LEADER 05211nam 2200613 a 450 001 9910677262103321 005 20230828222742.0 010 $a1-283-44598-0 010 $a9786613445988 010 $a1-118-27441-5 010 $a0-470-05265-1 035 $a(CKB)2550000000079532 035 $a(EBL)708316 035 $a(OCoLC)774271096 035 $a(SSID)ssj0000588734 035 $a(PQKBManifestationID)11351992 035 $a(PQKBTitleCode)TC0000588734 035 $a(PQKBWorkID)10649377 035 $a(PQKB)11737357 035 $a(MiAaPQ)EBC708316 035 $a(EXLCZ)992550000000079532 100 $a20060309d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied regression modeling$b[electronic resource] $ea business approach /$fIain Pardoe 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (326 p.) 300 $aDescription based upon print version of record. 311 $a0-471-97033-6 320 $aIncludes bibliographical references and index. 327 $aApplied Regression Modeling: A Business Approach; Contents; Preface; Acknowledgments; Introduction; 1.1 Statistics in business; 1.2 Learning statistics; 1 Foundations; 1.1 Identifying and summarizing data; 1.2 Population distributions; 1.3 Selecting individuals at random-probability; 1.4 Random sampling; 1.4.1 Central limit theorem-normal version; 1.4.2 Student's t-distribution; 1.4.3 Central limit theorem-t version; 1.5 Interval estimation; 1.6 Hypothesis testing; 1.6.1 The rejection region method; 1.6.2 The p-value method; 1.6.3 Hypothesis test errors; 1.7 Random errors and prediction 327 $a1.8 Chapter summaryProblems; 2 Simple linear regression; 2.1 Probability model for X and Y; 2.2 Least squares criterion; 2.3 Model evaluation; 2.3.1 Regression standard error; 2.3.2 Coefficient of determination-R2; 2.3.3 Slope parameter; 2.4 Model assumptions; 2.4.1 Checking the model assumptions; 2.5 Model interpretation; 2.6 Estimation and prediction; 2.6.1 Confidence interval for the population mean, E(Y); 2.6.2 Prediction interval for an individual Y-value; 2.7 Chapter summary; 2.7.1 Review example; Problems; 3 Multiple linear regression; 3.1 Probability model for (X1 ,X2,...) and Y 327 $a3.2 Least squares criterion3.3 Model evaluation; 3.3.1 Regression standard error; 3.3.2 Coefficient of determination-R2; 3.3.3 Regression parameters-global usefulness test; 3.3.4 Regression parameters-nested model test; 3.3.5 Regression parameters-individual tests; 3.4 Model assumptions; 3.4.1 Checking the model assumptions; 3.5 Model interpretation; 3.6 Estimation and prediction; 3.6.1 Confidence interval for the population mean, E(Y); 3.6.2 Prediction interval for an individual Y-value; 3.7 Chapter summary; Problems; 4 Regression model building I; 4.1 Transformations 327 $a4.1.1 Natural logarithm transformation for predictors4.1.2 Polynomial transformation for predictors; 4.1.3 Reciprocal transformation for predictors; 4.1.4 Natural logarithm transformation for the response; 4.1.5 Transformations for the response and predictors; 4.2 Interactions; 4.3 Qualitative predictors; 4.3.1 Qualitative predictors with two levels; 4.3.2 Qualitative predictors with three or more levels; 4.4 Chapter summary; Problems; 5 Regression model building II; 5.1 Influential points; 5.1.1 Outliers; 5.1.2 Leverage; 5.1.3 Cook's distance; 5.2 Regression pitfalls; 5.2.1 Autocorrelation 327 $a5.2.2 Multicollinearity5.2.3 Excluding important predictor variables; 5.2.4 Overfitting; 5.2.5 Extrapolation; 5.2.6 Missing Data; 5.3 Model building guidelines; 5.4 Model interpretation using graphics; 5.5 Chapter summary; Problems; 6 Case studies; 6.1 Home prices; 6.1.1 Data description; 6.1.2 Exploratory data analysis; 6.1.3 Regression model building; 6.1.4 Results and conclusions; 6.1.5 Further questions; 6.2 Vehicle fuel efficiency; 6.2.1 Data description; 6.2.2 Exploratory data analysis; 6.2.3 Regression model building; 6.2.4 Results and conclusions; 6.2.5 Further questions; 7 Extensions 327 $a7.1 Generalized linear models 330 $aAn applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regr 606 $aRegression analysis 606 $aStatistics 615 0$aRegression analysis. 615 0$aStatistics. 676 $a519.5/36 676 $a519.536 700 $aPardoe$b Iain$f1970-$0525053 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910677262103321 996 $aApplied regression modeling$9822811 997 $aUNINA