Pooled time-series là gì and cross sectional structure năm 2024

1. 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
2. CROSS SECTIONAL ANALYSIS 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
3. CROSS SECTIONAL ANALYSIS 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
4. 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
5. 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
6. 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.
7. 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)
8. Demand Charted over 4 Year, withTrend and Seasionality Indicated Figure 5.3 Compnents of Time Series
9. 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
10. 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.
11. 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.
12. 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).
13. 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).
14. 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.
15. 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.
16. 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
17. CAMPUS LIBRARY STATISTICAL STUDY
18. 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
19. 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
20. / 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 Newspapers & Magazines/Journals Research Publications & Research Journals
21. Books Weekly, Monthly & Yearly Basis Subject Books Reference Books Newspapers & Magazines/Journals Research Publications & Research Journals 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
22. 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)
23. 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
25. CROSS SECTIONAL ANALYSIS 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.
26. CROSS SECTIONAL ANALYSIS 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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