02 Guidelines for Module 1.docx

1. **Introduction** The training titled \"Statistical Literacy and Data Analysis Methods.\" This training is supported by the World Bank project named the Tax Administration and Statistical System Modernization, Kyrgyz Republic. The aim of this extensive project is to modernize and transform the...

1. **Introduction** The training titled \"Statistical Literacy and Data Analysis Methods.\" This training is supported by the World Bank project named the Tax Administration and Statistical System Modernization, Kyrgyz Republic. The aim of this extensive project is to modernize and transform the national statistical system of Kyrgyzstan. A significant goal of this initiative is to enhance Kyrgyzstan\'s expertise, enabling it to become an active partner in international statistical development cooperation and a center of excellence in statistics. To achieve this, we have planned numerous activities to build the capacities and skills of employees at the National Statistics Committee (NSC) and other institutions within the national statistical system of Kyrgyzstan. However, for the long-term development of statistics, it is crucial to spread statistical knowledge across all segments of society: government institutions as policy makers, scientific research institutions, businesses, and more. The NSC, along with the National Bank and other national and international bodies, produces and disseminates the statistics needed by these groups. One key focus of this project is the education sector, particularly students. As part of our initiative, we plan to introduce a course on Statistical Literacy and Data Analysis and develop a master\'s program. This will integrate Kyrgyz universities into a global network of institutions that are advancing statistical education. The purpose of these guidelines is to ensure a standardized and high-quality approach to teaching statistical literacy, fostering a deeper understanding of statistical concepts and their practical applications. The intended outcomes include enhancing trainees\' abilities to critically evaluate statistical information, understand data collection and analysis processes, and communicate statistical findings clearly and accurately. 2. **Overview of Module 1** Module 1 is divided into five essential topics: 1. **The Importance of Statistical Literacy (**explore why understanding statistics is vital, especially for countries aiming to implement data-driven policies) 2. **Understanding Official Statistics (**define what official statistics are. Every country has a system of official statistics, which is the largest public source of organized data. This data is available for free, making it essential for policymakers, researchers, journalists, and other users to understand how to access and use it) 3. **The Role of Official Statistics in Decision Making (**delve into how official statistics support the decision-making process and cover the importance of these statistics for different societal segments, such as government institutions, academic researchers, and businesses. 4. **Basic Concepts of Data Analysis (**once you obtain data from the NSC or other sources, the next step is to analyze it) 5. **Key Sources of Official Statistics (**identify and discuss the primary sources of official statistics, not only in Kyrgyzstan but globally. How to access and utilize these sources for various purposes.) 6. **Output of the First Module** By the end of the first module, you will achieve the following: - **Increased Data Awareness** - Develop a heightened awareness of the importance and value of data in various contexts. - **Improved Understanding of Official Statistics and Quality** - Gain a comprehensive understanding of the concept of official statistics and the standards of quality that underpin them. - **Recognition of the Need for Data-Driven Decisions** - Understand the critical need to transition towards making decisions based on data analysis and insights. 7. **Data-driven decision-making (DDDM) and Data Driven Governance** [\'Without data, you're just another person with an opinion\', W. Edwards Deming] Good policy/decision making requires good data and good data analysis. The capacity to collect, process and draw relevant conclusions from different sources data is vital to developing effective policies, and vital to the all-important feedback loop between implementation, monitoring and evaluation, and policy adjustment and review. Two key concepts highlighting the importance of data in decision-making are Data-Driven Decision-Making (DDDM) and Data-Driven Governance**.** 1. **Data-driven decision-making (DDDM)** is defined as using facts, metrics, and data to guide strategic business decisions that align with your goals, objectives, and initiatives; 2. **Data-driven governance** is defined as the practice of using data, analytics, and evidence to inform and guide decision-making processes within government institutions. Rather than relying solely on intuition or anecdotal evidence, data-driven governance emphasizes the use of empirical data to understand complex problems, evaluate policy options, and measure the effectiveness of government programs and initiatives. When government or organizations fully realize the value of data, everyone---from business analysts and sales managers to human resource specialists---is empowered to make better decisions with data daily. However, achieving this requires more than just selecting the right data source or analytics technology. The amount of information collected has never been greater, but it's also more complex. This makes it difficult for organizations to manage and analyze their data. For it we need educated specialists. Building core data capabilities within all levels of an organization fosters a culture of data-driven decision-making. When we are speaking about Government, traditionally government decision-making relied heavily on intuition, past experiences, and limited data sources. However, with the advent of technology and the proliferation of data, there has been a paradigm shift towards evidence-based policymaking. It that process usually the National Statistical Offices takes the main role in promoting and increasing the statistical literacy. ***Debate**: Organize a debate on the statement: "[\'Without data, you're just another person with an opinion\', W. Edwards Deming]* ***Activity**: Begin with a quick poll or interactive activity where students guess certain statistics about their country (e.g., population size, GDP, unemployment rate). Discuss the guesses briefly. Discuss economic indicators like GDP, inflation rates, and employment statistics and their impact on economic policies.