward or downward movement of the data over
time.
b) Seasonality (S) is a pattern of the demand fluctuation above or below the
trend line that repeats at regular intervals.
c) Cycles (C) are patterns in annual data that occur every several years.
They are usually tied into the business cycle.
d) Random variations (R
unusual situations; they follow no discernible pattern.
Figure 5.2& 5.3 shows a time series and its components. (Imdad Ullah, 2014)

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Figure 5.2 Product Demand Charted over 4 Year, withTrend and
Seasionality Indicated
Figure 5.3 Compnents of Time Series

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Trends:
The variation of observations in a time series over a long period of time is known as
Trends. Thus, a Trend short-term variations in the data. For
example:
Increase in Population
Increase in Gold Rates
Decrease in Death Rates
Any time series which is gradually increasing or decreasing over a long period of
time is said to have Trend.
Seasonality:
The variation of observations in a time series caused due to regular or periodic time
variations is known as Seasonality.
A repetitive pattern which can be predicted is termed as Seasonality. It also
considers the short-term fluctuations in time. For example:
Travel during holidays
Density of mosquitoes in winter
Ice cream sale in summer
Seasonality in sea turtle surface density
Cyclic Variation:
The variation of observations in a time series occurring generally in business and
economics where the rises and falls in the data are not of fixed period is known as
Cyclic Variation.
The duration of these cycles is more than a year. For example Sensex Price

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Irregular Variation:
The variation of observations in a time series which is unusual or unexpected is
known as Irregular Variation.
It is also termed as a Random Variation and is usually unpredictable. For example:
Strikes
Natural Disasters
There are various methods of isolating trend from the given series viz., the free hand
method, semi-average method, method of moving averages, method of least squares and
similarly there are methods of measuring cyclical and seasonal variations and whatever
variations are left over are considered as random or irregular fluctuations. The analysis of
time series is done to understand the dynamic conditions for achieving the short-term and
long-term goals of business firm(s). The past trends can be used to evaluate the success or
failure of management policy or policies practiced hitherto. On the basis of past trends, the
future patterns can be predicted and policy or policies may accordingly be formulated. We
can as well study properly the effects of factors causing changes in the short period of time
only, once we have eliminated the effects of trend. By studying cyclical variations, we can
keep in view the impact of cyclical changes while formulating various policies to make
them as realistic as possible. The knowledge of seasonal variations will be of great help to
us in taking decisions regarding inventory, production, purchases and sales policies so as to
optimize working results. Thus, analysis of time series is important in context of long term
as well as short term forecasting and is considered a very powerful tool in the hands of
business analysts and researchers.(Kothari, 2004).
Example:
The following plot is a time series plot of the annual number of earthquakes in the
world with seismic magnitude over 7.0, for a 99 consecutive years. By a time series
plot, the variable is plotted against time.

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Some features of the plot:
There is no consistent trend (upward or downward) over the entire time
span. The series appears to slowly wander up and down. The horizontal line
drawn at quakes = 20.2 indicates the mean of the series. Notice that the
series tends to stay on the same side of the mean (above or below) for a
while and then wanders to the other side.
Almost by definition, there is no seasonality as the data are annual data.
There are no obvious outliers.
variance is constant or not.
2.4. Techniques Used In Time Series Analysis
The fitting of time series models can be an ambitious yet ruthless undertaking. It requires
such as response models, uplift models, and so on where trends and seasonal effects may
not be present. For example, unlike data used for standard linear regression, time series data
are not necessarily independent and not necessarily identically distributed. One defining
characteristic of time series is that this is a list of observations where the ordering matters.
Ordering is very important because there is a dependency and changing the order could
change the meaning of the data.
There are a number of different methods for modeling time series data including the
following:
Box-Jenkins ARIMA models
Box-Jenkins Multivariate Models
Holt-Winters Exponential Smoothing (single, double, triple)
Unobserved Components Model
often decide the selection of the appropriate
technique.

