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010 a| 2014952972;

020 a| 0128006358;

020 a| 9780128006351;

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050 00 a| QA76.9.D343b| F75 2015;

082 00 a| 006.3/122| 23;

100 1 a| Fritz, Mike.=| ^A1287620;

245 10 a| Improving the user experience through practical data analyticsh| [electronic resource] :b| gain meaningful insight and increase your bottom line /c| Mike Fritz, Paul D. Berger.;

260 a| Amsterdam ;a| Boston :b| Morgan Kaufmann is an imprint of Elsevier,c| [2015];

300 a| xxii, 374 pages :b| illustrations ;c| 24 cm;

504 a| Includes bibliographical references and index.;

505 00 a| Machine generated contents note:g| ch. 1t| Introduction to a variety of useful statistical ideas and techniques --g| 1.1.t| Introduction --g| 1.2.t| Great Normal Curve in the Sky --g| 1.2.1.t| Finding Probabilities of Completion Times or Satisfaction Levels, or Anything Else, on a Normal Curve --g| 1.2.1.1.t| Vignette: how long does it take to hook up DSL Internet service? --g| 1.2.2.t| Finding Completion Times or Satisfaction Levels, or Anything Else, on a Normal Curve --g| 1.2.3.t| Probability Curve for the Mean of Many Results --g| 1.2.4.t| Central Limit Theorem --g| 1.3.t| Confidence Intervals --g| 1.3.1.t| Logic and Meaning of a Confidence Interval --g| 1.3.2.t| Finding a Confidence Interval Using Excel --g| 1.3.3.t| Finding a Confidence Interval Using SPSS --g| 1.4.t| Hypothesis Testing --g| 1.4.1.t| P-Value --g| 1.5.t| Summary --g| 1.6.t| Addendum: Activating "Data Analysis" --t| References --g| ch. 2t| Comparing two designs (or anything else!) using independent sample T-tests --g| 2.1.t| Introduction --g| 2.2.t| Case Study: Comparing Designs at Mademoiselle La La --g| 2.3.t| Comparing Two Means --g| 2.4.t| Independent Samples --g| 2.5.t| Mademoiselle La La Redux --g| 2.5.1.t| Excel --g| 2.5.2.t| SPSS --g| 2.6.t| But What If We Conclude that the Means Aren't Different? --g| 2.7.t| Final Outcome at Mademoiselle La La --g| 2.8.t| Addendum: Confidence Intervals --g| 2.9.t| Summary --g| 2.10.t| Exercise --t| Reference --g| ch. 3t| Comparing two designs (or anything else!) using paired sample T-tests --g| 3.1.t| Introduction --g| 3.2.t| Vignette: How Fast Can You Post a Job at Behemoth.com? --g| 3.3.t| Introduction to Paired Samples --g| 3.4.t| Example of Paired (Two-Sample) T-test --g| 3.4.1.t| Excel --g| 3.4.2.t| SPSS --g| 3.5.t| Behemoth.com Revisited --g| 3.6.t| Addendum: A Mini-Discussion Why the Independent and Paired Tests Need to be Different --g| 3.7.t| Summary --g| 3.8.t| Exercise --t| References --g| ch. 4t| Pass or fail? Binomial-related hypothesis testing and confidence intervals using independent samples --g| 4.1.t| Introduction --g| 4.2.t| Case Study: Is Our Expensive New Search Engine at Behemoth.com Better Than What We Already Have? --g| 4.3.t| Hypothesis Testing Using the Chi-Square Test of Independence or Fisher's Exact Test --g| 4.3.1.t| Excel --g| 4.3.2.t| SPSS --g| 4.4.t| Meanwhile, Back at Behemoth.com --g| 4.5.t| Binomial Confidence Intervals and the Adjusted Wald Method --g| 4.6.t| Summary --g| 4.7.t| Addendum 1: How to Run the Chi-Square Test for Different Sample Sizes --g| 4.8.t| Addendum 2: Comparing More than Two Treatments --g| 4.8.1.t| Excel --g| 4.8.2.t| SPSS --g| 4.9.t| Appendix: Confidence Intervals for all Possible Sample-Proportion Outcomes from N = 1 to N = 15, in Table A.1 --g| 4.10.t| Exercises --t| References --g| ch. 5t| Pass or fail? Binomial-related hypothesis testing and confidence intervals using paired samples --g| 5.1.t| Introduction --g| 5.2.t| Case Study: Can I Register for a Course at Backboard.com? --g| 5.3.t| Hypothesis Testing Using the Cochran Q Test --g| 5.3.1.t| Excel --g| 5.3.2.t| SPSS --g| 5.4.t| Meanwhile, Back at Backboard --g| 5.5.t| Summary --g| 5.6.t| Exercise --t| References --g| ch. 6t| Comparing more than two means: one factor ANOVA with independent samples. Multiple comparison testing with the Newman-Keuls test --g| 6.1.t| Introduction --g| 6.2.t| Case Study: Sophisticated for Whom? --g| 6.3.t| Independent Samples: One-Factor ANOVA --g| 6.4.t| Analyses --g| 6.4.1.t| Excel --g| 6.4.2.t| SPSS --g| 6.5.t| Multiple Comparison Testing --g| 6.6.t| Illustration of the S-N-K Test --g| 6.7.t| Application of the S-N-K to this Result --g| 6.8.t| Discussion of the Result --g| 6.8.1.t| Suppose That Your Only Software Available Is Excel --g| 6.9.t| Meanwhile, Back at Mademoiselle La La --g| 6.10.t| Summary --g| 6.11.t| Exercises --t| References --g| ch. 7t| Comparing more than two means: one factor ANOVA with a within-subject design --g| 7.1.t| Introduction --g| 7.2.t| Case Study: Comparing Multiple Ease-of-Use Ratings at Mademoiselle La La --g| 7.3.t| Comparing Several Means with a Within-Subjects Design --g| 7.3.1.t| Key --g| 7.4.;