* ***Additional resources*** **Books**: \"The Signal and the Noise\" by Nate Silver, \"Data-Driven Policy Making\" by OECD. **Websites**: UN Statistics Division, World Bank Open Data, National statistical offices\' websites. 8. **Why is it important to increase statistical literacy?** Statistical literacy is an essential skill in our data-driven world. It empowers individuals to make informed decisions, critically evaluate information, and understand the numbers that surround us daily. In today's data-driven world, the ability to analyze, understand, and leverage data is more valuable than ever before. Each bigger organizations or private company establishing own data analyst department to make better decisions and plan by interpreting complex data, predicting trends, and providing insights that will guide operational and strategic decisions. The increasing the statistical literacy influences on: **Understanding the Basics -** Statistical literacy begins with grasping fundamental concepts such as data, variables, and distributions. For instance, consider a study on the average income of a population. Statistical literacy allows you to comprehend how data was collected, what variables were considered (e.g., age, occupation), and how the income is distributed across the population **Critical Evaluation of Information** - one of the most crucial aspects of statistical literacy is the ability to critically assess information presented in charts, graphs, and reports. Misleading statistics are prevalent in media, politics, and advertising. Being statistically literate enables you to spot errors, manipulation, or bias in data presentations. For example, a chart might use a misleading scale to exaggerate a small change in data, or a news headline might highlight a statistic out of context to create a false impression. Statistical literacy equips you with the tools to question such presentations and dig deeper for the truth. **Career Advancement -** in today's job market, statistical literacy is a highly valuable skill. Many professions, including data analysis, marketing, finance, and research, require a deep understanding of statistics. Job seekers with strong statistical literacy are better equipped to excel in these roles and advance their career. The \"international lists,\" suggests that careers in statistics and data analysis are highly valued and financially rewarding in the current job market: - **Forebs list (4th place STATISTICIAN) -** Statisticians typically analyze numerical data to identify trends, patterns, and relationships. They play a crucial role in designing experiments, collecting data, and interpreting results in various fields such as business, healthcare, finance, and government - **World Economic Forum (7th place DATA DETECTIVE) -** The term \"data detective\" may refer to professionals who specialize in uncovering insights from large datasets using advanced analytical techniques. They are skilled in data visualization, machine learning, and statistical analysis, and they play a key role in extracting actionable insights from data to inform decision-making. - **Indeed (1st place DATA ENGINEER) *-*** Data engineers are responsible for designing, constructing, and maintaining the systems and architecture necessary for data generation, processing, and storage. They often work closely with data scientists and analysts to ensure that data pipelines are efficient and reliable. Thus, Statistical literacy is a vital skill in our modern world. It empowers individuals to make informed decisions, evaluate information critically, and participate actively in democratic processes. Whether in personal decision-making, career advancement, or civic engagement, statistical literacy plays a crucial role in shaping a more rational and informed society. It is an investment in individual empowerment and collective progress**.** The role of a statistician is more crucial than ever before. Statistics serve as the backbone of evidence-based decision-making across various sectors, ranging from public policy and healthcare to finance and technology. - **Data Interpretation Exercise**: Provide students with a dataset or a set of graphs and have them work in small groups to interpret the data and present their findings. This can be followed by a discussion on the different interpretations and common pitfalls in data analysis. - **Role-Playing**: Organize a role-playing activity where students take on different roles (e.g., policymaker, business owner, healthcare professional) and must use statistical data to make a decision) - **Discuss common challenges** in achieving statistical literacy such as math anxiety, lack of access to quality education, and the complexity of statistical concepts. ***Additional resources*** **Books**: A guide to improving statistical literacy, UNECE **Websites**: Statistical Literacy Initiatives Inventory, 9. **The role of Statistic in Data Driven Governance** We have seen that statistics have a wide use and role. We will observe it in the context of data driven governance. In a data-driven governance approach, governments collect, analyze, and interpret various types of data, including demographic information, economic indicators, social trends, and performance