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Business Analytics > Exam 1 > Flashcards

Exam 1 Flashcards

1
Q

strategic decisions

A
  • involve higher level issues concerned with the overall direction of the organization
  • define the organization’s overall goals and aspirations for the future
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2
Q

tactical decisions

A
  • concern how the organization should achieve the goals and objectives set by its strategy
  • are usually the responsibility of midlevel management
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3
Q

operational decisions

A
  • affect how the firm is run from day to day

- are the domain of first line managers who are closest to the customer

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4
Q

common approaches to decision making

A
  • tradition
  • intuition
  • rules of thumb
  • sacred cow
  • using the relevant data available
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5
Q

business analytics

A
  • 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
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6
Q

descriptive analytics

A

-the use of data to understand past and current business performance and make informed decisions

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7
Q

predictive analytics

A

-predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time

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8
Q

prescriptive analytics

A

-identify the best alternatives to minimize or maximize some objective

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9
Q

optimization models

A
  • part of prescriptive analytics

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

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10
Q

decision analytics

A
  • part of prescriptive analytics
  • used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events
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11
Q

big data

A

-any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software

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12
Q

cross-sectional data

A

data collected from several entities at the same, or approximately the same, point in time

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13
Q

time series data

A

data collected over several time periods

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14
Q

frequency distribution

A

a summary of data that shows the number (frequency) of observations in each of several non-overlapping classes (bins)

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15
Q

histogram

A

a common graphical presentation of quantitative data

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16
Q

arithmetic mean

A

average of a set of numerical values

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17
Q

skewness

A

an important numerical measure of the shape of a distribution

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18
Q

range

A

subtracting smallest value from the largest value

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19
Q

variance

A
  • a measure of variability that utilizes all the data

- based on the deviation of the observations from the mean

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20
Q

standard deviation

A

-the positive square root of the variance

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21
Q

coefficient of variation

A

descriptive statistic that indicates how large the standard deviation is relative to the mean

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22
Q

data visualization

A
  • first step in interpreting data
  • creating a summary table for data
  • generating charts to represent data
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23
Q

data ink ratio

A
  • remove unnecessary non-data ink
  • de-emphasize and regularize the remaining non-data ink
  • emphasize the most important data ink
24
Q

when to use tables

A
  • to look up and compare individual values

- data must be precise

25
Q

when to use graphs

A
  • to see patterns, trends, relationships, and exceptions, to make broader comparisons
  • to rapidly get a sense of the story
26
Q

percentile

A

the value of a variable at which a specified (approximate) percentage of observations are below that value

27
Q

quartile

A

division points when data is divided into four equal parts

28
Q

IQR

A

interquartile range

-difference between third and first quartiles

29
Q

z-score

A

measures the relative location of a value in the data set

  • helps to determine how far a particular value is from the mean relative to the data set’s standard deviation
  • often called the standardized value
30
Q

outliers

A

extreme values in a data set

  • may be incorrectly recorded
  • may be from an observation that doesn’t belong to the population we are studying
31
Q

box plot

A
  • a graphical summary of the distribution of data

- developed from the quartiles for a data set

32
Q

scatter chart

A
  • useful graph for analyzing the relationship between two variables
  • also suggests a trend line could be used as an approximation for the relationship between variables
33
Q

covariance

A

a descriptive measure of the linear association between two variables

34
Q

correlation coefficient

A

-measures the linear relationship between two variables

35
Q

parallel coordinates plot

A
  • used for plotting multivariate, numerical data

- ideal for comparing many variables together and seeing the relationships between them

36
Q

regression analysis

A

-a statistical tool that examines the relationship between two or more variables so that one may be predicted from the other(s)

37
Q

simple linear regression model

A

-the equation that describes how y is related to x and an error term

38
Q

experimental region

A

the range of values of the independent variables in the data used to estimate the simple linear regression model

39
Q

extrapolation

A

prediction of the value of the dependent variable outside the experimental region

40
Q

multiple regression model

A

the equation that describes how the dependent variable y is related to the independent variables x1,x2,x3…, and an error term

41
Q

multicollinearity

A

-the correlation among the independent variables in multiple regression analysis

42
Q

time series

A

a sequence of observations on a variable measured at successive points in time or over successive periods of time
-objective of analysis is to uncover a pattern in the time series and then extrapolate the pattern into the future

43
Q

horizontal time series pattern

A

exists when the data fluctuate randomly around a constant mean overtime

44
Q

trend pattern

A

shows gradual shifts or movements to relatively higher or lower values over a longer period of time
-usually a result of long-term factors

45
Q

seasonal pattern

A

-recurring patterns over successive periods of time

46
Q

cyclical pattern

A

exists if the time series plot shows an alternating sequence of points below and above the trend line that lasts for multiple years

47
Q

forecast error

A

difference between the actual and the forecasted values for period t

48
Q

MFE

A

mean forecast error

-mean or average of the errors

49
Q

MAE

A

mean absolute error

-measure of forecast accuracy that avoids problem of positive and negative errors offsetting one another

50
Q

MSE

A

measure that avoids the problem of positive and negative errors offsetting each other
-computing the average of the squared forecast errors

51
Q

MAPE

A
  • mean absolute percentage error

- average of the absolute value of percentage forecast errors

52
Q

moving averages

A

-use the average of the most recent k data values in the time series as the forecast for the next period

53
Q

exponential smoothing

A

uses a weighted average of past time series values as a forecast

54
Q

Four v’s of big data

A

Volume
Velocity
Veracity
Variety

55
Q

Veracity

A

How much uncertainty is in the data

Business Analytics (1 decks)

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