Content deleted Content added
Reverted 1 edit by Aadilali07 (talk): Spam |
m brackets fixed |
||
| (38 intermediate revisions by 27 users not shown) | |||
Line 1:
{{short description|
{{Data Visualization}}
{{Computational physics}}
'''Data analysis''' is the process of inspecting, [[Data cleansing|cleansing]], [[Data transformation|transforming]], and [[Data modeling|modeling]] [[data]] with the goal of discovering useful information, informing conclusions, and supporting [[decision-making]].<ref name="Auerbach Publications">{{Citation|title=Transforming Unstructured Data into Useful Information|date=2014-03-12|url=http://dx.doi.org/10.1201/b16666-14|work=Big Data, Mining, and Analytics|pages=227–246|publisher=Auerbach Publications|doi=10.1201/b16666-14|isbn=978-0-429-09529-0|access-date=2021-05-29}}</ref> Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains.<ref>{{Citation|title=The Multiple Facets of Correlation Functions |url=http://dx.doi.org/10.1017/9781108241922.013 |work=Data Analysis Techniques for Physical Scientists|year=2017|pages=526–576|publisher=Cambridge University Press|doi=10.1017/9781108241922.013|isbn=978-1-108-41678-8|access-date=2021-05-29}}</ref> In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.<ref>Xia, B. S., & Gong, P. (2015). Review of business intelligence through data analysis. ''Benchmarking'', ''21''(2), 300-311. {{doi|10.1108/BIJ-08-2012-0050}}</ref>
[[Data mining]] is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while [[business intelligence]] covers data analysis that relies heavily on aggregation, focusing mainly on business information.
==Data analysis process==
[[File:Data visualization process v1.png|right|350px|thumb|Data science process flowchart from ''Doing Data Science'', by Schutt & O'Neil (2013)]]
''Data analysis'' is a [[Process theory|process]] for obtaining [[raw data]], and subsequently converting it into information useful for decision-making by users.<ref name="Auerbach Publications"/> Statistician [[John Tukey]], defined data analysis in 1961, as:<blockquote>"Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."<ref>{{Cite journal |url=http://projecteuclid.org/download/pdf_1/euclid.aoms/1177704711 |title=John Tukey-The Future of Data Analysis-July 1961 |journal=The Annals of Mathematical Statistics |date=March 1962 |volume=33 |issue=1 |pages=1–67 |doi=10.1214/aoms/1177704711 |access-date=2015-01-01 |archive-date=2020-01-26 |archive-url=https://web.archive.org/web/20200126232007/https://projecteuclid.org/download/pdf_1/euclid.aoms/1177704711 |url-status=live |last1=Tukey |first1=John W. }}</ref></blockquote>
There are several phases | author2-last = O'Neil | author2-first= Cathy | author2-link= Cathy O'Neil
| author1-last = Schutt | author1-first= Rachel
| year = 2013
| title = Doing Data Science | publisher = [[O'Reilly Media]]
| isbn = 978-1-449-35865-5}}</ref>
===Data requirements===
The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis).<ref>{{Citation|title=USE OF THE DATA|date=2015-02-06|url=http://dx.doi.org/10.1002/9781118986370.ch18|work=Handbook of Petroleum Product Analysis|pages=296–303|place=Hoboken, NJ|publisher=John Wiley & Sons, Inc|doi=10.1002/9781118986370.ch18|isbn=978-1-118-98637-0|access-date=2021-05-29
===Data collection ===
Data
===Data processing===
[[File:Relationship of data, information and intelligence.png|thumb|350px|The phases of the [[intelligence cycle]] used to convert raw information into actionable intelligence or knowledge are conceptually similar to the phases in data analysis.]]
[[Data integration]] is a precursor to data analysis: Data, when initially obtained, must be processed or organized for analysis.
