TY - BOOK ID - 966015 TI - Nonparametric simple regression : smoothing scatterplots. PY - 2000 VL - 130 SN - 0761915850 1412985307 1452214506 0585212260 9780761915850 PB - Thousand Oaks (Calif.) Sage DB - UniCat KW - Mathematical statistics KW - QA 278.2 .H67 2000 Regression analysis. Correlation analysis KW - Regression analysis KW - Nonparametric statistics KW - #SBIB:303H10 KW - #PBIB:2003.3 KW - Analysis, Regression KW - Linear regression KW - Regression modeling KW - Multivariate analysis KW - Structural equation modeling KW - Distribution-free statistics KW - Statistics, Distribution-free KW - Statistics, Nonparametric KW - Methoden en technieken: algemene handboeken en reeksen KW - Mathematics KW - Physical Sciences & Mathematics KW - Mathematical Statistics KW - Nonparametric statistics. KW - Regression analysis. KW - Regression Analysis UR - https://www.unicat.be/uniCat?func=search&query=sysid:966015 AB - While regression analysis traces the dependence of the distribution of a response variable to see if it bears a particular (linear) relationship to one or more of the predictors, nonparametric regression analysis makes minimal assumptions about the form of relationship between the average response and the predictors. This makes nonparametric regression a more useful technique for analyzing data in which there are several predictors that may combine additively to influence the response. (An example could be something like birth order/gender/and temperament on achievement motivation). Unfortunately, researchers have not had accessible information on nonparametric regression analysisuntil now. Beginning with presentation of nonparametric regression based on dividing the data into bins and averaging the response values in each bin, Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit. The book concludes with ways nonparametric regression can be generalized to logit, probit, and Poisson regression. ER -