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2.5. Advantages & Disadvantages
2.5.1. Advantages of Time Series Analysis
Time series ideas appear basic to virtually all activities. Time series are used by
nature and humans alike for communication, description, and visualization. Because
time is a physical concept, parameters and other characteristics are mathematical
models for time series can have real-world interpretations. This is of great
assistance in the analysis and synthesis of time series.
Time series are basic to scientific investigations. There are: circadian rhythms,
seasonal behaviors, trends, changes, and evolving behavior to be studied and
understood. Basic questions of scientific concern are formulated in terms of time
series concepts Predicted value? Leading? Lagging? Causal connection?
Description? Association? Autocorrelation? Signal? Seasonal effect? New
phenomenon? Control? Periodic? Changing? Trending? Hidden period? Cycles?
Because of the tremendous variety of possibilities, substantial simplifications are
needed in many time series analyses. These may include assumptions of
stationarity, mixing or asymptotic independence, normality, linearity. Luckily such
assumptions often appear plausible in practice. The subject of time series analysis
would be important if for no other reason than that it provides means of examining
the basic assumption of statistical independence invariably made in ordinary
statistics. One of the first commonly used procedures for this problem was the
Durbin Watson test. The auto-covariance and spectrum functions are now often
used in this context. (Brillinger, 2001).
2.5.2. Disadvantages of Time Series Analysis
There are scientific problems and there are associated statistical problems that arise.
Methods have been devised for handling many of these. The scientific problems
include: smoothing, prediction, association, index numbers, feedback, and control.
Specific statistical problems arise directly. Among these are including explanatory
in a model, estimation of parameters such as hidden frequencies, uncertainty
computation, goodness of fit, and testing. Special difficulties arise. These include:
missing values, censoring, measurement error, irregular sampling, feedback,
outliers, shocks, signal-generated noise, trading days, festivals, changing seasonal
pattern, aliasing, data observed in two series at different time points.
Particularly important are the problems of association and prediction. The former
asks the questions of whether two series are somehow related and what the strength
of any association is. Measures of association include: the cross-correlation and the
coherence functions. The prediction problem concerns the forecasting of future
values. There are useful mathematical formulations of this problem but because of
unpredictable human intervention there are situations where guesswork seems just
as good.
Theoretical tools employed to address the problems of time series analysis include:
mathematical models, asymptotic methods, functional analysis, and transforms.
(Brillinger, 2001).

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3. CROSS-SECTIONAL ANALYSIS/DESIGN
Cross-sectional data are data that are collected from participants at one point in time. Time
is not considered one of the study variables in a cross-sectional research design. However, it
is worth noting that in a cross-sectional study, all participants do not provide data at one
exact moment. Even in one session, a participant will complete the questionnaire over some
duration of time. Nonetheless, cross-sectional data are usually collected from respondents
making up the sample within a relatively short time frame (field period). In a cross-sectional
study, time is assumed to have random effect that produces only variance, not bias. In
contrast, time series data or longitudinal data refers to data collected by following an
individual respondent over a course of time.
The terms cross-sectional design and cross-sectional survey often are used interchangeably.
Researchers typically use one-time cross-sectional survey studies to collect data that cannot
be directly observed, but instead are self-reported, such as opinions, attitudes, values, and
beliefs. The purpose often is to examine the characteristics of a population.
Cross-sectional data can be collected by self-administered questionnaires. Using these
instruments, researchers may put a survey study together with one or more questionnaires
measuring the target variable(s). A single-source cross-sectional design asks participants to
provide all data about themselves with the questionnaire generally administered in a single
session. A multi-source cross-sectional design gathers data from different sources, such as
the sampled respondents, their supervisors, coworkers, and/or families, with different
questionnaires administered to the different populations.
Cross-sectional data can also be collected by interviews. There are one-to-one interviews,
panel interviews, and focus groups. In a one-to-one interview, a participant is questioned by
one interviewer. In a panel interview, a participant is interviewed by a group of
interviewers. In a focus group, a group of participants are simultaneously asked about their
attitudes or opinions by a discussion leader or facilitator.
Cross-sectional data can be gathered from individuals, groups, organizations, countries,
or other units of analysis. Because cross-sectional data are collected at one point in time,
researchers typically use the data to determine the frequency distribution of certain
behaviors, opinions, attitudes, or beliefs. Researchers generally use cross-sectional data to
make comparisons between subgroups.
Cross-sectional data can be highly efficient in testing the associations between two
variables. These data are also useful in examining a research model that has been proposed
on a theoretical basis. Advanced statistical tests, such as path analytic techniques, are
required to test more complex associations among multiple variables. The biggest limitation
of cross-section data is that they generally do not allow the testing of causal relationships,
except when an experiment is embedded within a cross-sectional survey. Cross-sectional
data are widely used in social science research. (Lavrakas, ed., 2008).