===Data cleaning===
{{Main|Data cleansing}}
Once processed and organized, the data may be incomplete, contain duplicates, or contain errors.<ref name="Bohannon">{{Cite journal |last=Bohannon|first=John|date=2016-02-24|title=Many surveys, about one in five, may contain fraudulent data |journal=Science|doi=10.1126/science.aaf4104|issn=0036-8075|doi-access=free
Such data problems can also be identified through a variety of analytical techniques. For example; with financial information, the totals for particular variables may be compared against separately published numbers that are believed to be reliable.<ref name="Koomey1">{{Cite web |url=http://www.perceptualedge.com/articles/b-eye/quantitative_data.pdf |title=Perceptual Edge-Jonathan Koomey-Best practices for understanding quantitative data-February 14, 2006 |access-date=November 12, 2014 |archive-date=October 5, 2014 |archive-url=https://web.archive.org/web/20141005075112/http://www.perceptualedge.com/articles/b-eye/quantitative_data.pdf |url-status=live }}</ref> Unusual amounts, above or below predetermined thresholds, may also be reviewed. There are several types of data cleaning that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values.<ref>{{Cite journal |last1=Peleg |first1=Roni |last2=Avdalimov |first2=Angelika |last3=Freud |first3=Tamar|date=2011-03-23|title=Providing cell phone numbers and email addresses to Patients: the physician's perspective|journal=BMC Research Notes|volume=4|issue=1|page=76|doi=10.1186/1756-0500-4-76|pmid=21426591|issn=1756-0500|pmc=3076270 |doi-access=free }}</ref> Quantitative data methods for outlier detection can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. Text data spell checkers can be used to lessen the amount of mistyped words. However, it is harder to tell if the words are contextually (i.e., semantically and idiomatically) correct.
===Exploratory data analysis===
Once the datasets are cleaned, they can then begin to be analyzed
===Modeling and algorithms===
'''Mathematical formulas''' or '''models''' (also known as '''[[algorithms]]'''), may be applied to the data in order to identify relationships among the variables; for example,
[[Inferential statistics]]
===Data product===
A '''data product''' is a computer application that takes ''data inputs'' and generates ''outputs'', feeding them back into the environment.<ref>{{Cite journal|last=Conway|first=Steve|date=2012-07-04|title=A Cautionary Note on Data Inputs and Visual Outputs in Social Network Analysis|url=http://dx.doi.org/10.1111/j.1467-8551.2012.00835.x|journal=British Journal of Management |volume=25|issue=1|pages=102–117|doi=10.1111/j.1467-8551.2012.00835.x|hdl=2381/36068|s2cid=154347514|issn=1045-3172}}</ref> It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy.<ref>{{Citation|title=Customer Purchases and Other Repeated Events|date=2016-01-29|url=http://dx.doi.org/10.1002/9781119183419.ch8|work=Data Analysis Using SQL and Excel®|pages=367–420|place=Indianapolis, Indiana|publisher=John Wiley & Sons, Inc.|doi=10.1002/9781119183419.ch8|isbn=978-1-119-18341-9|access-date=2021-05-31}}</ref><ref name="Schutt & O'Neil"/>
===Communication===
Line 55 ⟶ 51:
{{Main|Data and information visualization}}
Once data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements.<ref>{{Citation|title=Data requirements for semiconductor die. Exchange data formats and data dictionary|url=http://dx.doi.org/10.3403/02271298|publisher=BSI British Standards|doi=10.3403/02271298|access-date=2021-05-31}}</ref> The users may have feedback, which results in additional analysis.
When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques to help communicate the message more clearly and efficiently to the audience.
==Quantitative messages==
{{Main|Data and information visualization}}
[[File:Total Revenues and Outlays as Percent GDP 2013.png|thumb|right|250px|A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time
[[File:U.S. Phillips Curve 2000 to 2013.png|thumb|right|250px|A scatterplot illustrating the correlation between two variables (inflation and unemployment) measured at points in time
[[Stephen Few]] described eight types of quantitative messages that users may attempt to communicate from a set of data, including the associated graphs.<ref name="Few GraphType">{{Cite web |url=http://www.perceptualedge.com/articles/ie/the_right_graph.pdf |title=Stephen Few-Perceptual Edge-Selecting the Right Graph for Your Message-2004 |access-date=2014-10-29 |archive-date=2014-10-05 |archive-url=https://web.archive.org/web/20141005080924/http://www.perceptualedge.com/articles/ie/the_right_graph.pdf |url-status=live }}</ref><ref>{{Cite web |url=http://www.perceptualedge.com/articles/misc/Graph_Selection_Matrix.pdf |title=Stephen Few-Perceptual Edge-Graph Selection Matrix |access-date=2014-10-29 |archive-date=2014-10-05 |archive-url=https://web.archive.org/web/20141005080945/http://www.perceptualedge.com/articles/misc/Graph_Selection_Matrix.pdf |url-status=live }}</ref>
#Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the ''measure'') by salespersons (the ''category'', with each salesperson a ''categorical subdivision'') during a single period.<ref>{{Citation|last=Riehl|first=Emily|title=A sampling of 2-categorical aspects of quasi-category theory|url=http://dx.doi.org/10.1017/cbo9781107261457.019|work=Categorical Homotopy Theory|year=2014|pages=318–336|place=Cambridge|publisher=Cambridge University Press|doi=10.1017/cbo9781107261457.019|isbn=978-1-107-26145-7|access-date=2021-06-03}}</ref> A [[bar chart]] may be used to show the comparison across the salespersons.<ref>{{cite book | doi=10.1007/1-4020-0612-8_1063 | chapter=X-Bar Chart | title=Encyclopedia of Production and Manufacturing Management | date=2000 | page=841 | isbn=978-0-7923-8630-8 | last1=Swamidass | first1=P. M. }}</ref>▼
#Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A [[line chart]] may be used to demonstrate the trend.
#Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A [[pie chart]] or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.<ref>{{Cite journal|title=Chart C5.3. Percentage of 15-19 year-olds not in education, by labour market status (2012)|url=http://dx.doi.org/10.1787/888933119055|access-date=2021-06-03|doi=10.1787/888933119055}}</ref>▼
▲#Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the ''measure'') by salespersons (the ''category'', with each salesperson a ''categorical subdivision'') during a single period.
▲#Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A [[pie chart]] or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.<ref>{{Cite journal |title=Chart C5.3. Percentage of 15-19 year-olds not in education, by labour market status (2012)|url=http://dx.doi.org/10.1787/888933119055|access-date=2021-06-03|doi=10.1787/888933119055}}</ref>
#Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show the comparison of the actual versus the reference amount.<ref>{{Cite journal|title=Chart 7: Households: final consumption expenditure versus actual individual consumption|url=http://dx.doi.org/10.1787/665527077310|access-date=2021-06-03|doi=10.1787/665527077310}}</ref>
#Frequency distribution: Shows the number of observations of a particular variable for a given interval, such as the number of years in which the stock market return is between intervals such as 0–10%, 11–20%, etc. A [[histogram]], a type of bar chart, may be used for this analysis.
#Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A [[scatter plot]] is typically used for this message.<ref>{{Cite journal|title=Table 2: Graph comparison between Scatter plot, Violin + Scatter plot, Heatmap and ViSiElse graph.|journal=PeerJ|date=3 February 2020|volume=8|pages=e8341|doi=10.7717/peerj.8341/table-2|last1=Garnier|first1=Elodie M.|last2=Fouret|first2=Nastasia|last3=Descoins|first3=Médéric |doi-access=free }}</ref>
#Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.<ref>{{Cite journal|date=2009|title=Product comparison chart: Wearables |url=http://dx.doi.org/10.1037/e539162010-006|access-date=2021-06-03|website=PsycEXTRA Dataset|doi=10.1037/e539162010-006}}</ref>
#Geographic or
==Analyzing quantitative data in finance==
{{See also|Problem solving}}
Author [[Jonathan Koomey]] has recommended a series of best practices for understanding quantitative data. These include:<ref
*Check raw data for anomalies prior to performing an analysis;
*Re-perform important calculations, such as verifying columns of data that are formula
*Confirm main totals are the sum of subtotals;
*Check relationships between numbers that should be related in a predictable way, such as ratios over time;
*Normalize numbers to make comparisons easier, such as analyzing amounts per person or relative to GDP or as an index value relative to a base year;
*Break problems into component parts by analyzing factors that led to the results, such as [[DuPont analysis]] of return on equity.
For the variables under examination, analysts typically obtain [[descriptive statistics]], such as the mean (average), [[median]], and [[standard deviation]]. They may also analyze the [[probability distribution|distribution]] of the key variables to see how the individual values cluster around the mean.<ref name="Koomey1"/>
[[File:US_Employment_Statistics_-_March_2015.png|thumb|250px|right|An illustration of the [[MECE principle]] used for data analysis]]
▲[[File:US_Employment_Statistics_-_March_2015.png|thumb|250px|right|An illustration of the [[MECE principle]] used for data analysis.]] The consultants at [[McKinsey and Company]] named a technique for breaking a quantitative problem down into its component parts called the [[MECE principle]].<ref>{{Citation|title=Consultants Employed by McKinsey & Company|date=2008-07-30|url=http://dx.doi.org/10.4324/9781315701974-15|work=Organizational Behavior 5|pages=77–82|publisher=Routledge|doi=10.4324/9781315701974-15|isbn=978-1-315-70197-4|access-date=2021-06-03}}</ref> Each layer can be broken down into its components; each of the sub-components must be [[Mutually exclusive events|mutually exclusive]] of each other and [[Collectively exhaustive events|collectively]] add up to the layer above them.<ref>{{Citation|last=Antiphanes|editor1-first=S. Douglas|editor1-last=Olson|title=H6 Antiphanes fr.172.1-4, from Women Who Looked Like Each Other or Men Who Looked Like Each Other|url=http://dx.doi.org/10.1093/oseo/instance.00232915|work=Broken Laughter: Select Fragments of Greek Comedy|year=2007|publisher=Oxford University Press|doi=10.1093/oseo/instance.00232915|isbn=978-0-19-928785-7|access-date=2021-06-03}}</ref> The relationship is referred to as "Mutually Exclusive and Collectively Exhaustive" or MECE. For example, profit by definition can be broken down into total revenue and total cost.<ref>{{Cite journal|last=Carey|first=Malachy|date=November 1981|title=On Mutually Exclusive and Collectively Exhaustive Properties of Demand Functions|url=http://dx.doi.org/10.2307/2553697|journal=Economica|volume=48|issue=192|pages=407–415|doi=10.2307/2553697|jstor=2553697|issn=0013-0427}}</ref> In turn, total revenue can be analyzed by its components, such as the revenue of divisions A, B, and C (which are mutually exclusive of each other) and should add to the total revenue (collectively exhaustive).<ref>{{Cite journal|title=Total tax revenue|url=http://dx.doi.org/10.1787/352874835867|access-date=2021-06-03|doi=10.1787/352874835867}}</ref>
Analysts may use robust statistical measurements to solve certain analytical problems.