metrics. This data is then used to identify patterns, trends, and correlations that can inform policy formulation, resource allocation, and service delivery. Statistics can be used to inform decision making throughout the different stages of the policy-making process. The following framework has been adapted from different approaches to the policy making cycle and highlights the importance of using statistical information at each of the stages of the policy cycle. **Stage 1: Identify and understand the issue** The first phase involves identifying and understanding the issue at hand. Statistics can assist policy makers to identify existing economic, social or environmental issues that need addressing. For example, statistical analysis could identify issues concerning the aging of the population or the implications of rising inflation. They are also vital for developing a better understanding of the issue by analysing trends over time, or patterns in the data. **Stage 2: Data collection** Statistics provide a valuable source of evidence to support the initiation of new policy or the alteration of an existing policy or program. Once an issue has been identified, it is then necessary to analyse the extent of the issue, and determine what urgency there is for the issue to be addressed. Statistics can highlight the relevance and severity of the issue in numerical terms, and thus demonstrate the importance of developing policy or programs to address the issue as quickly as possible. **Stage 3: Data analyses methods** Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. With the help of various techniques such as statistical analysis, regressions, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Policymakers rely on analytics processes and tools to extract insights that support strategic and operational decision-making. **Stage 4: Formulate policy** Once an issue has been identified and recognised as an important policy issue, it is then necessary to determine the best way to respond. This stage requires careful and rigorous statistical analysis and thorough consultation with key stake-holders to establish a clear understanding of the true extent of the problem. This will help to determine the most appropriate policy or program options to address the issue, and the best strategy for implementing these. During this stage, clearly defined aims and goals should be developed with quantifiable indicators for measuring success. Benchmarks should also be established to ensure that progress is measurable following the implementation of the policy/program. **Stage 5: Monitor and evaluate policy** The policy process does not end once the policy/program is up and running. It is essential that the progress of a policy/program is regularly monitored and evaluated to ensure it is effective. An evaluation of the success of the policy/ program in quantifiable terms can be measured against benchmarks which were established at an earlier stage to accurately measure progress. This enables an assessment to be made as to whether the policy is meeting initial aims and objectives, as well as providing insight and identification of areas that require improvement. The process should then be repeated, by beginning the cycle again Overall, data-driven governance enables governments to make more informed, effective, and responsive decisions that address the needs and priorities of the population. It helps governments optimize resource allocation, enhance service delivery, and achieve better outcomes for citizens and communities. **Group Discussion**: Divide students into small groups and assign each group a sector (health, education, economy, environment). Have them discuss how official statistics are used in their assigned sector and present their findings. 10. **Data Driven Governance and data needs** Before actively looking at data sources it is first necessary to define the data need for the process that you are performing (for example for policy making, research, for business improvement). Being able to specify what it is you are trying to find out or what you are hoping to achieve from the outset, will help ensure you get the data you require to make well informed decisions. The following questions can be useful in helping you define your data need: 1. what is the topic or subject area you are interested in? 2. who or what is your key population? (be specific about age, sex or geographical specifications) 3. what are you trying to find out about this issue or group? 4. are you interested in information relating to a specific time frame? Once a data need has been identified, it is then necessary to develop a better understanding\ of the kind of data required. There are many sources of data available, but careful consideration should be given to choosing the right data for the intended purpose. Choosing a data set that is not appropriate, can lead to inaccurate conclusions being drawn. That is why it is important to distinguish between official statistics and statistics in general. **Reflection Paper**: Ask students to write a reflection on the potential consequences of a lack of reliable official statistics in governance. 11. **Data Driven Governance based on Official statistics** The term 'statistics' is used differently; it can refer to a science, a certain kind of information (data) or institutions. Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Statistics is a highly interdisciplinary field; research in statistics finds applicability in virtually all scientific fields and research questions in the various scientific fields motivate the development of new statistical methods and theory. Essentially, statistics is the science of learning from data. When we talk about some kind of information or data, it refers to official statistics or statistics in general. What\'s the difference? While this course defines statistics and official statistics as shown on the slide, various definitions exist for statistics and official statistics. Most definitions of official statistics are conceptually similar to this, but there is no international consensus on exactly how to define the term. Generally, the term \"statistics\" means quantitative and qualitative, aggregated and representative information characterizing a collective phenomenon in a considered population. Statistics are numerical data but not a group of individual data. A significant factor of statistics is to find characteristics from the group as the aggregate of data. "Official" is used here as having state recognition. **Illustrate** how governments use official statistics to make evidence-based decisions (e.g., public health strategies, economic policies). **Final thoughts** - emphasize the importance of supporting robust statistical systems and the ongoing need for innovation and improvement in the field of official statistics. **Book (2020):** Official Statistics 4.0 Facts for People in the 21. Century, Walter Radermacher 12. **Definition of Official statistics** The UN Fundamental Principles of Official Statistics describe official statistics as providing an indispensable element in the information system of a democratic society, serving the Government, the economy and the public with data about the economic, demographic, social and environmental situation. According to the Principles, official statistics are produced by government agencies and can inform debate and decision-making both by governments and by the wider community. **According to the Statistical office of the European Union (EUROSTAT) official statistics** are statistics produced within a national statistical system based o**n the legal framework that regulates official statistics as an production activity**, and in accordance with **Fundamental principles** which ensure minimum professional standards, such as independency, accuracy and objectivity. National statistical systems include statistical organisations and units within a country that jointly collect, process and disseminate official statistics on behalf of the national government. Some national statistical offices explicitly state what are considered official statistics in their countries. For example for the European Union, the legal framework is based on the European regulation (EC) No 223/2009 and the set of principles for official statistics and its production and quality. The Statistics Act of the Kyrgyz Republic defines official: 3\. The provisions of this Law apply to any data in the possession of producers of official statistics used for development, production and dissemination of official statistics. **Book (2020):** Official Statistics 4.0 Facts for People in the 21. Century, Walter Radermacher **What are not official statistics?** ===================================== **Non-official statistics** or "statistics in general" include statistics collected and published by other bodies (private companies, scientific and research institutions, the media, as well as government bodies at the national and local level in the exercise of their competence). For example, the Ministry of Internal Affairs can publish data on the number of inhabitants, but if the same data is published by the National Statistical Institute, then it is official statistics. *Why? Discussion?* Instead of a discussion, let\'s look at an example together ( *Ministry of Health to examine the effects of ambient air pollution on public health with a particular focus on lung cancer and vulnerable group as children and elderly*). This video is an animation developed by UN Statistics Division that presents a short story explaining how to distinguish official statistics from all other data In an approximation based on this video, official statistics can be defined by using three questions: Who? Normally, official statistics are produced and provided by statistical offices, i.e. public administrations under specific Law What? Statistical work programmes and priorities are prepared according to public sector standards - the list of products (data) How? Statistical methodologies are nowadays subject of international cooperation and manifested in statistical standards; high-level quality is assured through management systems and ethical codes. Being official naturally implies that the statistical data respond to a collective need and are fit for purpose, satisfying as far as possible explicitly agreed upon quality standards for statistical production processes and outputs. Statistical institutions are the producers of statistics. Using scientific statistical methods, data is collected and existing data is processed in order to calculate condensed information, which is made available to the general public in different forms, such as statistical aggregates, graphics, maps, accounts or indicators. Statistical offices usually belong to the public administration, at state, international, regional or local level. On the other hand, there are some critical phrases about statistics as well. "There are three kinds of lies: lies, damned lies, and statistics" (Benjamin Disraeli). This phrase suggests that statistics can be the main source of misunderstanding. Government officials should realize that statistics entail such risks, and should create, compile, analyze and disseminate statistics in keeping with quality and have good communication with stakeholders. However, bearing in mind that official statistics are based on the law and clear rules because it is a public good, it seems to be of higher quality than other statistics in general. Certainly, it depends from country to country and the level of development of the national statistical system and public trust. In order for some information or data to be official statistics, it must be based on: 1\. Law on Official Statistics 2\. The fundamental principles of official statistics, which at least mean accuracy, objectivity and reliability 3\. International methodology defined by UN recommendations such as the SNA 2008 methodology **Exercise:** make a list of institutions in the Kyrgyz Republic that are official producers and which are not. **14. The quality of available data** Now that we know how to differentiate between data sources, the next question is the question of data quality. How do we know which data sources are reliable and which are not? How do we know which data sources are reliable and which are not? **Activity**: Start with a quick poll or discussion about experiences where students encountered poor quality data (e.g., misleading statistics in news articles, errors in datasets) There are some characteristics of official statistics that are different from private statistics. **Official statistics are impartial and free from political or commercial influence.