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3.1. Types of Cross-Sectional Surveys
Cross-sectional surveys can be conducted using any mode of data collection, including
telephone interviews in which landline telephones are called, telephone interviews in which
cell phones are called, face-to-face interviews, mailed questionnaires, other self-
administered questionnaires, electronic mail, Web data collection, or a mixture of data
collection modes. A variety of sampling frames can also be used to select potential
respondents for cross-sectional surveys: random-digit dialing frames, lists of addresses or
(landline) telephone numbers, lists of cell phone numbers, lists of businesses or other
establishments, and area probability frames. They may also use a multiple-frame approach
to sampling.
Examples of cross-sectional surveys include the American Community Survey,
Pakistan Bureau of Statistics, the Decennial Census long form, and many political and
opinion polls.
3.2. Advantages and Disadvantages
3.2.1. Advantages of Cross-Sectional Study
Research participants are usually more willing to cooperate in a one-time survey
research study than a series of multiple surveys taken at different points in time.
Researchers do not need to worry about the attrition problems that often plague
longitudinal studies, with some respondents not providing data at subsequent
survey waves.
Researchers are able to collect cross-sectional data from multiple individuals,
organizations, countries, or other entities.
Compared to longitudinal surveys, cross-sectional data are less expensive and
less time consuming to gather.
Used to prove and/or disprove assumptions
Captures a specific point in time
Contains multiple variables at the time of the data snapshot
The data can be used for various types of research
Many findings and outcomes can be analyzed to create new theories/studies or
in-depth research.
Highly efficient in testing the associations between two variables. These data
are also useful in examining a research model that has been proposed on a
theoretical basis.
Advanced statistical tests, such as path analytic techniques, are required to test
more complex associations among multiple variables.
Cross-sectional data are widely used in social science research.
(Liu, 2008).
3.2.2. Disadvantages of Cross-Sectional Study
There also are disadvantages with cross-sectional data. For example, cross-sectional
data are not appropriate for examining changes over a period of time. Thus, to
assess the stability of social or psychological constructs, longitudinal data are
required. Sociologists, in particular, made significant contributions to the early
design and conduct of cross-sectional studies.

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So, the disadvantages of cross-sectional study include:
Cannot be used to analyze behavior over a period to time
Does not help determine cause and effect
The timing of the snapshot is not guaranteed to be representative
Findings can be flawed or skewed if there is a conflict of interest with the
funding source
May face some challenges putting together the sampling pool based on the
variables of the population being studied.
The biggest limitation of cross-section data is that they generally do not
allow the testing of causal relationships, except when an experiment is
embedded within a cross-sectional survey.
(Liu, 2008)
4. COMPARISON BETWEEN TIME SERIES AND CROSS
SECTIONAL ANALYSIS
4.1. Time-Series Data
Time-series data refers to a set of observations taken over a given period of time at specific
and equally-spaced time intervals. That the observations are taken at specific points in time
means time intervals are discrete.
A good example of time-series data could be the daily or weekly closing price of a stock
recorded over a period spanning 13 weeks. Other appropriate examples could be the set of
monthly profits (both positive and negative) earned by Samsung between the 1st
of October
2016 and the 1st
of December 2016.
Time-series data can be used to predict future values of a given financial vehicle. Although
and the past are independent and therefore, past performance may not always be indicative
of future performance.
Time-series data has at least one systematic pattern with the most common patterns being
either trends or seasonality. Since most trends are linear or quadratic, regression analysis
and the moving average method are used to establish the linear relationship between
variables. Seasonality, on the other hand, is a trend that systematically keeps on repeating
itself over time. There are numerous modern computer-based programs that are used to
analyze time-series data including SPSS, JMP, SAS, Matlab, and R.
4.2. Cross-Sectional Data
Cross-sectional data refers to a set of observations taken at a single point in time. Samples
are constructed by collecting the data of interest across a range of observational units
people, objects, firms at the same time.