538 a| Mode of access: World Wide Web;

505 00 t| Hypotheses for Comparing Several Means --g| 7.5.t| SPSS Analysis --g| 7.6.t| Newman-Keuls Analysis --g| 7.7.t| Excel Analysis --g| 7.8.t| Mademoiselle La La: Let's Fix the Checkout ASAP! --g| 7.9.t| Summary --g| 7.10.t| Exercise --g| ch. 8t| Comparing more than two means: two factor ANOVA with independent samples; the important role of interaction --g| 8.1.t| Introduction --g| 8.2.t| Case Study: Comparing Age and Gender at Mademoiselle La La --g| 8.3.t| Interaction --g| 8.3.1.t| Interaction -- Definition 1 --g| 8.3.2.t| Interaction -- Definition 2 --g| 8.4.t| Working the Example in SPSS --g| 8.5.t| Meanwhile, Back at Mademoiselle La La --g| 8.6.t| Summary --g| 8.7.t| Exercise --g| ch. 9t| Can you relate? Correlation and simple linear regression --g| 9.1.t| Introduction --g| 9.2.t| Case Study: Do Recruiters Really Care about Boolean at Behemoth.com? --g| 9.3.t| Correlation Coefficient --g| 9.3.1.t| Excel --g| 9.3.2.t| SPSS --g| 9.3.3.t| CorrelationApplicationtoBehemoth.com --g| 9.4.t| Linear Regression --g| 9.4.1.t| Excel --g| 9.4.2.t| SPSS --g| 9.5.t| Linear Regression Analysis of Behemoth.com Data --g| 9.6.t| Meanwhile, Back at Behemoth --g| 9.7.t| Summary --g| 9.8.t| Addendum: A Quick Discussion of Some Assumptions Implicit in Interpreting the Results --g| 9.9.t| Exercise --g| ch. 10t| Can you relate in multiple ways? Multiple linear regression and stepwise regression --g| 10.1.t| Introduction --g| 10.2.t| Case Study: Determining the Ideal Search Engine at Behemoth.com --g| 10.3.t| Multiple Regression --g| 10.3.1.t| Excel --g| 10.3.2.t| SPSS --g| 10.4.t| Confidence Interval for the Prediction --g| 10.5.t| BacktoBehemoth.com --g| 10.6.t| Stepwise Regression --g| 10.6.1.t| How Does Stepwise Regression Work? --g| 10.6.2.t| Stepwise Regression Analysis of the Behemoth.com Data --g| 10.7.t| Meanwhile, Back at Behemoth.com --g| 10.8.t| Summary --g| 10.9.t| Exercise --g| ch. 11t| Will anybody buy? Logistic regression --g| 11.1.t| Introduction --g| 11.2.t| Case Study: Will Anybody Buy at the Charleston Globe? --g| 11.3.t| Logistic Regression --g| 11.4.t| Logistic Regression Using SPSS --g| 11.4.1.t| Computing a Predicted Probability --g| 11.4.2.t| Some Additional Useful Output to Request from SPSS --g| 11.4.2.1.t| Hosmer and Lemeshow goodness-of-fit test --g| 11.4.2.2.t| Finding the predicted probability of a "1" for each data point --g| 11.5.t| CharlestonGlobe.com Survey Data and its Analysis --g| 11.5.1.t| Stepwise Regression Analysis of the CharlestonGlobe.com Data --g| 11.5.2.t| Due Diligence Comparing Stepwise Results To Revised Binary Regression Results --g| 11.6.t| Implications of the Survey-Data Analysis Results -- Back to CharlestonGlobe.com --g| 11.6.1.t| Results Are In: Showtime At CharlestonGlobe.com --g| 11.7.t| Summary --g| 11.8.t| Exercise.;

