LEADER 05455nam 2200661Ia 450 001 9910144694803321 005 20170810195445.0 010 $a1-282-30751-7 010 $a9786612307515 010 $a0-470-31694-2 010 $a0-470-31778-7 035 $a(CKB)1000000000687551 035 $a(EBL)469129 035 $a(OCoLC)264624039 035 $a(SSID)ssj0000334792 035 $a(PQKBManifestationID)11929213 035 $a(PQKBTitleCode)TC0000334792 035 $a(PQKBWorkID)10271252 035 $a(PQKB)11426332 035 $a(MiAaPQ)EBC469129 035 $a(PPN)159329159 035 $a(EXLCZ)991000000000687551 100 $a19990119d1999 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied regression including computing and graphics$b[electronic resource] /$fR. Dennis Cook, Sanford Weisberg 210 $aNew York $cWiley$d1999 215 $a1 online resource (632 p.) 225 0 $aWiley series in probability and statistics. Texts and references section 300 $a"A Wiley-Interscience publication." 311 $a0-471-31711-X 320 $aIncludes bibliographical references (p. 571-578) and indexes. 327 $aApplied Regression Including Computing and Graphics; Contents; Preface; PART I INTRODUCTION; 1 Looking Forward and Back; 1.1 Example: Haystack Data; 1.2 Example: Bluegill Data; 1.3 Loading Data into Arc; 1.4 Numerical Summaries; 1.4.1 Display Summaries; 1.4.2 Command Line; 1.4.3 Displaying Data; 1.4.4 Saving Output to a File and Printing; 1 .5 Graphical Summaries; 1.5.1Histograms; 1.5.2 Boxplots; 1.6.Bringing in the Population; 1.6. I The Density Function; 1.6.2 Normal Distribution; I .6.3 Computing Normal Quantiles; 1.6.4 Computing Normal Probabilities; 1.6.5 Boxplots of Normal Data 327 $a1.6.6 The Sampling Distribution of the Mean1.7 Inference; 1.7.1 Sample Mean; 1.7.2 Confidence Interval for the Mean; 1.7.3 Probability of a Record Bluegill; 1.8 Complements; Problems; 2 Introduction to Regression; 2.1 Using Boxplots to Study Length \ Age; 2.2 Using a Scatterplot to Study Length \ Age; 2.3 Mouse Modes; 2.3.1 Show Coordinates Mouse Mode; 2.3.2 Slicing Mode; 2.3.3 Brushing Mode; 2.4 Characterizing Length\ Age; 2.5 Mean and Variance Functions; 2.5.1 Mean Function; 2.5.2 Variance Function; 2.6 Highlights; 2.7 Complements; Problems; 3 Introduction to Smoothing 327 $a3.1 Slicing a Scatterplot3.2 Estimating E(y I x) by Slicing; 3.3 Estimating E(y Ix) by Smoothing; 3.4 Checking a Theory; 3.5 Boxplots; 3.6 Snow Geese; 3.6.1 Snow Goose Regression; 3.6.2 Mean Function; 3.6.3 Variance Function; 3.7 Complements; Problems; 4 Bivariate Distributions; 4.1 General Bivariate Distributions; 4.1.1 Bivariate Densities; 4.1.2 Connecting with Regression; 4.1.3 Independence; 4.1.4 Covariance; 4.1.5 Correlation Coefficient; 4.2 Bivariate Normal Distribution; 4.2.1 Correlation Coefficient in Normal Populations; 4.2.2 Correlation Coefficient in Non-normal Populations 327 $a4.3.Regression in Bivariate Normal Populations4.3.1 Mean Function; 4.3.2 Mean Function in Standardized Variables; 4.3.3 Mean Function as a Straight Line; 4.3.4 Variance Function; 4.4 Smoothing Bivariate Normal Data; 4.5 Complements; 4.5.1 Confidence Interval for a Correlation; 4.5.2 References; Problems; 5 Two-Dimensional Plots; 5.1 Aspect Ratio and Focusing; 5.2 Power Transformations; 5.3 Thinking about Power Transformations; 5.4 Log Transformations; 5.5 Showing Labels and Coordinates; 5.6 Linking Plots; 5.7 Point Symbols and Colors; 5.8 Brushing; 5.9 Name Lists; 5.10 Probability Plots 327 $a5.11 ComplementsProblems; PART II. TOOLS; 6 Simple Linear Regression; 6.1 Simple Linear Regression; 6.2 Least Squares Estimation; 6.2.1 Notation; 6.2.2 The Least Squares Criterion; 6.2.3 Ordinary Least Squares Estimators; 6.2.4 More on Sample Correlation; 6.2.5 Some Properties of Least Squares Estimates; 6.2.6 Estimating the Common Variance, (T*; 6.2.7 Summary; 6.3 Using Arc; 6.3.1 Interpreting the Intercept; 6.4 Inference; 6.4.1 Inferences about Parameters; 6.4.2 Estimating Population Means; 6.4.3 Prediction; 6.5 Forbes' Experiments, Revisited; 6.6 Model Comparison; 6.6.1 Models 327 $a6.6.2 Analysis of Variance 330 $aA step-by-step guide to computing and graphics in regression analysisIn