000 09533cam a2200493Ia 4500
001 ocn894611890
003 OCoLC
005 20190328114809.0
006 m o d
007 cr |||||||||||
008 141103s2015 ne a ob 001 0 eng d
040 _aUKMGB
_beng
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016 7 _a016933119
_2Uk
019 _a931603540
_a1005807911
_a1008960036
_a1066447458
020 _a9780128006788
_q(electronic bk.)
020 _a0128006781
_q(electronic bk.)
020 _z0128006358
020 _z9780128006351
035 _a(OCoLC)894611890
_z(OCoLC)931603540
_z(OCoLC)1005807911
_z(OCoLC)1008960036
_z(OCoLC)1066447458
050 4 _aQA76.9.D343
072 7 _aCOM
_x021030
_2bisacsh
082 0 4 _a005.74
_a006.3/12
_223
100 1 _aFritz, Mike,
_eauthor.
245 1 0 _aImproving the user experience through practical data analytics : gain meaningful insight and increase your bottom line /
_h[electronic resource]
_cMike Fritz, Paul D. Berger.
264 1 _aAmsterdam ;
_aBoston :
_bMorgan Kaufmann, an imprint of Elsevier,
_c�2015.
300 _a1 online resource :
_billustrations
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
588 0 _aOnline resource; title from PDF title page (Ebsco, viewed March 18, 2015).
504 _aIncludes bibliographical references and index.
520 _aThis book shows you how to make UX design decisions based on data-not hunches. The authors recognize the enormous potential of user data that is collected as a natural by-product of routine UX research methods, including moderated usability tests, unmoderated usability tests, surveys, and contextual inquiries. They explain how to utilize both descriptive and predictive statistical techniques to gain meaningful insight with that data. By mastering the use of these techniques, you'll delight your users, increase your bottom line and gain a powerful competitive advantage for your company-and yourself. --
_cEdited summary from book.
505 0 0 _gMachine generated contents note:
_gch. 1
_tIntroduction to a variety of useful statistical ideas and techniques --
_g1.1.
_tIntroduction --
_g1.2.
_tGreat Normal Curve in the Sky --
_g1.2.1.
_tFinding Probabilities of Completion Times or Satisfaction Levels, or Anything Else, on a Normal Curve --
_g1.2.1.1.
_tVignette: how long does it take to hook up DSL Internet service? --
_g1.2.2.
_tFinding Completion Times or Satisfaction Levels, or Anything Else, on a Normal Curve --
_g1.2.3.
_tProbability Curve for the Mean of Many Results --
_g1.2.4.
_tCentral Limit Theorem --
_g1.3.
_tConfidence Intervals --
_g1.3.1.
_tLogic and Meaning of a Confidence Interval --
_g1.3.2.
_tFinding a Confidence Interval Using Excel --
_g1.3.3.
_tFinding a Confidence Interval Using SPSS --
_g1.4.
_tHypothesis Testing --
_g1.4.1.
_tP-Value --
_g1.5.
_tSummary --
_g1.6.
_tAddendum: Activating "Data Analysis" --
_tReferences --
_gch. 2
_tComparing two designs (or anything else!) using independent sample T-tests --
_g2.1.
_tIntroduction --
_g2.2.
_tCase Study: Comparing Designs at Mademoiselle La La --
_g2.3.
_tComparing Two Means --
_g2.4.
_tIndependent Samples --
_g2.5.
_tMademoiselle La La Redux --
_g2.5.1.
_tExcel --
_g2.5.2.
_tSPSS --
_g2.6.
_tBut What If We Conclude that the Means Aren't Different? --
_g2.7.
_tFinal Outcome at Mademoiselle La La --
_g2.8.
_tAddendum: Confidence Intervals --
_g2.9.
_tSummary --
_g2.10.
_tExercise --
_tReference --
_gch. 3
_tComparing two designs (or anything else!) using paired sample T-tests --
_g3.1.
_tIntroduction --
_g3.2.
_tVignette: How Fast Can You Post a Job at Behemoth.com? --
_g3.3.
