In this article, I choose the issue of how many dental implants are exported every day to various dental hospitals and private clinics. The goal and scope of this study was to examine the amount of dental equipment that our business has supplied to the hospital as well as the income that we have earned. I use Excel to summarize goods, sales, buyers, and the dates on which we deliver our products. When we sell a unit of a commodity, we summarize it in Excel and store it in our database on a daily basis.
ASSIGNMENT 01 FRONT SHEET Qualification BTEC Level HND Diploma in Business Unit number and title Unit 31: Statistics for management Submission date Date received (1st Submission) Re-submission date Date received (2nd Submission) Student Name Student ID Class No Assessor Name Student declaration I certify that the assignment submission is entirely my own work and I fully understand the consequences of plagiarism I understand that making a false declaration is a form of malpractice Student Signature Grading grid P1 P2 M1 Description of activity undertaken Assessment & Grading criteria How the activity meets the requirements of the criteria Student Signature Date: Assessor Signature Date: Assessor name: Summative Feedbacks Grade: Resubmission Feedbacks Assessor Signature: Date: Internal Verifier’s Comments: Signature & Date: Contents I Introduction .6 II Evaluate business and economic data/information obtained from published sources 2.1 Evaluate the nature and process of business and economic data/information from a range of different published sources 2.1.1 Data 2.1.2 Information 2.1.3 Knowledge 2.1.4 The top six data collection methods 2.1.5 Transformation process of data into information and knowledge 10 2.1.6 Data collection process .10 2.2 Evaluate data from a variety of sources using different methods of analysis 11 2.2.1 Descriptive 11 2.2.2 Exploratory 13 2.2.3 Confirmatory .15 III Conclusion .16 IV References 16 I Introduction As a Research Analyst My company is planning to improve information systems and decision-making process by applying some statistical methods More specifically I am required to show my understanding by evaluating and analyzing business data (financial information, stock markets) or microeconomics or near macroeconomic issues Here, future trends / plans, etc related to the research topic All variables can be nominal or order, interval or rate All methods to be explored including information about data, concept of information and knowledge, including converting data and information into knowledge, how data is collected and discovery transformation steps and using descriptive analysis, discovery, and validation techniques II Evaluate business and economic data/information obtained from published sources 2.1 Evaluate the nature and process of business and economic data/information from a range of different published sources 2.1.1 Data Data are units of information, usually numeric in nature, that are gathered by observation In a more technical context, statistics are a set of qualitative or quantitative variables concerning one or more people or things, while a datum (singular of data) is a single value of a single variable Although the words "data" and "information" are often used interchangeably, they have separate meanings When data are interpreted in detail or after post-analysis, they are said to be converted into knowledge in certain common publications Information, on the other hand, are essentially units of knowledge in scholarly treatments of the matter Data are used in science analysis, corporate administration (e.g., market data, turnover, earnings, stock price), economics, government (e.g., crime rates, unemployment rates, literacy rates), and about every other kind of human organizational operation (e.g., censuses of the number of homeless people by non-profit organizations) The map displays information about the employee's decimal architecture in the workplace The raw data ("untreated data") seen above is a list of numbers or characters that have not yet been "cleaned" and corrected by researchers Raw data should be corrected to exclude outliers and apparent data entry or instrumentation mistakes (e.g reading a thermometer from an outdoor arctic location recording tropical temperatures) Data processing is typically done in steps, with "processed data" from one stage serving as "raw data" for the next Most of the above data is collected from employee surveys and assessments of the issue that the company's management strategy in the company has impact on talent management This is a list of only many questions and answers, unsorted to represent many real values The questions are answered by Nominal when it comes to whether to change the strategy is really necessary (the answer is really necessary, not really necessary, and very necessary) The data was categorized based on the evaluation of a company's current management strategy from to as determined by Ordinal Scales, with responses ranging from to For the Percentage variable, questions about% people focus and pay attention on this issue and topic How many people will choose to change in response to the new management strategy and how many will choose not to change Types of qualitative questions about changes in the adoption of new management strategies with impact on employees, quantitative questions is used to measure the number of employees interested in and employee satisfaction on this topic 2.1.2 Information Information may be thought of as the resolution of uncertainty; it addresses the question "What an object is," defining both its meaning and the existence of its characteristics In different ways, the definition of knowledge has different interpretations As a consequence, the term is connected to concepts such as constraint, communication, power, data, shape, schooling, information, meaning, comprehension, mental stimulus, pattern, perception, representation, and entropy Data is correlated with information The distinction is that information eliminates ambiguity Data can represent redundant symbols, but it is closer to knowledge due to optimum data compression 2.1.3 Knowledge Knowledge is the understanding based on extensive experience dealing with information on a subject For example, the height of Mount Everest is generally considered data The height can be measured precisely with an altimeter and entered into a database This data may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to make a decision about the best method to climb it An