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A good example of cross-sectional data can be the stock returns earned by shareholders of
Microsoft, IBM, and Samsung as for the year ended 31st December 2015:
It is possible to pool time series data and cross-sectional data. If we were to study a
particular characteristic or phenomenon across several entities over a period of time, we
example, suppose we study the GDP
of 3 developing countries for a period spanning 3 years, from 2015 to 2017:
Country Year GDP
Kenya 2015
Kenya 2016
Kenya 2017
India 2015
India 2016
India 2017
Brazil 2015
Brazil 2016
Brazil 2017
Here, we would study a group of entities (Kenya, India, and Brazil) over a period of time (3
yrs).This would constitute panel data.(Quantitative Methods

PRESTON UNIVERSITY KOHAT
ISLAMABAD CAMPUS
LIBRARY STATISTICAL STUDY

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5. INTRODUCTION TO PRESTON UNIVERSITY
Preston University, Pakistan was established as School of Business and Commerce in 1984
to foster academic excellence. Preston University is seriously committed to improving the
quality of higher education in Pakistan. The university is managed by a group of dedicated
professionals and academicians who have committed their lives to the cause of higher
education in Pakistan. Since its inception in 1984, Preston Network has imparted knowledge
and skills to thousands of individuals through many teaching programs.
Preston University is the first private university of Pakistan and now has one of the largest
networks of campuses in the country. Being pioneer in private-sector higher education in
Pakistan, we are proud that Preston University plays an important role as a leader and pace-
setter in higher education in Pakistan.
5.1 Charter / NOC
Preston University, Kohat NWFP has been chartered by the Government of NWFP through
Ordinance No. LII of 2002, and is recognized by the Higher Education Commission,
Government of Pakistan. HEC has placed the University in the highes
5.2 Mission Statement
Preston University is committed to providing university education of the highest quality to
prepare students for professional and managerial positions. The mission of Preston is to
offer students the opportunity for personal growth and development, skill enhancement, or
professional job advancement through the provision of high quality education.
5.3 Campuses
Preston University has six (6) campuses one in Kohat, Peshawar, Lahore, Islamabad and
two in Karachi .
5.4 Islamabad Campus - Facilities
Purpose built campus on 2-acre plot with 100,000 sq.ft. covered area and one-acre
plot with 40,000 sqft covered area
Fully air-conditioned campus
45 Classrooms with a seating capacity of 1500
3 Seminar Rooms
5 Computer Labs
2 Auditorium
4 Engineering & Technology Labs
Equipped with state-of-the-art multimedia technology
High speed internet access and e-mail account facility for students
Library with 20,000+books, periodicals, journals, newspapers, audio / video
materials and software
160 highly qualified permanent and visiting Faculty Members
16 Ph.D Faculty Members

16
75 Management Staff Members
Pick & Drop Facility by University vans for female students and staff
( Preston University, 2018)
Every university maintains its statistics regarding employees & students, its finance &
maintain the statistics for every department. Here Library department is under consideration
for this assignment.
5.5 LIBRARY
Statistics may simply record the size and activity of a library at a point in time, but they can
also provide the data for benchmarking, for planning and demonstrating value. Time-series
data, i.e. data collected for the same library (or group of libraries) using the same data
elements over a number of years, is often used to illustrate change or stability.
5.5.1 LIBRARY OVERVIEW:
Library Covered Area: 3,428 sq. ft.
Sitting Capacity: 100+ students
Library has both types of collection: hard copies and digital/online.
Eight PCs (with UPS attached) for students with internet connection
Wi-Fi facility also available
Photocopier near Library
Library members 500+
Timings 8:30a.m to 8:30p.m. (Monday to Saturday)
5.5.1.1 Books Collection
Hard copy of Books: 25,700 +
Digital / Online Books: 7000+
Preston Digital Library (Calibre): 6,000+ books, magazines, lectures
E-Books through HEC National Digital Library (1000+ of e-books)
5.5.1.2 Library Website
URL is http://librarypreston.weebly.com/
5.5.1.3 Research Journals
Hard Copy = 124 (Foreign RJ= 95; Local RJ=29)
Digital - via HEC Digital Library and online access (1000+)
5.5.1.4 Journals / Magazines
Hard copy 25
Digital 18

