analytics

Examples of analytics in the following topics:

• Analytical Mindset

  • The derivation, interpretation, and expression of patterns within data sets is referred to as analytics.
  • Strong analytical skills are as much a developed competency as they are a perspective.
  • It is a critical role of management to ask the right questions and align employee behavior with analytical thinking.
  • Prescriptive analytics – Using optimization and simulation, managers can produce recommended decisions through analytical modeling.
  • Indeed, utilizing analytics incorrectly can be just as disastrous as not using it at all!

• The Value of Analytics in Decision Making

  • Analytics help decision makers determine risk, weigh outcomes, and quantify costs and benefits associated with decisions.
  • Predictive analytics help decision makers to predict the outcome(s) of a decision before it is implemented.
  • Predictive analytics are particularly useful when there is a high degree of uncertainty.
  • Descriptive analytics answer the questions, "What happened and why did it happen?"
  • Recognize the decision-making value of utilizing statistics and analytics to create accurate predictions

• Redox Titrations

  • Redox titration determines the concentration of an analyte containing either an oxidizing or a reducing agent.
  • A standardized 4 M solution of KMnO4 is titrated against a 100 mL sample of an unknown analyte containing Fe2+.
  • What is the concentration of the analyte?
  • Now that we know the number of moles of iron present in the sample, we can calculate the concentration of the analyte:
  • Calculate the concentration of an unknown analyte by performing a redox titration.

• Integrated Analytics

  • Boundless offers a suite of integrated data and analytics so educators can have greater control of and insight into their classrooms.
  • Boundless offers a suite of integrated data and analytics so educators can have greater control of and insight into their classrooms.

• Modeling Ecosystem Dynamics

  • Conceptual models describe ecosystem structure, while analytical and simulation models use algorithms to predict ecosystem dynamics.
  • In these cases, scientists often use analytical or simulation models.
  • Like analytical models, simulation models use complex algorithms to predict ecosystem dynamics.
  • Simulation models use numerical techniques to solve problems for which analytic solutions are impractical or impossible.
  • Compare and contrast conceptual, analytical, and simulation models of ecosystem dynamics

• Analytical Epidemiology

  • Epidemiologists employ a range of study designs from observational to experimental and generally categorized as descriptive, analytic (aiming to further examine known associations or hypothesized relationships), and experimental (a term often equated with clinical or community trials of treatments and other interventions).
  • Where descriptive epidemiology describes occurrence of disease (or of its determinants) within a population, the analytical epidemiology aims to gain knowledge on the quality and the amount of influence that determinants have on the occurrence of disease.
  • Analytical epidemiology attempts to determine the cause of an outbreak.

• Studying Ecosystem Dynamics

  • Many different models are used to study ecosystem dynamics, including holistic, experimental, conceptual, analytical, and simulation models.
  • Three basic types of ecosystem modeling are routinely used in research and ecosystem management: conceptual models, analytical models, and simulation models.
  • Analytical and simulation models are mathematical methods of describing ecosystems that are capable of predicting the effects of potential environmental changes without direct experimentation, although with limitations in accuracy.
  • An analytical model is created using simple mathematical formulas to predict the effects of environmental disturbances on ecosystem structure and dynamics.
  • Differentiate between conceptual, analytical, and simulation models of ecosystem dynamics, and mesocosm and microcosm research studies

• Descriptive Epidemiology

  • In order to accomplish this, epidemiology has two main branches: descriptive and analytical.
  • Analytical epidemiologists use data gathered by descriptive epidemiology experts to look for patterns suggesting causation.
  • Both descriptive and analytical epidemiology often serve public health organizations by providing information that may reduce disease or reduce other kinds of events that impact people's health.
  • Frequency evaluates the rate of occurrence, and pattern helps analytical epidemiologists suggest risk factors.

• Data and Information

  • Data consists of nothing but facts, which can be manipulated to make it useful; the analytical process turns the data into information.
  • During processing, raw data is used as an input to produce information as an output, typically in the form of reports and other analytical tools.

• Strong Acid-Strong Base Titrations

  • The reactant of unknown concentration is deposited into an Erlenmeyer flask and is called the analyte.
  • The indicator—phenolphthalein, in this case—has been added to the analyte in the Erlenmeyer flask.
  • Step 2: Use stoichiometry to figure out the moles of HCl in the analyte.