LECTURE 1: The Role‚ Collection and Presentation of Quantitative Data in the Business Decision Making Process 1. What is data Data is important for many business decisions. Data can be qualitative or quantitative. Data that is specifically collected first-hand for the project is called primary data and data that was collected for a different project and used in this project is called secondary data. It is always important when collecting data to ensure that the sample collected is random and representative
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Types of Variables Binary variable Obsevations (i.e.‚ dependent variables) that occur in one of two possible states‚ often labelled zero and one. E.g.‚ “improved/not improved” and “completed task/failed to complete task.” Usually an independent or predictor variable that contains values indicating membership in one of several possible categories. E.g.‚ gender (male or female)‚ marital status (married‚ single‚ divorced‚ widowed). The categories are often assigned numerical values used as lables
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Quantitative research involves collecting numerical data though content analysis‚ performance tests‚ personality measures‚ questionnaires‚ etc. Quantitative research allows for experimental and non-experimental research. Experimental research tests cause and effect‚ while non-experimental research‚ such as‚ correlation research‚ relates variables‚ and survey research describes trends. There are three basic types of quantitative research questions: descriptive research questions‚ correlational research
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Statistics is the study of the collection‚ organization‚ analysis‚ interpretation and presentation of data. It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments. Descriptive statistics is the discipline of quantitatively describing the main features of a collection of information‚ or the quantitative description itself. Descriptive statistics are distinguished from inferential statistics (or inductive statistics)‚ in that
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establish the appropriate level of measurement for variables being measured in a study‚ because level of measurement impacts…? A. How long the study will run B. How often measurements should be made C. The number of participants that must be recruited D. The assumptions underlying the quantitative methods of analysis chosen 2) The principle of “parsimony” in research may be reflected in measurement efforts by…? A. Selecting long and complex measurement strategies to ensure full coverage
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Productivity Measurement at International‚ National and organization level. PRODUCTIVITY MEASUREMENT Productivity measurement is the quantification of both the output and input resources of a productive system. The goal of productivity measurement is productivity improvement‚ which involves a combination of increased effectiveness and a better use of available resources. While productivity can be given the sort of short hand definition as the ratio between output and input USE OF PRODUCTIVITY
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Introduction Wrong or inaccurate measurements can lead to wrong decisions‚ which can have serious consequences‚ costing money and even lives. The human and financial consequences of wrong decisions based on poor measurement being taken in matters as important as environmental change and pollution are almost incalculable. It is important therefore to have reliable and accurate measurements which are agreed and accepted by the relevant authorities worldwide. Metrologists are therefore continuously
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affects hematocrit values. Hematocrit levels rise with an increase in the number of RBC’s or a decrease in the plasma volume. An elevated hematocrit is most often associated with severe burns‚ diarrhea‚ and dehydration‚ where water in tissues decreases. The low level of plasma water concentrates the RBC’s leading to the condition‚ hemoconcentration. An elevated hematocrit may also be caused by an absolute increase in blood cells‚ called polycythemia. Decreased levels can be due to conditions including
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Theory The first experiment focused on the concept of errors and uncertainties that are obtained during measurements. For an experiment to be successful‚ especially those that involve measurements‚ the number of significant figures must be known. Significant figures are the digits required to express a measured quantity and thus reflect the accuracy of the measurement. Uncertainty is defined as the smallest increment that can be measured and is defined by the instrument used. An error is defined
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Analytical measurement: measurement uncertainty and statistics Ricardo Bettencourt da Silva‚ Ewa Bulska‚ Beata Godlewska-Żyłkiewicz‚ Martina Hedrich‚ Nineta Majcen‚ Bertil Magnusson‚ Snježana Marinčić‚ Ioannis Papadakis‚ Marina Patriarca‚ Emilia Vassileva‚ Philip Taylor Editors: Nineta Majcen‚ Vaidotas Gegevičius Joint Research Centre Analytical measurement: measurement uncertainty and statistics Editors: Nineta Majcen Vaidotas Gegevičius Authors: Ricardo Bettencourt da
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