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5.5.1.5 Newspapers
Subscribed 11
Digital / Online 1000+
Pakistan Online Newspapers
(http://www.onlinenewspapers.com/pakistan.htm)
World Newspapers (http://www.world-newspapers.com/)
5.5.1.6 HEC National Digital Library Resources
Access to thousands of research articles
ASTM BRILL
Ebrary IMF ELIBRARY
INFORMS ProQuest Dissertation & Theses
SIAM SPRINGER EBOOKS
Springerlink Taylor & Francis Journals
University of Chicago Press Wiley-Blackwell Journals
5.5.1.7 Reports & Theses
Graduate & Master level 4,760+
MPhil/ PhD theses 470+
5.5.1.8 CDs/DVDs (Books / Lectures/ Events)
CDs/DVDs Accompanying books 620+
CDs/DVDs of Lectures 1,350+
CDs/DVDs of Seminars, Guest lectures, conferences, workshops,
200+
5.5.2 LIBRARY STATISTICS
Large Libraries always maintain their data using quantitative methods. These data reflects
both time series and cross-sectional analysis. Some areas include:
Acquisition Statistics -
General Books
Subject Books
Reference Books
Newspapers & Magazines/Journals
Research Publications & Research Journals
Govt. Publications
Newspapers
Library Catalogue Statistics (Total Record of all types of materials)
General Books
Subject Books
Reference Books

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Govt. Publications
Newspapers
Use of
General Books Weekly, Monthly & Yearly Basis
Subject Books
Reference Books
Textbooks Books
Govt. Publications
Digital Library resources
Library Members Statistics (Membership record)
Students
Faculty members department wise
Staff
Higher Admin. (Deans, HODs, Directors, VC, etc.)
User Statistics circulation records
No. of users visit library and/or issue library material daily, weekly, monthly &
annual basis
Students & Faculty members
Staff
Higher Admin.
As library module of PUMIS is not fully operational for Library, so Library Statistics are
maintained manually. Some areas for which statistics are maintained are as below:
Circulation (issue/receive of reading materials)
Library catalogue (total books)
Library members
Books (with respect to its year of publication)
Research journals

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Here some data is collected to show some areas statistically.
5.5.2.1 BOOKS STATISTICS SUBJECT-WISE DIVISION
SUBJECTS AREA NO. OF BOOKS
1. MGT. SC / BUSINESS ADMIN. 5,168
2. COMP. SC. 3,828
3. EDUCATION 731
4. ECONOMICS 1,665
5. MATHEMATICS 952
6. PSYCHOLOGY 814
7. INT. RELATIONS 1,683
8. SCIENCE & TECHNOLOGY 2,105
9. ENVIRONMENTAL SC. / DRM / OHS 571
10. NANO SCIENCE & TECHNOLOGY 847
11. COMMON SUBJECTS 3,440
12. MISCLL. 1,410
13. TEXTBOOKS 2,520
TOTAL = 25,734
Graphical representation
This graph represents there are very less number of books on Education and Science
& Technology subjects since 1998 to date. Business administration / Management
Sciences and Computer Science have very strong collection of books. There is need
to enhance collection of book in weak areas also.
STATISTICS OF LIBRARY BOOKS (1998-2018)

