09533cam a2200493Ia 4500 ocn894611890 OCoLC 20190328114809.0 m o d cr ||||||||||| 141103s2015 ne a ob 001 0 eng d UKMGB eng pn UKMGB OCLCO OCLCF N$T OPELS N$T OCL YDXCP COO STF B24X7 TEFOD OCLCQ LIV NLE VT2 U3W D6H OCLCQ WYU 016933119 Uk 931603540 1005807911 1008960036 1066447458 9780128006788 (electronic bk.) 0128006781 (electronic bk.) 0128006358 9780128006351 (OCoLC)894611890 (OCoLC)931603540 (OCoLC)1005807911 (OCoLC)1008960036 (OCoLC)1066447458 QA76.9.D343 COM 021030 bisacsh 005.74 006.3/12 23 Fritz, Mike, author. Improving the user experience through practical data analytics : gain meaningful insight and increase your bottom line / [electronic resource] Mike Fritz, Paul D. Berger. Amsterdam ; Boston : Morgan Kaufmann, an imprint of Elsevier, �2015. 1 online resource : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from PDF title page (Ebsco, viewed March 18, 2015). Includes bibliographical references and index. This 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. -- Edited summary from book. Machine generated contents note: ch. 1 Introduction to a variety of useful statistical ideas and techniques -- 1.1. Introduction -- 1.2. Great Normal Curve in the Sky -- 1.2.1. Finding Probabilities of Completion Times or Satisfaction Levels, or Anything Else, on a Normal Curve -- 1.2.1.1. Vignette: how long does it take to hook up DSL Internet service? -- 1.2.2. Finding Completion Times or Satisfaction Levels, or Anything Else, on a Normal Curve -- 1.2.3. Probability Curve for the Mean of Many Results -- 1.2.4. Central Limit Theorem -- 1.3. Confidence Intervals -- 1.3.1. Logic and Meaning of a Confidence Interval -- 1.3.2. Finding a Confidence Interval Using Excel -- 1.3.3. Finding a Confidence Interval Using SPSS -- 1.4. Hypothesis Testing -- 1.4.1. P-Value -- 1.5. Summary -- 1.6. Addendum: Activating "Data Analysis" -- References -- ch. 2 Comparing two designs (or anything else!) using independent sample T-tests -- 2.1. Introduction -- 2.2. Case Study: Comparing Designs at Mademoiselle La La -- 2.3. Comparing Two Means -- 2.4. Independent Samples -- 2.5. Mademoiselle La La Redux -- 2.5.1. Excel -- 2.5.2. SPSS -- 2.6. But What If We Conclude that the Means Aren't Different? -- 2.7. Final Outcome at Mademoiselle La La -- 2.8. Addendum: Confidence Intervals -- 2.9. Summary -- 2.10. Exercise -- Reference -- ch. 3 Comparing two designs (or anything else!) using paired sample T-tests -- 3.1. Introduction -- 3.2. Vignette: How Fast Can You Post a Job at Behemoth.com? -- 3.3. Introduction to Paired Samples -- 3.4. Example of Paired (Two-Sample) T-test -- 3.4.1. Excel -- 3.4.2. SPSS -- 3.5. Behemoth.com Revisited -- 3.6. Addendum: A Mini-Discussion Why the Independent and Paired Tests Need to be Different -- 3.7. Summary -- 3.8. Exercise -- References -- ch. 4 Pass or fail? Binomial-related hypothesis testing and confidence intervals using independent samples -- 4.1. Introduction -- 4.2. Case Study: Is Our Expensive New Search Engine at Behemoth.com Better Than What We Already Have? -- 4.3. Hypothesis Testing Using the Chi-Square Test of Independence or Fisher's Exact Test -- 4.3.1. Excel -- 4.3.2. SPSS -- 4.4. Meanwhile, Back at Behemoth.com -- 4.5. Binomial Confidence Intervals and the Adjusted Wald Method -- 4.6. Summary -- 4.7. Addendum 1: How to Run the Chi-Square Test for Different Sample Sizes -- 4.8. Addendum 2: Comparing More than Two Treatments -- 4.8.1. Excel -- 4.8.2. SPSS -- 4.9. Appendix: Confidence Intervals for all Possible Sample-Proportion Outcomes from N = 1 to N = 15, in Table A.1 -- 4.10. Exercises -- References -- ch. 5 Pass or fail? Binomial-related hypothesis testing and confidence intervals using paired samples -- 5.1. Introduction -- 5.2. Case Study: Can I Register for a Course at Backboard.com? -- 5.3. Hypothesis Testing Using the Cochran Q Test -- 5.3.1. Excel -- 5.3.2. SPSS -- 5.4. Meanwhile, Back at Backboard -- 5.5. Summary -- 5.6. Exercise -- References -- ch. 6 