** Statistical legislation gives official statisticians guaranteed professional independence, thus ensuring objective and unbiased information. Methods and procedures for collection, compilation and dissemination of statistical data are based solely on professional considerations, ethics and scientific principles, as well as internationally agreed concepts and methods. This is a unique feature of official statistics. They are of best professional quality. Professional peer pressure and review acts as a strong mechanism to maintain and improve the quality of official statistics, so they come with this assurance. **The provision of uniquely comprehensive information that is consistent over time is another characteristic of official statistics**. Non-official producers of statistics generally act in accordance with their own needs and circumstances. This means they often have little or no incentive to maintain statistics that are produced and consistent over long periods of time. Furthermore, official statistics generally cover topics, regions, types of activities and other groupings that are essential to our societies but for which non-official producers of statistics may have no incentive to operate. Examples include statistics on economic development, construction, employment, prices, human capital, housing, health, wellbeing, agricultural supply and demand, business performance, international trade, and other similar topics. Statistics needed for public policy and service delivery, measuring national progress, legislative requirements and international reporting obligations are the top priorities. Without official statistics these needs would be largely unmet. **Assured equal access to official statistics is a fundamental principle of official statistics to honor people's right to information and secure equal access to statistics for everyone**. By contrast, non-official providers of statistics and information may often have a commercial or other incentive structure which means they will not want to share all statistics that they compile. In the absence of official statistics, this would lead to seriously suboptimal economic and social outcomes. **Official statisticians are trusted guardians of data and con**fidentiality. Statistical offices have a uniquely strong legal setting for ensuring strict confidentiality of individual data, as well as a reputation built up over many decades. Individual data are not given to any other authorities and cannot be used for any other purposes than statistics and selected scientific research projects. Consequently, businesses and households are prepared to provide information to official statisticians that they would not be prepared to give to other statistical providers. **Data cleaning exercise**: Provide a messy dataset and ask students to perform basic data cleaning tasks, identifying and correcting errors. **15. The Main Principles of Official Statistics** Data quality varies from one source to the next due to a wide range of factors. Quality is generally accepted as "fitness for purpose" and this implies an assessment of an output, with specific reference to its intended objectives or aims. When making an assessment of the fitness of data, it is important to keep in mind where the data has come from, how and why it was collected, and whether the data is of suitable quality for your requirements. UN *Fundamental Principles* can assist in evaluating the quality of statistical collections and products (e.g. survey data, statistical tables and administrative data). It is comprised of seven dimensions of quality which are: (1) Professional independence, (2) Coherence and comparability, (3) Impartiality and objectivity, (4) Clarity and transparency, (5) Accuracy and reliability, (6) Statistical confidentiality and exclusive use for statistical purpose and (7) Relevance. All seven dimensions should be used in quality assessment and reporting. However, the importance of each dimension may vary depending on the data source and the requirement of the user. All producers of official statistics develop, produce and disseminate official statistics according to the following main principles of official statistics and other agreed statistical principles: 1. **Professional independence** means that producers of official statistics decide, independently and free from any pressures or interference from political or other external sources, on the development, production and dissemination of statistics, including the selection of data sources, concepts, definitions, methods and classifications to be used, and the timing and content of all forms of dissemination. Producers of official statistics, in their respective areas of competence, may comment publicly on statistical issues and any misuse of official statistics; 2. **Impartiality and objectivity** mean that official statistics must be developed, produced and disseminated in a neutral, reliable and unbiased manner according to professional standards and free from any political statements or considerations. All users must be given equal and simultaneous access to official statistics; 3. **Accuracy and reliability** mean that official statistics must reflect as faithfully, accurately