20
5.5.2.2 RESEARCH JOURNALS ONE YEAR GROWTH
SUBJECT AREA
TOTAL RES. JR.
IN 2017
TOTAL RES. JR.
IN 2018
1. COMPUTER SC. 12 17
2. BUSINESS ADMIN. / MGT. SC. 24 39
3. EDUCATION 2 10
4. ECONOMICS 3 10
5. INT. RELATIONS 7 11
6. MATHEMATICS 2 8
7. PSYCHOLOGY 2 11
8. SCIENCE & TECH. 7 7
9. SOCIAL SC. 7 9
10. MISCLL. 1 2
GRAND TOTAL = 67 124
SD = 40.3 VAR = 1624.5
Graphical representation
This graph represents there is growth in collection of Research Journals during one year.
Business administration/Management Sciences and Computer Science have very strong
collection of Research Journals. There has been no growth in Science & Technology
research journals whereas minor growth in Social Science research journals.
RESEARCH JOURNALS IN YEAR 2017-18

22
It is clear that during months of exams (mid-term and terminal), demand of library
books increase and student borrow more books for study.
The decline is observed in start of semester as students rely on their lectures and notes.
Also during decline in summer semester is observed as many students go to internship
and some have break and others have subjects to study for improvement.
There is +ve trend in borrowing books and regression id less than zero.
Forecast for Nov. 2018 is calculated using formula of excel sheet, which shows approx.
153 books will be borrowed in this month.
This forecast help library staff to be pro-active during the time students needs more
books.
6. CONCLUSION
Time series data is used in some areas of library statistics. Mostly figures are collected to show
the yearly growth in circulation of books, new collection added and number of books and
research materials in specific subject area. There is no regular process of data collection except
maintaining some facts and figures.
7. RECOMMENDATIONS
There should be well developed MIS for Library to maintain all statistics and generate
report regularly.
Time-series data of circulation and collection development should be displayed on
library display board to reflect library usage and progress and also to encourage other
members to use library resources.
8. REFERENCES
Brillinger, D. R. (2000).Time Series: General. General international encyclopedia of the social
& behavioral sciences, (pp. 15724-15730).
Fawumi, K. (2015). Design of an interactive and web-based software for the management,
analysis and transformation of time series (Master's Thesis in Informatik). Munchin,
Germany: Der Technische Universität München.
Gay, L. R., Mills, G. E. and Airasian, P. W. (2012).Educational research: competencies for
analysis and applications, 10th
ed. Boston: Pearson.
Imdad Ullah, M. (2014). Component of time series data (Lecture notes, MCQS of Statistics).
Retrieved from http://itfeature.com/time-series-analysis-and-forecasting/component-of-
time-series-data
Jupp, V. (Ed. & Comp.). (2006). The Sage dictionary of social research methods. London: Sage
Publications Ltd.
Kothari, C. R. (2004). Research methodology: Methods and techniques. New Delhi: New Age
International.
Lavrakas, P. J. (Ed.). (2008). Encyclopedia of survey research methods. California: SAGE
Publications, Inc.

23
Liu, C. (2008). Cross-Sectional Data. In P. J. Lavrakas (ed.), Encyclopedia of survey research
methods. California: Sage Publications.
Neuman, W. L. (2013). Social research methods: Qualitative and quantitative approaches, 7th
ed. Harlow: Pearson education.
Preston University. (2018). Retrieved from http://preston.edu.pk/
Preve, D. (2008). Essays on time series analysis: with applications to financial
econometrics (Doctoral dissertation, Acta Universitatis Upsaliensis). Retrieved from
https://www.diva-portal.org/smash/get/diva2:171806/FULLTEXT01.pdf
Quantitative Methods. Analystprep.com. Retrieved from https:// /cfa-level-1-exam/quantitative-
methods/time-series-data-vs-cross-sectional-data/
Render, B., Stair, R. M. and Hanna, M. E. (2012). Quantitative analysis for management, 11th
ed. Boston: Pearson Education.
Senter, A. (n.d.). Time series analysis. Retrieved from
http://userwww.sfsu.edu/efc/classes/biol710/timeseries/timeseries1.htm
What is Time Series Analysis? Retrieved from http://www.grroups.com

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