stat Flashcards

1
Q

It is the responsibility of managers to plan, coordinate, organize, and lead their organizations to better performance. Ultimately, managers’ responsibilities require that they make strategic, tactical, or operational decisions.

A

Decision Making

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2
Q

➫ involve higher-level issues concerned with the overall direction of the organization
➫ these decisions define the organization’s overall goals and aspirations for the future.
➫ usually the domain of higher-level executives and have a time horizon of three to five years.

A

Strategic decisions

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3
Q

concern how the organization should achieve the goals and objectives set by its strategy,
➫ they are usually the responsibility of midlevel management.
➫ usually span a year and thus are revisited annually or even every six months.

A

Tactical Decisions

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4
Q

affect how the firm is run from day today
➫ they are the domain of operations managers,
who are the closest to the customer.

A

Operational decisions

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5
Q

Regardless of the level within the firm, decision making can be defined as the following process

A
  1. Identify and define the problem.
  2. Determine the criteria that will be used to
    evaluate alternative solutions.
  3. Determine the set of alternative solutions.
  4. Evaluate the alternatives.
  5. Choose an alternative.
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6
Q

There are a number of approaches to making decisions

A

-tradition(“We’vealwaysdoneitthisway”)
-­ intuition(“gutfeeling”)
­ -rules of thumb (“As the restaurant owner, I
schedule twice the number of waiters and cooks on holidays”).

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7
Q

What makes decision making difficult and challenging?

A

Uncertainty is probably the number one challenge.

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8
Q

the scientific process of transforming data into insight for making better decisions.
➫ used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making.

A

Business analytics

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9
Q

Analytics is generally thought to comprise three broad categories of techniques

A

descriptive analytics, predictive analytics, and prescriptive analytics.

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10
Q

encompasses the set of techniques that describes what has happened in the past.
➫ Examples are data queries, reports, descriptive statistics, data visualization including data dashboards, some data-mining techniques, and basic what-if spreadsheet models.

A

Descriptive Analytics

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11
Q

is a request for information with certain characteristics from a database.
• For example, a query to a manufacturing plant’s database might be for all records of shipments to a particular distribution center during the month of March.

A

data query

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12
Q

• are collections of tables, charts, maps, and summary statistics that are updated as new data become available.
• Dashboards are used to help management monitor specific aspects of the company’s performance rela

A

data dashboard

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13
Q

the use of analytical techniques for better understanding patterns and relationships that exist in large data sets.
• For example, by analyzing text on social network platforms like Twitter, data-mining techniques are used by companies to better understand their customers.

A

data mining

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14
Q

consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another.
➫ Forexample,pastdataonproductsalesmaybe used to construct a mathematical model to predict future sales.
➫ This mode can factor in the product’s growth trajectory and seasonality based on past patterns.

A

Predictive Analytics

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15
Q

often referred to as risk analysis, all fall under the banner of predictive analytics.

A

Linear regression, time series analysis, some data- mining techniques, and simulation,

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16
Q

previously discussed as a descriptive analytics tool, is also often used in predictive analytics.

A

Data mining

17
Q

is also often used in the pharmaceutical industry to assess the risk of introducing a new drug.

A

Simulation

18
Q

differs from descriptive and predictive analytics in that 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.
➫ However, a forecast or prediction, when combined with a rule, becomes a prescriptive model.

A

Prescriptive Analysis

19
Q

types of prescriptive models that rely on a rule or set of rules

A

rule-based models

20
Q

models that give the best decision subject to the constraints of the situation.

A

optimization models

21
Q

Another type of modeling in the prescriptive analytics category
• combines the use of probability and statistics to model uncertainty with optimization techniques to find good decisions in highly complex and highly uncertain settings.

A

simulation optimization models