understanding based on experience climbing mountains that could advise persons on the way to reach Mount Everest's peak may be seen as "knowledge" The practical climbing of Mount Everest's peak based on this knowledge may be seen as "wisdom" In other words, wisdom refers to the practical application of a person's knowledge in those circumstances where good may result Thus wisdom complements and completes the series "data", "information" and "knowledge" of increasingly abstract concepts Data are often assumed to be the least abstract concept, information the next least, and knowledge the most abstract In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guidebook containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge" "Information" bears a diversity of meanings that ranges from everyday usage to technical use This view, however, has also been argued to reverse the way in which data emerges from information, and information from knowledge Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation Beynon-Davies uses the concept of a sign to differentiate between data and information; data are a series of symbols, while information occurs when the symbols are used to refer to something 2.1.4 The top six data collection methods • Observation • Interview • Schedule • • Questionnaire Projective Techniques Case Study Method 2.1.5 Transformation process of data into information and knowledge We are living in the information age! Farmers and their advisors have access to ever increasing amounts of data However, data does not equal knowledge To be effectively used in making decisions, data must go through a transformation process that involves six basic steps: 1) data collection 2) data organization 3) data processing 4) data integration 5) data reporting and finally 6) data utilization Through this process data is transformed into information, which becomes knowledge, if interpreted correctly However, mistakes at any level of the process compromise the entire system, resulting in useless information The Dairy Herd Improvement program is a premiere example of the process Standardized data collection procedures, and the definitions and formulas which organize the data, are the foundation of the program The weak link with DHI data is in the utilization step, as many producers fail to review their reports Applying the process to financial data is needed to supplement the efforts of the Farm Financial Standards Council, which has done an excellent job in developing standards for certain phases of the process Educators and industry professionals in Pennsylvania and the Northeast collaborated to develop a standardized chart of accounts for dairy accounting systems, which is now available as a PDF document or in a QuickBooks backup file Analysis and planning tools that can automatically integrate data from accounting systems using this chart of accounts will enable producers to utilize their accounting and other information systems to make well informed decisions 2.1.6 Data collection process The four stages in the data collection process are as follows: 10 Clarifying data collection priorities- The pre-collection activity reflects consensus on the targets, target data, meanings, and methods The development of organizational concepts and procedures The start of the data collection process Continue to develop the assessment system and ensure that people are adhering to the data collection guidelines- present results, which typically include some sorting, review, or presentation, as defined in Practice Brief: Creating a Data Collection Process It should be noted that it is important to be systematic in collecting data It is important to have a clear plan and goals that will help in the data collection process in the future Different data collection methods can be chosen from the different surveys, questionnaires, interviews, books, journals, internet, etc There are many ways to obtain information nowadays However, it is important to remember that there are many sources that not necessarily have truthful information and help in the research Only trusted sources with valuable information can be helpful in the data collection process, and will lead to the positive outcome Each data collection method has its advantages and disadvantages and it should be noted that one of the most important things are truthful data and being systematic in the data collection process 2.2 Evaluate data from a variety of sources using different methods of analysis 2.2.1 Descriptive Descriptive statistics outline and arrange data collection characteristics A data set is a series of responses or findings from a survey or population as a whole Following data collection in quantitative studies, the first step of statistical analysis is to define features of the responses, such as the average of one variable (e.g., age) or the association between two variables (e.g., age and creativity) Inferential figures are used next to determine whether the evidence supports or refutes your hypothesis and whether it is generalizable to a wider population Descriptive statistics are classified into three types: • The frequency of each value is represented by the distribution • The core bias is concerned with the significance averages • The variability or dispersion of values refers to how evenly distributed the values are 11 The data set for the question of why the kernel management strategy is important is the set of responses to the survey Descriptive statistics can now be used to find out the overall frequency of each activity (distribution), the average for each activity (central tendency), and the prevalence of responses for each activity (the ability to change) Measures of central tendency estimate the center, or average, of a data set The mean, median and mode are ways of finding the average • The mean, or M, is the most commonly used method for finding the average To find the mean, simply add up all response values and divide the sum by the total number of responses The total number of responses or observations is called N • The median is the value that’s exactly in the middle of a data set To find the median, order each response value from the smallest to the biggest Then, the median is the number in the middle If there are two numbers in the middle, find their mean • The mode is the simply the most popular or most frequent response value A data set can have no mode, one mode, or more than one mode To find the mode, order your data set from lowest to highest and find the response