[[Regression analysis]] may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate (Y)?").<ref name="Yanamandra 57–68">{{Cite journal|last=Yanamandra|first=Venkataramana|date=September 2015|title=Exchange rate changes and inflation in India: What is the extent of exchange rate pass-through to imports?|url=http://dx.doi.org/10.1016/j.eap.2015.07.004 |journal=Economic Analysis and Policy |volume=47 |pages=57–68 |doi=10.1016/j.eap.2015.07.004|issn=0313-5926
[[Necessary condition analysis]] (NCA) may be used when the analyst is trying to determine the extent to which independent variable X allows variable Y (e.g., "To what extent is a certain unemployment rate (X) necessary for a certain inflation rate (Y)?").<ref name="Yanamandra 57–68"/> Whereas (multiple) regression analysis uses additive logic where each X-variable can produce the outcome and the X's can compensate for each other (they are sufficient but not necessary),<ref>{{Cite web |url=https://doi.org/10.1049%2Fiet-tv.48.859 |last=Feinmann|first=Jane|title=How Can Engineers and Journalists Help Each Other?|access-date=2021-06-03|doi=10.1049/iet-tv.48.859|url-access=subscription|type=Video|publisher=The Institute of Engineering & Technology}}</ref> necessary condition analysis (NCA) uses necessity logic, where one or more X-variables allow the outcome to exist, but may not produce it (they are necessary but not sufficient). Each single necessary condition must be present and compensation is not possible.<ref>{{Cite journal|last=Dul|first=Jan|date=2015|title=Necessary Condition Analysis (NCA): Logic and Methodology of 'Necessary But Not Sufficient' Causality|url=http://dx.doi.org/10.2139/ssrn.2588480|journal=SSRN Electronic Journal|doi=10.2139/ssrn.2588480|hdl=1765/77890|s2cid=219380122|issn=1556-5068}}</ref>
==Analytical activities of data users==
[[File:User-activities.png|Analytic activities of data visualization users|thumb|right|350px]]
Users may have particular data points of interest within a data set, as opposed to the general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points.<ref>Robert Amar, James Eagan, and John Stasko (2005) [http://www.cc.gatech.edu/~stasko/papers/infovis05.pdf "Low-Level Components of Analytic Activity in Information Visualization"] {{Webarchive|url=https://web.archive.org/web/20150213074349/http://www.cc.gatech.edu/~stasko/papers/infovis05.pdf |date=2015-02-13 }}</ref><ref>William Newman (1994) [http://www.mdnpress.com/wmn/pdfs/chi94-pro-formas-2.pdf "A Preliminary Analysis of the Products of HCI Research, Using Pro Forma Abstracts"] {{Webarchive|url=https://web.archive.org/web/20160303212019/http://www.mdnpress.com/wmn/pdfs/chi94-pro-formas-2.pdf |date=2016-03-03 }}</ref><ref>Mary Shaw (2002) [https://www.cs.cmu.edu/~Compose/ftp/shaw-fin-etaps.pdf "What Makes Good Research in Software Engineering?"] {{Webarchive|url=https://web.archive.org/web/20181105042928/http://www.cs.cmu.edu/~Compose/ftp/shaw-fin-etaps.pdf |date=2018-11-05
{| class="wikitable"
! align="center" | # !! width="160" | Task !! General<br />
|-
| align="center" | 1
Line 183 ⟶ 184:
|-
| align="center" | 11
| '''
| Given a set of data cases, find contextual relevancy of the data to the users.
| Which data cases in a set S of data cases are relevant to the current users' context?
