C207 OA QUESTIONS WITH CORRECT ANSWERS | GRADE A+
ASSURED/NEWEST UPDATE
Terms in this set (501)
Common analytic measure to improve performance. Compares
current data with data during a base period.
(Price / Price during "Base Period") x 100
Simple indexing i.e. Big Mac was 1.60 in 1968 which is base period. what is index for 2014 if
price was
4.80 then?
(4..60) * 100 = 300 (means price is 3x greater than
base period) Used to identify price fluctuations of
supplies, materials, products, etc.
Weighted Index assign a weight to allow for significant differences in the index.
decrease cost of data storage
Reasons for including analytics in
decision- making
increase processing power
using current and past data for strictly descriptive purposes.
Descriptive Analytics i.e. car price data shows a 2% increase over the prior year
a manager wants to know why sales spiked during the prior quarter
using current and past data to predict/estimate future.
Predictive / Inferential Analytics
i.e. based on the past 10 years of data for car prices, we predict an
increase of 1.5% over the upcoming year.
, using past data to PREDICT or ESTIMATE future in order to optimize
operations
includes experimental design and optimization to aid in
Prescriptive Analytics
DECISION-MAKING. MANAGERIAL DECISIONS.
i.e. based on past data, sales prices for electric cars could
increase by 5% if we increased charging stations by 7%
Data so big that it's difficult to process using
traditional methods. Stored in a Data
Warehouse.
Mined to identify patterns and trends
Big data
Primary purpose is to encourage buying behavior.
Enables products to be more tailored to customer
base. Improves decision-making.
Supports development of next generation products/services.
watch for keywords in test options. i.e. company TOTAL sales (
just one number) vs all sales invoices
Data follows pre-defined formats.
Structured / Quantitative Data
i.e. multiple choice answers, addresses, names, stock tickers
Data doesn't follow pre-defined formats. Usually gets
structured by a "theme analysis"
Unstructured / Qualitative Data
i.e. blocks of freeform text, audio, video
Data that can take any value (within a set range)
Continuous Data
i.e. 3.14159, -189,115.2
a thermometer reads 66.5 degrees
, data is ordered at equal intervals apart and "0" doesn't mean
absence of data, just another data point
Interval Data (data measuring levels)
a type of continuous data
i.e. date, time, degrees
0 actually means nothing, not just a
Ratio Data (data measuring levels) data point a type of continuous
data
i.e. money, height weight
Data that can only take on whole values and has clear boundaries
Discrete Data
i.e. 4, 7, 8 in a preset range of 1-100
ASSURED/NEWEST UPDATE
Terms in this set (501)
Common analytic measure to improve performance. Compares
current data with data during a base period.
(Price / Price during "Base Period") x 100
Simple indexing i.e. Big Mac was 1.60 in 1968 which is base period. what is index for 2014 if
price was
4.80 then?
(4..60) * 100 = 300 (means price is 3x greater than
base period) Used to identify price fluctuations of
supplies, materials, products, etc.
Weighted Index assign a weight to allow for significant differences in the index.
decrease cost of data storage
Reasons for including analytics in
decision- making
increase processing power
using current and past data for strictly descriptive purposes.
Descriptive Analytics i.e. car price data shows a 2% increase over the prior year
a manager wants to know why sales spiked during the prior quarter
using current and past data to predict/estimate future.
Predictive / Inferential Analytics
i.e. based on the past 10 years of data for car prices, we predict an
increase of 1.5% over the upcoming year.
, using past data to PREDICT or ESTIMATE future in order to optimize
operations
includes experimental design and optimization to aid in
Prescriptive Analytics
DECISION-MAKING. MANAGERIAL DECISIONS.
i.e. based on past data, sales prices for electric cars could
increase by 5% if we increased charging stations by 7%
Data so big that it's difficult to process using
traditional methods. Stored in a Data
Warehouse.
Mined to identify patterns and trends
Big data
Primary purpose is to encourage buying behavior.
Enables products to be more tailored to customer
base. Improves decision-making.
Supports development of next generation products/services.
watch for keywords in test options. i.e. company TOTAL sales (
just one number) vs all sales invoices
Data follows pre-defined formats.
Structured / Quantitative Data
i.e. multiple choice answers, addresses, names, stock tickers
Data doesn't follow pre-defined formats. Usually gets
structured by a "theme analysis"
Unstructured / Qualitative Data
i.e. blocks of freeform text, audio, video
Data that can take any value (within a set range)
Continuous Data
i.e. 3.14159, -189,115.2
a thermometer reads 66.5 degrees
, data is ordered at equal intervals apart and "0" doesn't mean
absence of data, just another data point
Interval Data (data measuring levels)
a type of continuous data
i.e. date, time, degrees
0 actually means nothing, not just a
Ratio Data (data measuring levels) data point a type of continuous
data
i.e. money, height weight
Data that can only take on whole values and has clear boundaries
Discrete Data
i.e. 4, 7, 8 in a preset range of 1-100
