WGU C215 Study Guide - FINAL

C207 OA

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WGU C207 OA

66 terms Omar_Chacon9 Preview C215 Teacher Ma Simple indexingCommon analytic measure to improve performance. Compares current data with data during a base period.(Price / Price during "Base Period") x 100 i.e. Big Mac was 1.60 in 1968 which is base period. what is index for 2014 if price was 4.80 then?(4.80 / 1.60) * 100 = 300 (means price is 3x greater than base period) Used to identify price fluctuations of supplies, materials, products, etc.Weighted Indexassign a weight to allow for significant differences in the index.Reasons for including analytics in decision-making decrease cost of data storage increase processing power Descriptive Analyticsusing current and past data for strictly descriptive purposes.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 Predictive / Inferential Analyticsusing current and past data to predict/estimate future.i.e. based on the past 10 years of data for car prices, we predict an increase of 1.5% over the upcoming year.

Prescriptive Analyticsusing past data to PREDICT or ESTIMATE future in order to optimize operations includes experimental design and optimization to aid in 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% Big dataData so big that it's difficult to process using traditional methods.Stored in a Data Warehouse.Mined to identify patterns and trends 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 Structured / Quantitative DataData follows pre-defined formats.i.e. multiple choice answers, addresses, names, stock tickers Unstructured / Qualitative DataData doesn't follow pre-defined formats. Usually gets structured by a "theme analysis" i.e. blocks of freeform text, audio, video Continuous DataData that can take any value (within a set range) i.e. 3.14159, -189,115.2 a thermometer reads 66.5 degrees Interval Data (data measuring levels)data is ordered at equal intervals apart and "0" doesn't mean absence of data, just another data point a type of continuous data i.e. date, time, degrees Ratio Data (data measuring levels)0 actually means nothing, not just a data point a type of continuous data i.e. money, height weight

Discrete DataData that can only take on whole values and has clear boundaries i.e. 4, 7, 8 in a preset range of 1-100 Ordinal data (data measuring levels)data is ordered based on quality a type of discrete data i.e. in blackbelt data, level "3" is higher quality than "1" gold, silver, and bronze medals Nominal / Categorical Data (data measuring levels) data is assigned a category/label for identification and grouping purposes a type of discrete data i.e. males are assigned "0" and females "1"

potential quality errors: categories can be misspelled

Attribute DataData that shows whether a result meets a requirement or not (yes/no, pass/fail).Davenport-Kim Three-Stage Model1. Frame the problem - recognize problem and review previous findings.

• Solve the problem - modeling, collection, analysis
• Communicate results - tailor to audience, use visuals, show results.

Reliability of Datadata that is consistent (but not necessarily accurate) i.e. a thermometer reads 20, 21, 21, 20, 20, 19, 19, 21, 20, 19 a test given to a student consistently shows similar scores Validity of Datadata that is accurate requires sample selection to be adequate size and random.i.e. a thermometer consistently reads from 20-25 F but the water isn't even frozen (not valid) Data Error TypesOmission - data being left out, missed, forgotten. sorting in spreadsheet can help to identify Out of Range - data that doesn't fit the expected, viable range. sorting in spreadsheet can help to identify outliers.Entry/input errors - typos, miscommunications, illegible handwriting

Systematic Errorerror will cause other errors until fixed i.e. a tire pressure sensor breaks and stops functioning, resulting in omission errors until fixed a scale is calibrated prior to being used in order to reduce systemic error Random Error / Unpredictable Errorerror that does not consistently repeat due to system flaw and therefore doesn't need fix/adjustment. aka its "self fixing".minimize effects by increasing sample size i.e. a tire pressure sensor records an outlier / out-of-range caused by going over a speed bump at high speed True Score TheoryObserved Score (raw data score) = true score + random error score + systematic error in absence of systematic error, it's just true score + random error

Measurement Biasdata doesn't represent the study group because of:

• sample isn't random enough
• sample isn't big enough
• sample wasn't inclusive enough, or was too inclusive, to represent study group

i.e. a survey on favorite foods was sent to all renters in a city (didn't include homeowners so not a "Truly Representative Sample") Conscious Biasthe subject is biased towards a certain result because he believes it will benefit him in some way Information Biasresponse bias - people give different answers when the response isn't anonymous and confidential i.e. a boss surveys his own employees to see if they are satisfied with is performance conscious bias - questions are deliberately leading or persuading the subject toward a certain answer i.e. a survey question reads: "Don't you think it would be better if the gov't provided free contraception?" Data Managementcleaning and organizing data Quality Control in datareducing and minimizing data errors clean and organize data reduce amount of incomplete data