Line 196 ⟶ 197:
{{quote box|quote=You are entitled to your own opinion, but you are not entitled to your own facts.|source=[[Daniel Patrick Moynihan]]|width = 250px}}
Effective analysis requires obtaining relevant [[fact]]s to answer questions, support a conclusion or formal [[opinion]], or test [[hypotheses]].<ref>{{Citation|title=Information relevant to your job|date=2007-07-11 |url=http://dx.doi.org/10.4324/9780080544304-16 |work=Obtaining Information for Effective Management|pages=48–54 |publisher=Routledge|doi=10.4324/9780080544304-16|doi-broken-date=
===Cognitive biases===
There are a variety of [[cognitive bias]]es that can adversely affect analysis. For example, [[confirmation bias]] is the tendency to search for or interpret information in a way that confirms one's preconceptions.<ref>{{Cite thesis|title=Confirmation bias in witness interviewing: Can interviewers ignore their preconceptions?|url=http://dx.doi.org/10.25148/etd.fi14071109|publisher=Florida International University|first=Jillian R|last=Rivard|year=2014 |doi=10.25148/etd.fi14071109}}</ref> In addition, individuals may discredit information that does not support their views.<ref>{{Citation|last=Papineau|first=David|title=Does the Sociology of Science Discredit Science?|date=1988|url=http://dx.doi.org/10.1007/978-94-009-2877-0_2|work=Relativism and Realism in Science|pages=37–57|place=Dordrecht|publisher=Springer Netherlands|doi=10.1007/978-94-009-2877-0_2|isbn=978-94-010-7795-8|access-date=2021-06-03}}</ref>
Analysts may be trained specifically to be aware of these biases and how to overcome them.<ref>{{Cite book|date=2005|editor-last=Bromme|editor-first=Rainer|editor2-last=Hesse|editor2-first=Friedrich W.|editor3-last=Spada|editor3-first=Hans|title=Barriers and Biases in Computer-Mediated Knowledge Communication |url=http://dx.doi.org/10.1007/b105100|doi=10.1007/b105100|isbn=978-0-387-24317-7}}</ref> In his book ''Psychology of Intelligence Analysis'', retired CIA analyst [[Richards Heuer]] wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions.<ref>{{Cite book |last=Heuer |first=Richards |editor1-first=Richards J|editor1-last=Heuer|date=2019-06-10|title=Quantitative Approaches to Political Intelligence|url=http://dx.doi.org/10.4324/9780429303647|doi=10.4324/9780429303647|isbn=9780429303647|s2cid=145675822}}</ref> He emphasized procedures to help surface and debate alternative points of view.<ref name="Heuer1">{{cite web |url=https://www.cia.gov/static/9a5f1162fd0932c29bfed1c030edf4ae/Pyschology-of-Intelligence-Analysis.pdf |title=Introduction|publisher=Central Intelligence Agency|access-date=2021-10-25|archive-date=2021-10-25|archive-url=https://web.archive.org/web/20211025160526/https://www.cia.gov/static/9a5f1162fd0932c29bfed1c030edf4ae/Pyschology-of-Intelligence-Analysis.pdf|url-status=live}}</ref>
===Innumeracy===
Effective analysts are generally adept with a variety of numerical techniques. However, audiences may not have such literacy with numbers or [[numeracy]]; they are said to be innumerate.<ref>{{Cite
For example, whether a number is rising or falling may not be the key factor. More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy (GDP) or the amount of cost relative to revenue in corporate financial statements.<ref>{{Cite journal|last1=Gusnaini|first1=Nuriska|last2=Andesto|first2=Rony|last3=Ermawati|date=2020-12-15|title=The Effect of Regional Government Size, Legislative Size, Number of Population, and Intergovernmental Revenue on The Financial Statements Disclosure|url=http://dx.doi.org/10.24018/ejbmr.2020.5.6.651|journal=European Journal of Business and Management Research|volume=5|issue=6|doi=10.24018/ejbmr.2020.5.6.651|s2cid=231675715|issn=2507-1076}}</ref> This numerical technique is referred to as normalization<ref name="Koomey1"/> or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation (i.e., comparing real vs. nominal data) or considering population increases, demographics, etc.<ref>{{Citation|last1=Linsey|first1=Julie S.|author1-link=Julie Linsey|title=Effectiveness of Brainwriting Techniques: Comparing Nominal Groups to Real Teams|date=2011|url=http://dx.doi.org/10.1007/978-0-85729-224-7_22|work=Design Creativity 2010|pages=165–171|place=London|publisher=Springer London|isbn=978-0-85729-223-0|access-date=2021-06-03|last2=Becker|first2=Blake|doi=10.1007/978-0-85729-224-7_22}}</ref> Analysts apply a variety of techniques to address the various quantitative messages described in the section above.<ref>{{Cite journal|last=Lyon|first=J.|date=April 2006|title=Purported Responsible Address in E-Mail Messages|doi=10.17487/rfc4407|url=http://dx.doi.org/10.17487/rfc4407}}</ref>▼
