LEADER 01516nam 2200397 450 001 000015560 005 20050718115600.0 010 $a3-540-06293-9 100 $a20030730d1976----km-y0itay0103----ba 101 0 $ager 102 $aDE 200 1 $aVorlesungen uber die theorie der polyeder$eunter Einschluss der Elemente der Topologie$fE. Steinitz, H. Rademacher 210 $aBerlin [etc.]$cSpringer$d1976 215 $aVIII, 351 p.$cill.$d25 cm. 225 2 $aGrundlehren der mathematischen Wissenschaften$v41 410 0$12001$aGrundlehren der mathematischen Wissenschaften 606 $aTopologia 606 $aPoliedri 676 $a516.15$v(21. ed.)$9Geometria. Configurazioni geometriche 691 $a51M20$9Geometry. Real and complex geometry. 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Janke, Frederick Tinsley 210 $aHoboken, NJ $cWiley$dc2005 215 $a1 online resource (600 p.) 300 $aDescription based upon print version of record. 311 08$a9780471662594 311 08$a0471662593 320 $aIncludes bibliographical references (p. 575-579) and index. 327 $aIntroduction to Linear Models and Statistical Inference; Contents; INTRODUCTION: STATISTICAL QUESTIONS; 1. DATA: PLOTS AND LOCATION; 1.1 Plot the Data; 1.2 Measures of Location: Single Observations; 1.3 Measures of Location: Paired Observations; 1.4 Robust Measures of Location: Paired Observations; 1.5 Linear Algebra for Least Squares (Optional); Exercises; 2. DATA: DISPERSION AND CORRELATION; 2.1 Measures of Dispersion: Single Observations; 2.2 Measures of Dispersion: Paired Observations; 2.3 Robust Measures of Dispersion: Paired Observations; 2.4 Analysis of Variance 327 $a2.5 Measures of Linear Relationship2.6 Analysis of Variance using Linear Algebra (Optional); Exercises; 3. RANDOM VARIABLES: PROBABILITY AND DENSITY; 3.1 Random Variables; 3.2 Probability; 3.3 Finding Probabilities; 3.4 Densities: Discrete Random Variables; 3.5 Densities: Continuous Random Variables; 3.6 Binomial Random Variables; 3.7 Normal Random Variables; Exercises; 4. RANDOM VARIABLES: EXPECTATION AND VARIANCE; 4.1 Expectation of a Random Variable; 4.2 Properties of Expectation; 4.3 Independent Random Variables; 4.4 Variance of a Random Variable; 4.5 Correlation Coefficient 327 $a4.6 Properties of Normal Random Variables4.7 Linear Algebra for Random Vectors (Optional); Exercises; 5. STATISTICAL INFERENCE; 5.1 Populations and Samples; 5.2 Unbiases Estimators; 5.3 Distribution of X; 5.4 Confidence Intervals; 5.5 Hypothesis Testing; 5.6 General Inference Problem; 5.7 The Runs Test for Randomness; 5.8 Testing for Normality; 5.9 Linear Algebra for Inference (Optional); Exercises; 6. SIMPLE LINEAR MODELS; 6.1 Basics of the Simple Linear Model; 6.2 Estimators for the Simple Linear Model; 6.3 Inference for the Slope; 6.4 Testing the Hypothesis b = 0 327 $a6.5 Coefficient of Determination6.6 Inference for the Intercept; 6.7 Inference for the Variance; 6.8 Prediction Intervals; 6.9 Regression Through the Origin; 6.10 Earthquake Example; 6.11 Linear Algebra: The Simple Linear Model (Optional); Exercises; 7. LINEAR MODEL DIAGNOSTICS; 7.1 Residual Plots; 7.2 Standardized Residuals; 7.3 Testing Assumption 1: Is X a Valid Predictor?; 7.4 Testing Assumption 2: Does E(ei) = 0 for all i?; 7.5 Testing Assumption 2: Does Var(ei) = s2 for all i?; 7.6 Testing Assumption 3: Are the Errors Independent?; 7.7 Testing Assumption 4: Are the Errors Normal? 327 $a7.8 Distribution of the Residuals7.9 Linear Algebra for Residuals (Optional); Exercises; 8. LINEAR MODELS: TWO INDEPENDENT VARIABLES; 8.1 Calculating Parameters; 8.2 Analysis of Variance; 8.3 The Effects of Independent Variables; 8.4 Inference for the Bivariate Linear Model; 8.5 Diagnostics for the Bivariate Linear Model; 8.6 Linear Algebra: Bivariate Linear Model (Optional); Exercises; 9. LINEAR MODELS: SEVERAL INDEPENDENT VARIABLES; 9.1 A Multivariate Example; 9.2 Analysis of Variance; 9.3 Inference for the Multivariate Linear Model; 9.4 Selecting Predictors 327 $a9.5 Diagnostics for the Multivariate Model 330 $aA multidisciplinary approach that emphasizes learning by analyzing real-world data setsThis book is the result of the authors' hands-on classroom experience and is tailored to reflect how students best learn to analyze linear relationships. The text begins with the introduction of four simple examples of actual data sets. These examples are developed and analyzed throughout the text, and more complicated examples of data sets are introduced along the way. Taking a multidisciplinary approach, the book traces the conclusion of the analyses of data sets taken from geology, biology, econom 606 $aLinear models (Statistics)$vTextbooks 615 0$aLinear models (Statistics) 676 $a519.5/4 700 $aJanke$b Steven J.$f1947-$0922086 701 $aTinsley$b Frederick$f1951-$0922087 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143571903321 996 $aIntroduction to linear models and statistical inference$92069199 997 $aUNINA