and consistently as possible the reality and be based on scientific criteria used for the selection of sources, methods and procedures; 4. **Coherence and comparability** mean that statistics are consistent internationally and comparable over time and across regions and countries; 5. **Clarity and transparency** mean that official statistics must be presented in a clear and understandable way, and the methods and procedures applied must be transparently communicated to users to facilitate proper interpretation; 6. **Statistical confidentiality** and exclusive use for statistical purposes means that individual data collected or obtained by producers of official statistics that refer to natural or legal persons are to be strictly confidential and used exclusively for statistical purposes; 7. **Relevance** means the degree to which official statistics meet current and emerging user needs and honor citizens' right to public information. **Group Exercise**: Divide students into small groups and provide each group with a dataset. Have them assess the quality of the data based on the dimensions discussed and present their findings. **Homework**: Ask students to select a dataset relevant to their field of interest and write a report assessing its quality based on the dimensions discussed. **16. DATA ANALYSIS** The 'gold' that data scientists 'mine' comes in two distinctive types: *qualitative* and *quantitative*, and both are critical to making a data driven decision. **Qualitative data** refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. **Qualitative data analysis** can be divided into the following five categories: **1. Content analysis**. This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data; **2. Narrative analysis**. This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher. **3. Discourse analysis**. A method of analysis of naturally occurring talk and all types of written text. **4. Framework analysis**. This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation. **5.Grounded theory -** this method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory. ***Quantitative analysis*** focuses on numbers and statistics. The median, standard deviation, and other descriptive stats are pivotal here. This type of analysis is measured rather than observed. Both qualitative and quantitative data should be analyzed to achieve smarter business decisions. **17. DATA ANALYSIS METHODS** Data analysis involves various methods used to examine, clean, transform, and interpret data to derive meaningful insights and make informed decisions. Here are some common data analysis methods: **Descriptive Statistics -** descriptive statistics summarize and describe the main features of a dataset. Measures such as mean, median, mode, standard deviation, and range help understand the basic characteristics of the data. **Inferential Statistics -** inferential statistics help in making predictions, testing hypotheses, and drawing conclusions about a population based on a sample. Techniques include t-tests, chi-square tests, ANOVA (analysis of variance), regression analysis etc. **Correlation -** statistical measure that describes the relationship between two variables. It helps in understanding how changes in one variable might be associated with changes in another variable. It is a numerical measure that quantifies the strength and direction of the relationship between two variables. The value of the correlation coefficient ranges between -1 to 1. A correlation coefficient of 1 indicates a perfect positive correlation, meaning as one variable increases, the other variable also increases in a linear fashion. A correlation coefficient of -1 indicates a perfect negative correlation, where one variable increases while the other decreases in a linear fashion. A correlation coefficient of 0 suggests no linear relationship between the variables. However, it\'s essential to note that absence of linear correlation doesn\'t imply the absence of other types of relationships. **Regression analysis** - regression uses historical data to understand how a dependent variable\'s value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable\'s relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future. **Time Series Analysis**: As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. Forecasting techniques such as ARIMA (Auto Regressive Integrated Moving Average) and exponential smoothing are used in time series analysis. **Factor Analysis** Factor analysis is a statistical method used to identify underlying relationships among a set of observed variables and to uncover the latent factors that explain patterns of correlations among these variables. It aims to reduce the dimensionality of the data by capturing the shared variance among variables and explaining it in terms of a smaller number of latent factors. Factor analysis seeks to identify the underlying structure or dimensions that best explain the relationships between observed variables. It assumes that observed variables are influenced by a smaller number of unobservable (latent) variables or factors. **Cluster analysis** is a statistical method used in data analysis to identify inherent groupings or clusters within a dataset. It\'s an unsupervised learning technique that organizes objects (such as data points or observations) into groups based on similarities in their characteristics or attributes. The similarity is based on certain metrics or distance measures. There are many other methods of data analysis, but for the purposes of this course we will be looking specifically at the first two groups of methods. We will cover them in Modules 2, 3 and 4. **18. Data sources -- national statistical system** The National Statistical Committee of the Kyrgyz Republic (NSC) is the primary institution responsible for the collection, analysis, and dissemination of official statistics in Kyrgyzstan. Here\'s an overview of its structure and functions: **1. Role and functions** - **Data collection:** The NSC is responsible for collecting data on various socio-economic aspects, including demographics, economic performance, agriculture, industry, environment, and social indicators. - **Data analysis and dissemination:** After collecting data, the NSC analyzes and processes it to generate statistical reports, publications, and databases that are made available to the public, government institutions, businesses, and international organizations. - **Quality assurance:** Ensuring the quality and reliability of statistical data is a key function, adhering to international standards and methodologies. - **Policy support:** Providing data and analysis to support evidence-based policy-making and monitoring the effectiveness of implemented policies. **2.