that occurs most frequently Advantages of descriptive research • Data collected from descriptive research is helpful in important decision-making because the data is obtained from a large population Because using the descriptive survey method, statistical information can be obtained, and analysis of that data can be made to deduce desired results • A variety of data can be obtained using different descriptive research methods like surveys, observation, and vase study These three research methods provide different type of data 12 which can be used to analysis for a research problem For example, using the case study research method can be used to develop a hypothesis about a research problem • One advantage of descriptive research over other research methods is that it is cheap and quick to conduct descriptive research You don’t require having a great place dedicated only to research Descriptive research like observation research can be held in natural settings, and you can distribute surveys to people online or get them answered by random people at your business place or other public places • Descriptive research provides both quantitative and qualitative data The variety of data provides a holistic understanding of the research problem • Descriptive research can be conducted in natural settings There is no need to have a designated space to conduct research using any of the descriptive research methods Disadvantages of descriptive research • Descriptive methods only provide the answers for “what” and not answer the why and how Therefore, descriptive research methods are not suitable for determining cause and effect relationships • Descriptive methods mainly depend on the responses of people There are chances that people might not act their true selves if they know they are being observed In the case of the survey method, there are chances that some people don’t answer the questions honestly, which makes the output of the descriptive research study invalid Because the results derived from this type of data will not be accurate • Another problem associated with descriptive research is the halo effect A researcher might get partial if he knows the participant personally The observations made in this way would be considered invalid • In descriptive research methods, participants are picked randomly The randomness of the sample can’t represent the whole population accurately 2.2.2 Exploratory Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods It helps determine 13 how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions The main purpose of EDA is to help look at data before making any assumptions It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, find interesting relations among the variables Advantages of Exploratory research • The researcher is adaptable and can react to changes as the research progresses • It is typically inexpensive • It aids in the creation of a research base, which can lead to additional research • It enables the researcher to determine if the subject is worth investing time and money in and pursuing at an early stage • It will help other researchers identify potential causes of the problem, which can then be extensively investigated to determine which of them is the most probable cause of the problem 14 Disadvantages of Exploratory research • Even if it can point you in the right direction for the answer, it is generally inconclusive • The primary drawback of exploratory research is that it produces qualitative results Such facts can be interpreted in a judgmental and biased manner • Since exploratory study typically requires a smaller sample size, the findings cannot be reliably interpreted for a generalized population • Many times, if data is gathered through secondary research, it is likely to be outdated and out of date 2.2.3 Confirmatory Confirmatory research are research that test the validity of already made hypothesis, known as a priori hypothesis This means that possibly some previous studies have been carried out on the subject matter and some results have been presented Checking theories, generating predictions with a given degree of accuracy, regression analysis, and variance analysis are all examples of Confirmatory Data Analysis As a consequence, the confirmatory data analysis is where you put your conclusions and claims to the test Advantages of Confirmatory research 15 Provide precise information in the right circumstances Well-established theory and methods Disadvantages of Confirmatory research • Misleading impression of precision in less than ideal circumstances • • Analysis driven by preconceived ideas Difficult to notice unexpected results III Conclusion Business owners face many situations with outcomes that seem unpredictable For example, your main supplier of a key batch of parts could have a lower cost, but more uncertainty in delivery time Data and statistics can be used to concretely define and measure this uncertainty and predict when the next shipment is coming Managerial decision-making with this statistical insight can avoid steering production, costs and customer service into bad avenues IV References Bhasin, H., 2021 Descriptive Research - Characteristics, Methods, Examples, Advantages [online] Marketing91 Available at: [Accessed April 2021] Ainsworth, Q., 2021 Data Collection Methods [online] JotForm Available at: [Accessed April 2021] Your Article Library 2021 Top Methods of Data Collection - Explained! [online] Available at: [Accessed April 2021] Essay4you.net 2021 What Are the Steps in Data Collection Process? | Essay Writing Blog [online] Available at: [Accessed April 2021] 16 AgRisk.umn.edu 2021 Transforming Data into Knowledge: Defining the Six Steps of Information Management | Conferences Library [online] Available | at: AgRisk [Accessed April 2021] Ibm.com 2021 What is Exploratory Data Analysis? [online] Available at: [Accessed April 2021] Sisense 2021 Exploratory and Confirmatory Analysis: What’s the Difference? l Sisense [online] Available at: [Accessed April 2021] 17 ... process .10 2.2 Evaluate data from a variety of sources using different methods of analysis 11 2.2 .1 Descriptive 11 2.2.2 Exploratory 13 2.2.3 Confirmatory... 2 .1. 3 Knowledge 2 .1. 4 The top six data collection methods 2 .1. 5 Transformation process of data into information and knowledge 10 2 .1. 6 Data collection... published sources 2 .1 Evaluate the nature and process of business and economic data/information from a range of different published sources 2 .1. 1 Data 2 .1. 2 Information