506 a| Available only to authorized users.;

520 a| Improving the User Experience through Practical Data Analytics is your must-have resource for making UX design decisions based on data, rather than hunches. Authors Fritz and Berger help the UX professional recognize and understand the enormous potential of the ever-increasing user data that is often accumulated as a by-product of routine UX tasks, such as conducting usability tests, launching surveys, or reviewing clickstream information. Then, step-by-step, they explain how to utilize both descriptive and predictive statistical techniques to gain meaningful insight with that data.;

650 0 a| Data mining.=| ^A408868;

650 0 a| Quantitative research.=| ^A1012268;

650 7 a| Data mining.2| fast0| (OCoLC)fst00887946;

650 7 a| Quantitative research.2| fast0| (OCoLC)fst01742283;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Berger, Paul D.,d| 1943-=| ^A935811;

856 40 z| Full text available from eBook - Computer Science 2015 [EBCCS15]u| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Fbook%2F9780128006351;

947 a| (OCoLC)ocn894300476;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

596 a| 1 3 4;

998 a| 5958705;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5958705-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 2/15/2023x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5958705-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 2/15/2023x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5958705-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 2/15/2023x| EBOOKz| JERESOURCE;

Improving the user experience through practical data analytics gain meaningful insight and increase your bottom line / Mike Fritz, Paul D. Berger.

Author/creator	Fritz, Mike
Other author	Berger, Paul D., 1943-
Format	Electronic
Publication Info	Amsterdam ; Boston : Morgan Kaufmann is an imprint of Elsevier, [2015]
Description	xxii, 374 pages : illustrations ; 24 cm
Supplemental Content	Full text available from eBook - Computer Science 2015 [EBCCS15]
Subjects	Data mining. Quantitative research.

Contents	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.
Contents	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.
Abstract	Improving the User Experience through Practical Data Analytics is your must-have resource for making UX design decisions based on data, rather than hunches. Authors Fritz and Berger help the UX professional recognize and understand the enormous potential of the ever-increasing user data that is often accumulated as a by-product of routine UX tasks, such as conducting usability tests, launching surveys, or reviewing clickstream information. Then, step-by-step, they explain how to utilize both descriptive and predictive statistical techniques to gain meaningful insight with that data.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2014952972
ISBN	0128006358
ISBN	9780128006351

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Improving the user experience through practical data analytics gain meaningful insight and increase your bottom line / Mike Fritz, Paul D. Berger.

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