_tIntroduction to Paired Samples --
_g3.4.
_tExample of Paired (Two-Sample) T-test --
_g3.4.1.
_tExcel --
_g3.4.2.
_tSPSS --
_g3.5.
_tBehemoth.com Revisited --
_g3.6.
_tAddendum: A Mini-Discussion Why the Independent and Paired Tests Need to be Different --
_g3.7.
_tSummary --
_g3.8.
_tExercise --
_tReferences --
_gch. 4
_tPass or fail? Binomial-related hypothesis testing and confidence intervals using independent samples --
_g4.1.
_tIntroduction --
_g4.2.
_tCase Study: Is Our Expensive New Search Engine at Behemoth.com Better Than What We Already Have? --
_g4.3.
_tHypothesis Testing Using the Chi-Square Test of Independence or Fisher's Exact Test --
_g4.3.1.
_tExcel --
_g4.3.2.
_tSPSS --
_g4.4.
_tMeanwhile, Back at Behemoth.com --
_g4.5.
_tBinomial Confidence Intervals and the Adjusted Wald Method --
_g4.6.
_tSummary --
_g4.7.
_tAddendum 1: How to Run the Chi-Square Test for Different Sample Sizes --
_g4.8.
_tAddendum 2: Comparing More than Two Treatments --
_g4.8.1.
_tExcel --
_g4.8.2.
_tSPSS --
_g4.9.
_tAppendix: Confidence Intervals for all Possible Sample-Proportion Outcomes from N = 1 to N = 15, in Table A.1 --
_g4.10.
_tExercises --
_tReferences --
_gch. 5
_tPass or fail? Binomial-related hypothesis testing and confidence intervals using paired samples --
_g5.1.
_tIntroduction --
_g5.2.
_tCase Study: Can I Register for a Course at Backboard.com? --
_g5.3.
_tHypothesis Testing Using the Cochran Q Test --
_g5.3.1.
_tExcel --
_g5.3.2.
_tSPSS --
_g5.4.
_tMeanwhile, Back at Backboard --
_g5.5.
_tSummary --
_g5.6.
_tExercise --
_tReferences --
_gch. 6
_tComparing more than two means: one factor ANOVA with independent samples. Multiple comparison testing with the Newman-Keuls test --
_g6.1.
_tIntroduction --
_g6.2.
_tCase Study: Sophisticated for Whom? --
_g6.3.
_tIndependent Samples: One-Factor ANOVA --
_g6.4.
_tAnalyses --
_g6.4.1.
_tExcel --
_g6.4.2.
_tSPSS --
_g6.5.
_tMultiple Comparison Testing --
_g6.6.
_tIllustration of the S-N-K Test --
_g6.7.
_tApplication of the S-N-K to this Result --
_g6.8.
_tDiscussion of the Result --
_g6.8.1.
_tSuppose That Your Only Software Available Is Excel --
_g6.9.
_tMeanwhile, Back at Mademoiselle La La --
_g6.10.
_tSummary --
_g6.11.
_tExercises --
_tReferences --
_gch. 7
_tComparing more than two means: one factor ANOVA with a within-subject design --
_g7.1.
_tIntroduction --
_g7.2.
_tCase Study: Comparing Multiple Ease-of-Use Ratings at Mademoiselle La La --
_g7.3.
_tComparing Several Means with a Within-Subjects Design --
_g7.3.1.
_tKey --
_g7.4.
_tHypotheses for Comparing Several Means --
_g7.5.
_tSPSS Analysis --
_g7.6.
_tNewman-Keuls Analysis --
_g7.7.
_tExcel Analysis --
_g7.8.
_tMademoiselle La La: Let's Fix the Checkout ASAP! --
_g7.9.
_tSummary --
_g7.10.
_tExercise --
_gch. 8
_tComparing more than two means: two factor ANOVA with independent samples; the important role of interaction --
_g8.1.
_tIntroduction --
_g8.2.
_tCase Study: Comparing Age and Gender at Mademoiselle La La --
_g8.3.
_tInteraction --
_g8.3.1.
