BSIS 308 Exam 1 #1 Questions with correct answers
why is business analytics important? - ✔✔can aid decision making by creating
insights from data, by improving our ability to more accurately forecast for
planning, by helping us quantify risk, and by yielding better alternatives through
analysis and optimization.
Descriptive analytics - ✔✔the use of data to understand past and current business
performance and make informed decisions. Examples are data queries, reports,
descriptive statistics, data visualization including data dashboards, some data-
mining techniques, and basic what-if spreadsheet models.
Predictive Analytics - ✔✔consists of techniques that use models constructed from
past data to predict the future or ascertain the impact of one variable on another.
Linear regression, time series analysis, some data-mining techniques, and
simulation, often referred to as risk analysis, all fall under the banner of predictive
analytics.
Prescriptive Analytics - ✔✔indicates a course of action to take; that is, the output
of a prescriptive model is a decision. Predictive models provide a forecast or
prediction, but do not provide a decision. Other examples of prescriptive analytics
are portfolio models in finance, supply network design models in operations, and
price-markdown models in retailing.
Big Data - ✔✔Any set of data that is too large or too complex to be handled by
standard data-processing techniques and typical desktop software
Volume - ✔✔Because data are collected electronically, we are able to collect
more of it. To be useful, these data must be stored, and this storage has led to
why is business analytics important? - ✔✔can aid decision making by creating
insights from data, by improving our ability to more accurately forecast for
planning, by helping us quantify risk, and by yielding better alternatives through
analysis and optimization.
Descriptive analytics - ✔✔the use of data to understand past and current business
performance and make informed decisions. Examples are data queries, reports,
descriptive statistics, data visualization including data dashboards, some data-
mining techniques, and basic what-if spreadsheet models.
Predictive Analytics - ✔✔consists of techniques that use models constructed from
past data to predict the future or ascertain the impact of one variable on another.
Linear regression, time series analysis, some data-mining techniques, and
simulation, often referred to as risk analysis, all fall under the banner of predictive
analytics.
Prescriptive Analytics - ✔✔indicates a course of action to take; that is, the output
of a prescriptive model is a decision. Predictive models provide a forecast or
prediction, but do not provide a decision. Other examples of prescriptive analytics
are portfolio models in finance, supply network design models in operations, and
price-markdown models in retailing.
Big Data - ✔✔Any set of data that is too large or too complex to be handled by
standard data-processing techniques and typical desktop software
Volume - ✔✔Because data are collected electronically, we are able to collect
more of it. To be useful, these data must be stored, and this storage has led to