▲For example, whether a number is rising or falling may not be the key factor. More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy (GDP) or the amount of cost relative to revenue in corporate financial statements.<ref>{{Cite journal |last1=Gusnaini |first1=Nuriska |last2=Andesto |first2=Rony |last3=Ermawati|date=2020-12-15|title=The Effect of Regional Government Size, Legislative Size, Number of Population, and Intergovernmental Revenue on The Financial Statements Disclosure |url=http://dx.doi.org/10.24018/ejbmr.2020.5.6.651 |journal=European Journal of Business and Management Research |volume=5 |issue=6 |doi=10.24018/ejbmr.2020.5.6.651 |s2cid=231675715|issn=2507-1076}}</ref> This numerical technique is referred to as normalization<ref name="Koomey1"/> or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation (i.e., comparing real vs. nominal data) or considering population increases, demographics, etc.<ref>{{
Analysts may also analyze data under different assumptions or scenario. For example, when analysts perform [[financial statement analysis]], they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock.<ref>{{Cite book|last=Stock|first=Eugene|title=The History of the Church Missionary Society Its Environment, its Men and its Work|date=10 June 2017|publisher=Hansebooks GmbH |isbn=978-3-337-18120-8|oclc=1189626777}}</ref><ref>{{Cite journal|last=Gross|first=William H.|date=July 1979|title=Coupon Valuation and Interest Rate Cycles|url=http://dx.doi.org/10.2469/faj.v35.n4.68|journal=Financial Analysts Journal|volume=35|issue=4|pages=68–71|doi=10.2469/faj.v35.n4.68|issn=0015-198X}}</ref> Similarly, the CBO analyzes the effects of various policy options on the government's revenue, outlays and deficits, creating alternative future scenarios for key measures.<ref>{{Cite journal|title=25. General government total outlays|url=http://dx.doi.org/10.1787/888932348795|access-date=2021-06-03|doi=10.1787/888932348795}}</ref>▼
▲Analysts may also analyze data under different assumptions or
==
===Analytics and business intelligence===
{{Main|Analytics}}
Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions." It is a subset of [[business intelligence]], which is a set of technologies and processes that uses data to understand and analyze business performance to drive decision-making
===Education===
Line 238 ⟶ 221:
==Practitioner notes==
This section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a Wikipedia article.<ref>{{Citation|last=Brödermann|first=Eckart J.|title=Article 2.2.1 (Scope of the Section) |date=2018 |url=http://dx.doi.org/10.5771/9783845276564-525|work=Commercial Law|pages=525|publisher=Nomos Verlagsgesellschaft mbH & Co. KG|doi=10.5771/9783845276564-525|isbn=978-3-8452-7656-4|access-date=2021-06-03}}</ref>
===Initial data analysis===
The most important distinction between the initial data analysis phase and the main analysis phase
====Quality of data====
Line 247 ⟶ 230:
*Analysis of [[Outlier|extreme observations]]: outlying observations in the data are analyzed to see if they seem to disturb the distribution.<ref>{{Citation|title=Practice for Dealing With Outlying Observations|url=http://dx.doi.org/10.1520/e0178-16a|publisher=ASTM International|doi=10.1520/e0178-16a|access-date=2021-06-03}}</ref>
*Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.<ref>{{Citation|title=Alternative Coding Schemes for Dummy Variables|url=http://dx.doi.org/10.4135/9781412985628.n5|work=Regression with Dummy Variables|year=1993|pages=64–75|___location=Newbury Park, CA|publisher=SAGE Publications, Inc.|doi=10.4135/9781412985628.n5|isbn=978-0-8039-5128-0|access-date=2021-06-03}}</ref>
*Test for [[common-method variance]]. The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.{{sfn|Adèr|2008a|pp=338-341}}▼
▲The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.{{sfn|Adèr|2008a|pp=338-341}}
====Quality of measurements====
The quality of the [[measuring instrument|measurement instruments]] should only be checked during the initial data analysis phase when this is not the focus or research question of the study.