_tInteraction -- Definition 1 --
_g8.3.2.
_tInteraction -- Definition 2 --
_g8.4.
_tWorking the Example in SPSS --
_g8.5.
_tMeanwhile, Back at Mademoiselle La La --
_g8.6.
_tSummary --
_g8.7.
_tExercise --
_gch. 9
_tCan you relate? Correlation and simple linear regression --
_g9.1.
_tIntroduction --
_g9.2.
_tCase Study: Do Recruiters Really Care about Boolean at Behemoth.com? --
_g9.3.
_tCorrelation Coefficient --
_g9.3.1.
_tExcel --
_g9.3.2.
_tSPSS --
_g9.3.3.
_tCorrelationApplicationtoBehemoth.com --
_g9.4.
_tLinear Regression --
_g9.4.1.
_tExcel --
_g9.4.2.
_tSPSS --
_g9.5.
_tLinear Regression Analysis of Behemoth.com Data --
_g9.6.
_tMeanwhile, Back at Behemoth --
_g9.7.
_tSummary --
_g9.8.
_tAddendum: A Quick Discussion of Some Assumptions Implicit in Interpreting the Results --
_g9.9.
_tExercise --
_gch. 10
_tCan you relate in multiple ways? Multiple linear regression and stepwise regression --
_g10.1.
_tIntroduction --
_g10.2.
_tCase Study: Determining the Ideal Search Engine at Behemoth.com --
_g10.3.
_tMultiple Regression --
_g10.3.1.
_tExcel --
_g10.3.2.
_tSPSS --
_g10.4.
_tConfidence Interval for the Prediction --
_g10.5.
_tBacktoBehemoth.com --
_g10.6.
_tStepwise Regression --
_g10.6.1.
_tHow Does Stepwise Regression Work? --
_g10.6.2.
_tStepwise Regression Analysis of the Behemoth.com Data --
_g10.7.
_tMeanwhile, Back at Behemoth.com --
_g10.8.
_tSummary --
_g10.9.
_tExercise --
_gch. 11
_tWill anybody buy? Logistic regression --
_g11.1.
_tIntroduction --
_g11.2.
_tCase Study: Will Anybody Buy at the Charleston Globe? --
_g11.3.
_tLogistic Regression --
_g11.4.
_tLogistic Regression Using SPSS --
_g11.4.1.
_tComputing a Predicted Probability --
_g11.4.2.
_tSome Additional Useful Output to Request from SPSS --
_g11.4.2.1.
_tHosmer and Lemeshow goodness-of-fit test --
_g11.4.2.2.
_tFinding the predicted probability of a "1" for each data point --
_g11.5.
_tCharlestonGlobe.com Survey Data and its Analysis --
_g11.5.1.
_tStepwise Regression Analysis of the CharlestonGlobe.com Data --
_g11.5.2.
_tDue Diligence Comparing Stepwise Results To Revised Binary Regression Results --
_g11.6.
_tImplications of the Survey-Data Analysis Results -- Back to CharlestonGlobe.com --
_g11.6.1.
_tResults Are In: Showtime At CharlestonGlobe.com --
_g11.7.
_tSummary --
_g11.8.
_tExercise.
650 0 _aData mining.
650 0 _aQuantitative research.
650 7 _aCOMPUTERS
_xDatabase Management
_xData Mining.
_2bisacsh
650 7 _aQuantitative research
_2fast
_0(OCoLC)fst01742283
650 7 _aData mining.
_2fast
_0(OCoLC)fst00887946
655 4 _aElectronic books.
655 0 _aElectronic book.
700 1 _aBerger, Paul D.,
_d1943-
_eauthor.
776 0 8 _iPrint version:
_aFritz, Mike.
_tImproving the user experience through practical data analytics : gain meaningful insight and increase your bottom line.
_dWaltham, Massachusetts : Morgan Kaufmann, �2015
_hxxii, 374 pages
_z9780128006351
856 4 0 _3ScienceDirect
_uhttp://www.sciencedirect.com/science/book/9780128006351
999 _c246994
_d246994