** **Structure:** - **Central office:** Located in Bishkek, the central office oversees the coordination and management of all statistical activities. - **Regional offices:** The NSC has several regional offices across the country that handle local data collection and processing. - **Specialized departments:** These departments focus on specific areas such as economic statistics, social statistics, population statistics, and environmental statistics. **3. Key initiatives and projects:** - **Modernization projects:** With the support of international organizations like the World Bank, the NSC is involved in modernization projects aimed at improving the statistical infrastructure, adopting new technologies, and enhancing the skills of its staff. The National Statistical Committee of the Kyrgyz Republic (NSC) covers a wide range of statistical areas, ensuring comprehensive data collection and analysis across various sectors. Here are the primary statistical areas covered by the NSC: Demographic and Social Statistics; Economic Statistics; Environmental Statistics; Population Census; Agricultural Census; Business Statistics; Transport and Communication; Regional Statistics; Household Surveys. Science, Technology, and Innovation. For this part, use the online course entitled \"Introduction to the System of Official Statistics of the Kyrgyz Republic\". Part 1 and Part 2. [LINK](https://stat.gov.kg/ru/rmc/institut/?_gl=1*12gx2hl*_ga*NDY3OTI0OTUxLjE3MjA0MTg2MzU.*_ga_ENLBH6NGYM*MTcyMDQxODYzNC4xLjEuMTcyMDQxODY4NC4wLjAuMA..) **18. Data sources -- international statistical system** The international statistical system refers to the collective framework and collaboration among international organizations, national statistical offices (NSOs), and other entities involved in producing and disseminating statistical information at a global level. This system plays a crucial role in providing consistent, comparable, and reliable data across countries and regions, which is essential for global governance, policymaking, development planning, and research. Here are key components and aspects of the international statistical system: 1. **International Organizations** - Organizations such as the United Nations (UN), its specialized agencies (e.g., UNICEF, WHO, ILO), the World Bank, the International Monetary Fund (IMF), the Organisation for Economic Co-operation and Development (OECD), and regional bodies (e.g., Eurostat for Europe) play central roles. They coordinate statistical activities, set standards, and facilitate data collection, analysis, and dissemination globally. 2. **Data Collection and Compilation** - International statistical organizations collect data from member countries on various topics including population, economic indicators (e.g., GDP, inflation), social indicators (e.g., education, health), environment, trade, and more. This data is compiled into databases and reports that provide a global perspective. 3. **International Statistical Standards -** Standardization ensures that statistical data are comparable across countries. The UN Statistical Commission (UNSC) and other bodies develop and promote international statistical standards, classifications (e.g., International Standard Industrial Classification of All Economic Activities - ISIC), and methodologies (e.g., System of National Accounts - SNA). 4. **Data Dissemination** - International statistical organizations disseminate data through publications, databases (e.g., World Development Indicators by World Bank, UNdata by UN), and online platforms. This ensures accessibility and transparency of statistical information for policymakers, researchers, businesses, and the public. 5. **Capacity Building and Technical Assistance** - International organizations provide technical assistance, training, and capacity-building programs to NSOs and developing countries to strengthen their statistical systems. This includes improving data collection methods, enhancing analytical capabilities, and adopting international standards. 6. **Monitoring Global Trends and SDGs**: The international statistical system monitors progress towards global development goals, such as the Sustainable Development Goals (SDGs). Indicators are developed and monitored to assess global trends and inform policies aimed at achieving sustainable development. 7. **Data Ethics and Privacy**: International statistical standards emphasize data confidentiality, ethics, and privacy protection. Guidelines are established to ensure that statistical data are used responsibly and respect individuals\' privacy rights. Overall, the international statistical system plays a critical role in providing evidence-based information for informed decision-making at global, regional, and national levels. By promoting data quality, comparability, and transparency, it supports efforts to address global challenges and promote sustainable development worldwide. For this part, use the online course entitled \"Introduction to the System of Official Statistics of the Kyrgyz Republic\". Part 6 [LINK](https://stat.gov.kg/ru/rmc/institut/?_gl=1*12gx2hl*_ga*NDY3OTI0OTUxLjE3MjA0MTg2MzU.*_ga_ENLBH6NGYM*MTcyMDQxODYzNC4xLjEuMTcyMDQxODY4NC4wLjAuMA..)