There are two ways to assess measurement quality:
Line 266 ⟶ 248:
====Did the implementation of the study fulfill the intentions of the research design?====
One should check the success of the [[randomization]] procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups.
*[[Dropout (electronics)|dropout]] (this should be identified during the initial data analysis phase)
*Item [[Response rate (survey)|non-response]] (whether this is random or not should be assessed during the initial data analysis phase)
Line 272 ⟶ 254:
====Characteristics of data sample====
In any report or article, the structure of the sample must be accurately described.
*Basic statistics of important variables
*Scatter plots
Line 279 ⟶ 261:
====Final stage of the initial data analysis====
During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.
*In the case of non-[[Normal distribution|normal]]s: should one [[Data transformation (statistics)|transform]] variables; make variables categorical (ordinal/dichotomous); adapt the analysis method?
*In the case of [[missing data]]: should one neglect or impute the missing data; which imputation technique should be used?
Line 316 ⟶ 298:
====Exploratory and confirmatory approaches====
In the main analysis phase, either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected.<ref>{{Citation|title=Exploratory Data Analysis |date=2017-10-13 |url=http://dx.doi.org/10.1002/9781119126805.ch4 |work=Python® for R Users|pages=119–138|place=Hoboken, NJ, USA |publisher=John Wiley & Sons, Inc.|doi=10.1002/9781119126805.ch4|hdl=11380/971504|isbn=978-1-119-12680-5|access-date=2021-06-03}}</ref> In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well.<ref>{{Citation|title=Engaging in Exploratory Data Analysis, Visualization, and Hypothesis Testing – Exploratory Data Analysis, Geovisualization, and Data|date=2015-07-28|url=http://dx.doi.org/10.1201/b18808-8|work=Spatial Analysis|pages=106–139|publisher=CRC Press|doi=10.1201/b18808-8|isbn=978-0-429-06936-9|s2cid=133412598 |access-date=2021-06-03}}</ref> In a confirmatory analysis, clear hypotheses about the data are tested.<ref>{{Citation|title=Hypotheses About Categories|url=http://dx.doi.org/10.4135/9781446287873.n14|work=Starting Statistics: A Short, Clear Guide|year=2010|pages=138–151|___location=London|publisher=SAGE Publications Ltd |doi=10.4135/9781446287873.n14 |isbn=978-1-84920-098-1|access-date=2021-06-03}}</ref>
[[Exploratory data analysis]] should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a [[type 1 error]].
====Stability of results====
It is important to obtain some indication about how generalizable the results are.{{sfn|Adèr|2008b|pp=361-371}} While this is often difficult to check, one can look at the stability of the results. Are the results reliable and reproducible? There are two main ways of doing that.
* ''[[Cross-validation (statistics)|Cross-validation]]''. By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well.<ref>{{cite journal
| last1 = Benson | first1 = Noah C
Line 332 ⟶ 314:
| pmid = 30520736
| pmc = 6340702
}} Supplementary file 1. Cross-validation schema. {{doi|10.7554/elife.40224.014|doi-access=free}}</ref> Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with [[panel data]].<ref>{{Citation|last=Hsiao|first=Cheng|title=Cross-Sectionally Dependent Panel Data|url=http://dx.doi.org/10.1017/cbo9781139839327.012|work=Analysis of Panel Data|year=2014|pages=327–368|place=Cambridge|publisher=Cambridge University Press|doi=10.1017/cbo9781139839327.012|isbn=978-1-139-83932-7|access-date=2021-06-03}}</ref> Hence other methods of validation sometimes need to be used. For more on this topic, see [[statistical model validation]].<ref>{{Citation|last=Hjorth|first=J.S. Urban|title=Cross validation|date=2017-10-19|url=http://dx.doi.org/10.1201/9781315140056-3|work=Computer Intensive Statistical Methods|pages=24–56|publisher=Chapman and Hall/CRC|doi=10.1201/9781315140056-3|isbn=978-1-315-14005-6|access-date=2021-06-03}}</ref>
* ''[[Sensitivity analysis]]''. A procedure to study the behavior of a system or model when global parameters are (systematically) varied. One way to do that is via [[Bootstrapping (statistics)|bootstrapping]].<ref>{{Cite journal |last1=Sheikholeslami|first1=Razi|last2=Razavi|first2=Saman|last3=Haghnegahdar|first3=Amin|date=2019-10-10|title=What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models|journal=Geoscientific Model Development|volume=12|issue=10|pages=4275–4296|doi=10.5194/gmd-12-4275-2019|bibcode=2019GMD....12.4275S|s2cid=204900339|issn=1991-9603 |doi-access=free }}</ref>
==Free software for data analysis==
<!--Free software in this list should be "notable" with a sourced Wikipedia article (see WP:GNG, WP:WTAF).-->
* [[DevInfo]] – A database system endorsed by the [[United Nations Development Group]] for monitoring and analyzing human development.<ref>{{Cite book|chapter=Human development composite indices|title= Human Development Indices and Indicators 2018|pages=21–41|doi=10.18356/ce6f8e92-en|s2cid=240207510|author=United Nations Development Programme|date= 2018|publisher=United Nations }}</ref>
* [[ELKI]] – Data mining framework in Java with data mining oriented visualization functions.