Comparing more than two means: one factor ANOVA with independent samples. Multiple comparison testing with the Newman-Keuls test -- 6.1. Introduction -- 6.2. Case Study: Sophisticated for Whom? -- 6.3. Independent Samples: One-Factor ANOVA -- 6.4. Analyses -- 6.4.1. Excel -- 6.4.2. SPSS -- 6.5. Multiple Comparison Testing -- 6.6. Illustration of the S-N-K Test -- 6.7. Application of the S-N-K to this Result -- 6.8. Discussion of the Result -- 6.8.1. Suppose That Your Only Software Available Is Excel -- 6.9. Meanwhile, Back at Mademoiselle La La -- 6.10. Summary -- 6.11. Exercises -- References -- ch. 7 Comparing more than two means: one factor ANOVA with a within-subject design -- 7.1. Introduction -- 7.2. Case Study: Comparing Multiple Ease-of-Use Ratings at Mademoiselle La La -- 7.3. Comparing Several Means with a Within-Subjects Design -- 7.3.1. Key -- 7.4. Hypotheses for Comparing Several Means -- 7.5. SPSS Analysis -- 7.6. Newman-Keuls Analysis -- 7.7. Excel Analysis -- 7.8. Mademoiselle La La: Let's Fix the Checkout ASAP! -- 7.9. Summary -- 7.10. Exercise -- ch. 8 Comparing more than two means: two factor ANOVA with independent samples; the important role of interaction -- 8.1. Introduction -- 8.2. Case Study: Comparing Age and Gender at Mademoiselle La La -- 8.3. Interaction -- 8.3.1. Interaction -- Definition 1 -- 8.3.2. Interaction -- Definition 2 -- 8.4. Working the Example in SPSS -- 8.5. Meanwhile, Back at Mademoiselle La La -- 8.6. Summary -- 8.7. Exercise -- ch. 9 Can you relate? Correlation and simple linear regression -- 9.1. Introduction -- 9.2. Case Study: Do Recruiters Really Care about Boolean at Behemoth.com? -- 9.3. Correlation Coefficient -- 9.3.1. Excel -- 9.3.2. SPSS -- 9.3.3. CorrelationApplicationtoBehemoth.com -- 9.4. Linear Regression -- 9.4.1. Excel -- 9.4.2. SPSS -- 9.5. Linear Regression Analysis of Behemoth.com Data -- 9.6. Meanwhile, Back at Behemoth -- 9.7. Summary -- 9.8. Addendum: A Quick Discussion of Some Assumptions Implicit in Interpreting the Results -- 9.9. Exercise -- ch. 10 Can you relate in multiple ways? Multiple linear regression and stepwise regression -- 10.1. Introduction -- 10.2. Case Study: Determining the Ideal Search Engine at Behemoth.com -- 10.3. Multiple Regression -- 10.3.1. Excel -- 10.3.2. SPSS -- 10.4. Confidence Interval for the Prediction -- 10.5. BacktoBehemoth.com -- 10.6. Stepwise Regression -- 10.6.1. How Does Stepwise Regression Work? -- 10.6.2. Stepwise Regression Analysis of the Behemoth.com Data -- 10.7. Meanwhile, Back at Behemoth.com -- 10.8. Summary -- 10.9. Exercise -- ch. 11 Will anybody buy? Logistic regression -- 11.1. Introduction -- 11.2. Case Study: Will Anybody Buy at the Charleston Globe? -- 11.3. Logistic Regression -- 11.4. Logistic Regression Using SPSS -- 11.4.1. Computing a Predicted Probability -- 11.4.2. Some Additional Useful Output to Request from SPSS -- 11.4.2.1. Hosmer and Lemeshow goodness-of-fit test -- 11.4.2.2. Finding the predicted probability of a "1" for each data point -- 11.5. CharlestonGlobe.com Survey Data and its Analysis -- 11.5.1. Stepwise Regression Analysis of the CharlestonGlobe.com Data -- 11.5.2. Due Diligence Comparing Stepwise Results To Revised Binary Regression Results -- 11.6. Implications of the Survey-Data Analysis Results -- Back to CharlestonGlobe.com -- 11.6.1. Results Are In: Showtime At CharlestonGlobe.com -- 11.7. Summary -- 11.8. Exercise. Data mining. Quantitative research. COMPUTERS Database Management Data Mining. bisacsh Quantitative research fast (OCoLC)fst01742283 Data mining. fast (OCoLC)fst00887946 Electronic books. Electronic book. Berger, Paul D., 1943- author. Print version: Fritz, Mike. Improving the user experience through practical data analytics : gain meaningful insight and increase your bottom line. Waltham, Massachusetts : Morgan Kaufmann, �2015 xxii, 374 pages 9780128006351 ScienceDirect http://www.sciencedirect.com/science/book/9780128006351 246994 246994