this unique book, leading statisticians Dennis Cook and Sanford Weisberg expertly blend regression fundamentals and cutting-edge graphical techniques. They combine and up- date most of the material from their widely used earlier work, An Introduction to Regression Graphics, and Weisberg's Applied Linear Regression; incorporate the latest in statistical graphics, computing, and regression models; and wind up with a modern, fully integrated approach to one of the most important tools of data analysis.In 23 co 410 0$aWiley Series in Probability and Statistics 606 $aRegression analysis 606 $aMultivariate analysis 615 0$aRegression analysis. 615 0$aMultivariate analysis. 676 $a519.5 676 $a519.536 700 $aCook$b R. Dennis$089150 701 $aWeisberg$b Sanford$f1947-$0104044 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910144694803321 996 $aApplied regression including computing and graphics$9509083 997 $aUNINA LEADER 03623nam 2200661 450 001 9910828095403321 005 20230125183102.0 010 $a1-63157-121-4 035 $a(CKB)3710000000329621 035 $a(EBL)1911815 035 $a(OCoLC)900878630 035 $a(CaBNVSL)swl00404634 035 $a(Au-PeEL)EBL1911815 035 $a(CaPaEBR)ebr11007926 035 $a(CaONFJC)MIL688591 035 $a(CaSebORM)9781631571206 035 $a(MiAaPQ)EBC1911815 035 $a(EXLCZ)993710000000329621 100 $a20150126d2015 fy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aBusiness intelligence and data mining /$fAnil K. Maheshwari 205 $aFirst edition. 210 1$aNew York, New York (222 East 46th Street, New York, NY 10017) :$cBusiness Expert Press,$d2015. 215 $a1 online resource (180 p.) 225 1 $aBig data and business analytics collection,$x2333-6757 300 $aDescription based upon print version of record. 311 $a1-63157-120-6 320 $aIncludes bibliographical references (pages 157-158) and index. 327 $a1. Wholeness of business intelligence and data mining -- 2. Business intelligence concepts and applications -- 3. Data warehousing -- 4. Data mining -- 5. Decision trees -- 6. Regression -- 7. Artificial neural networks -- 8. Cluster analysis -- 9. Association rule mining -- 10. Text mining -- 11. Web mining -- 12. Big data -- 13. Data modeling primer -- Additional resources -- Index. 330 3 $aBusiness is the act of doing something productive to serve someone's needs, and thus earn a living, and make the world a better place. Business activities are recorded on paper or using electronic media, and then these records become data. There is more data from customers' responses and on the industry as a whole. All this data can be analyzed and mined using special tools and techniques to generate patterns and intelligence, which reflect how the business is functioning. These ideas can then be fed back into the business so that it can evolve to become more effective and efficient in serving customer needs. And the cycle continues on. Business intelligence includes tools and techniques for data gathering, analysis, and visualization for helping with executive decision making in any industry. Data mining includes statistical and machine-learning techniques to build decision-making models from raw data. Data mining techniques covered in this book include decision trees, regression, artificial neural networks, cluster analysis, and many more. Text mining, web mining, and big data are also covered in an easy way. A primer on data modeling is included for those uninitiated in this topic. 410 0$aBig data and business analytics collection.$x2333-6757 606 $aBusiness information services 606 $aData mining 606 $aBusiness intelligence 610 $aData Analytics 610 $aData Mining 610 $aBusiness Intelligence 610 $aDecision Trees 610 $aRegression 610 $aNeural Networks 610 $aCluster analysis 610 $aAssociation rules 615 0$aBusiness information services. 615 0$aData mining. 615 0$aBusiness intelligence. 676 $a658.4038 700 $aMaheshwari$b Anil$f1949-,$01658032 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910828095403321 996 $aBusiness intelligence and data mining$94011806 997 $aUNINA