Line 350 ⟶ 332:
== Reproducible analysis ==
The typical data analysis workflow involves collecting data, running analyses
To address these challenges, it is essential to Different companies and organizations hold data analysis contests to encourage researchers to utilize their data or to solve a particular question using data analysis. A few examples of well-known international data analysis contests are:
* [[Kaggle]]
▲==International data analysis contests==
* [[LTPP International Data Analysis Contest|LTPP data analysis contest]]
▲* Kaggle competition, which is held by [[Kaggle]].<ref>{{cite news|title=The machine learning community takes on the Higgs|url=http://www.symmetrymagazine.org/article/july-2014/the-machine-learning-community-takes-on-the-higgs/|access-date=14 January 2015|newspaper=Symmetry Magazine|date=July 15, 2014|archive-date=16 April 2021|archive-url=https://web.archive.org/web/20210416100455/https://www.symmetrymagazine.org/article/july-2014/the-machine-learning-community-takes-on-the-higgs|url-status=live}}</ref>
▲* [[LTPP International Data Analysis Contest|LTPP data analysis contest]] held by [[FHWA]] and [[ASCE]].<ref name="Nehme 2016-09-29">{{cite web |first = Jean |last = Nehme |date = September 29, 2016 |url = https://www.fhwa.dot.gov/research/tfhrc/programs/infrastructure/pavements/ltpp/2016_2017_asce_ltpp_contest_guidelines.cfm |title = LTPP International Data Analysis Contest |publisher = Federal Highway Administration |access-date = October 22, 2017 |archive-date = October 21, 2017 |archive-url = https://web.archive.org/web/20171021010012/https://www.fhwa.dot.gov/research/tfhrc/programs/infrastructure/pavements/ltpp/2016_2017_asce_ltpp_contest_guidelines.cfm |url-status = live }}</ref><ref>{{cite web |date = May 26, 2016 |url = https://www.fhwa.dot.gov/research/tfhrc/programs/infrastructure/pavements/ltpp/ |title = Data.Gov:Long-Term Pavement Performance (LTPP) |access-date = November 10, 2017 |archive-date = November 1, 2017 |archive-url = https://web.archive.org/web/20171101191727/https://www.fhwa.dot.gov/research/tfhrc/programs/infrastructure/pavements/ltpp/ |url-status = live }}</ref>
==See also==
Line 362 ⟶ 347:
*[[Analytics]]
*[[Augmented Analytics]]
*[[Business intelligence]]
*[[Data presentation architecture]]
*[[Exploratory data analysis]]
*[[Machine learning]]
*[[Multiway data analysis]]
*[[Qualitative research]]
*[[Structured data analysis (statistics)]]
*[[Text mining]]
*[[Unstructured data]]
*[[List of datasets for machine-learning research]]
{{Div col end}}
Line 417 ⟶ 377:
* Juran, Joseph M.; Godfrey, A. Blanton (1999). ''Juran's Quality Handbook, 5th Edition.'' New York: McGraw Hill. {{ISBN|0-07-034003-X}}
* Lewis-Beck, Michael S. (1995). ''Data Analysis: an Introduction'', Sage Publications Inc, {{ISBN|0-8039-5772-6}}
* NIST/SEMATECH (2008) [http://www.itl.nist.gov/div898/handbook/ ''Handbook of Statistical Methods'']
* Pyzdek, T, (2003). ''Quality Engineering Handbook'', {{ISBN|0-8247-4614-7}}
* [[Richard Veryard]] (1984). ''Pragmatic Data Analysis''. Oxford : Blackwell Scientific Publications. {{ISBN|0-632-01311-7}}
Line 429 ⟶ 389:
[[Category:Scientific method]]
[[Category:Computational fields of study]]
[[Category:Data management]]
| |||