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Business Analytics Final

Terms in this set (44)

Decision Making Steps
1. Identify the problem
2. Determine criteria used to evaluate alternative solutions
3. Determine set of alternative solutions
4. Evaluate alternatives
5. Choose an alternative
Types of analytics
descriptive, predictive, and prescriptive
Descriptive analytics
set of techniques that describe what happened in the past
Data queries, reports, and stats
descriptive analytics (what?)
Predictive analytics
consists of techniques that use models constructed from past data to predict the future/ascertain the impact of 1 variable on another
survey data, linear regression, data mining
predictive stats (why)
survey data, linear regression, data mining
predictive analytics (why)
prescriptive analytics
indicates best course of action
optimization models, simulation opt. , decision analytics, utility theory, big data
prescriptive analytics (how?)
data dashboard
Data visualization tool that illustrates multiple metrics and automatically updates these metrics as new data become available
data visualization
Generating a summary table, generating charts to help interpret , analyze, learn from data
optimization
best decision subject to constraints of the situation, ex: portfolio, supply network design models, price markdown models, etc.
simulation optimization
Combines the use of probability and statistics to model uncertainty with optimization techniques to find good decisions in highly complex and highly uncertain
Numerical variables
have values that represent quantities
categorical variables
nominal and ordinal data which arithmetic operations can't be performed
dummy variables
In regression analysis, a way of representing two-group or dichotomous, nominally scaled independent variables by coding one group as 0 and the other as 1.
independent variables/ predictor variables
X (IQ, EQ, attitude)
dependent/outcome variables
Y (learning outcomes)
population
all elements of interest
sample
a subset of the population
sampling
process by which participants are selected
frequency distribution
summary of data that shows the # of observations in each of several non-overlapping classes, typically referred to as bins
data-ink ratio
Measures the proportion of what Tufte terms "data-ink" to the total amount of ink used in a table or chart
scatter chart
graphical presentation of the relationship between 2 quantitative variables
pie chart
compares categorical data
bar chart
use horizontal bars to display the magnitude of the quantitative data
line chart (time series)
a line connects the points in the chart (ex: used for minutes, days, hours, years, etc.)
2 types of observation
supervised + unsupervised
supervised learning
Data mining methods for predicting an outcome based on a set of input variables, or features
unsupervised learning
when there is no outcome variable to predict; rather, the goal is to use the variable values to identify relationships between observations
regression analysis
used to develop an equation showing how the variables are related
forecast analysis
A prediction of what will happen in the future. Can be qualitative or quantitative
cluster analysis
Organizing observations into similar groups based on the observed variables. Ex: k-means, hierarchical
meaning of extrapolation
Prediction of the value of the dependent variable outside the experimental region
Why is extrapolation risky?
We have no empirical evidence that data used to estimate the relationship between x and y holds true for x values outside the range of x values in the data used to estimate the relationship
time series patterns
1. Horizontal
2. Trend
3. Seasonal
4. Cyclical
5. Random
stationary series patterns
used to denote a time series whose statistical properties are independent of time:
1. the process generating data has a constant mean
2. The variability of the time series is constant over time
class 0
no default or effect
class 1
default or effect
confusion matrix
displays a model's correct and incorrect classifications
feasible region
the set of all feasible solutions, and is used to find the solution that gives the best objective function value
objective
optimization problems maximize or minimize some function and have a set of restrictions known as constraints
maximization/minimization
the objective in all linear programming problems.
non-negativity constraints
constraints that ensure the solution to the problem will only contain nonnegative values for the decision variables
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