SPSS 14.0 Advanced Statistical Procedures Companion
SPSS 14.0 Advanced Statistical Procedures Companion
复制标题
DOI：
--
复制
发表时间：
2005-01
期刊：
影响因子：
--
通讯作者：
M. Norusis
中科院分区：
文献类型：
--
作者：
M. Norusis
SPSS 14.0 Advanced Statistical Procedures Companion：Chapters 1.对数线性分析中的模型选择饱和模型中的模型公式参数假设检验收敛性拟合优度检验分层模型生成类向后淘汰模型选择。2. Logit对数线性分析。二分logit模型对数线性表示参数估计拟合优度统计离散度和关联的测量多分logit模型解释参数检查残差引入协变量。3.多项Logistic回归模型和个体效应的基线logits似然比检验评估模型计算预测概率分类表拟合优度检验残差伪R方测度过度离散模型选择匹配病例对照研究. 4.有序回归建模累积计数参数估计平行线检验模型拟合观察和预期计数关联强度测量分类病例链接函数拟合异方差概率单位模型拟合位置和尺度参数. 5.概率回归Probit和logit反应模型有效剂量的置信区间比较组比较相对效力估计自然反应率多种刺激。6. Kaplan-Meier生存分析。计算生存时间，估计生存函数，条件生存概率，和累积生存概率，绘制生存函数，比较生存函数，分层比较。7.生命表计算生存概率，假设失访观察，绘制生存函数，比较生存函数。8.考克斯回归。模型比例风险假设编码分类变量解释回归系数基线风险和累积生存率模型的全局检验检查比例风险假设分层对数减对数生存图识别有影响的病例检查残差部分（Schoenfeld）残差鞅残差变量选择方法时间相关协变量指定时间相关协变量计算分段时间-相关协变量用拟合条件logistic回归模型的时间相关协变量检验比例风险假设。9.方差分量。因子、效应和模型单向分类估计方法的模型负方差估计双向分类嵌套设计模型使用混合模型方法的单变量重复测量分析分布假设估计方法。10.线性混合模型背景无条件随机效应模型分层模型随机系数模型学校水平和个人水平协变量三水平分层模型重复测量选择残差协方差结构. 11.非线性回归非线性模型转换非线性模型本质上非线性模型拟合Logistic人口增长模型找到初始值近似的置信区间参数自举估计初始值从以前的分析线性近似计算问题非线性回归指定一个分段模型的共同模型。12.两阶段最小二乘回归。需求-价格-收入经济模型的普通最小二乘反馈估计及相关误差的两阶段最小二乘估计。13.加权最小二乘回归。诊断问题，估计权重，检查对数似然函数，WLS解决方案，估计权重，从线性回归过程中重复诊断。14.多维缩放。数据、模型和多维标度分析MDS中分析的数据的性质测量数据的级别数据的形状数据的条件性数据缺失数据的多变量数据经典MDS欧几里得模型CMDS的详细信息复制MDS加权MDS几何加权欧几里得模型的代数加权欧几里得模型的矩阵代数加权欧几里得模型的代数怪异指数扁平化权重。
SPSS 14.0 Advanced Statistical Procedures Companion: Chapters 1. Model Selection in Loglinear Analysis. Model formulation parameters in saturated models hypothesis testing convergence goodness-of-fit tests hierarchical models generating classes model selection with backward elimination. 2. Logit Loglinear Analysis. Dichotomous logit model loglinear representation parameter estimates goodness-of-fit statistics measures of dispersion and association polychotomous logit model interpreting parameters examining residuals introducing covariates. 3. Multinomial Logistic Regression. Baseline logits likelihood-ratio tests for models and individual effects evaluating the model calculating predicted probabilities the classification table goodness-of-fit tests residuals pseudo R-square measures overdispersion model selection matched case-control studies. 4. Ordinal Regression. Modeling cumulative counts parameter estimates testing for parallel lines model fit observed and expected counts measures of strength of association classifying cases link functions fitting a heteroscedastic probit model fitting location and scale parameters. 5. Probit Regression. Probit and logit response models confidence intervals for effective dosages comparing groups comparing relative potencies estimating the natural response rate multiple stimuli. 6. Kaplan-Meier Survival Analysis. Calculating survival time estimating the survival function, the conditional probability of survival, and the cumulative probability of survival plotting survival functions comparing survival functions stratified comparisons. 7. Life Tables. Calculating survival probabilities assumptions observations lost to follow-up plotting survival functions comparing survival functions. 8. Cox Regression. The model proportional hazards assumption coding categorical variables interpreting the regression coefficients baseline hazard and cumulative survival rates global tests of the model checking the proportional hazards assumption stratification log-minus-log survival plot identifying influential cases examining residuals partial (Schoenfeld) residuals martingale residuals variable-selection methods time-dependent covariates specifying a time-dependent covariate calculating segmented time-dependent covariates testing the proportional hazards assumption with a time-dependent covariate fitting a conditional logistic regression model. 9. Variance Components. Factors, effects, and models model for one-way classification estimation methods negative variance estimates nested design model for two-way classification univariate repeated measures analysis using a Mixed Models Approach distribution assumptions estimation methods. 10. Linear Mixed Models. Background Unconditional random-effects models hierarchical models random-coefficient model model with school-level and individual-level covariates three-level hierarchical model repeated measurements selecting a residual covariance structure. 11. Nonlinear Regression. The nonlinear model transforming nonlinear models intrinsically nonlinear models fitting a logistic population growth model finding starting values approximate confidence intervals for the parameters bootstrapped estimates starting values from previous analysis linear approximation computational issues common models for nonlinear regression specifying a segmented model. 12. Two-Stage Least-Squares Regression. Demand-price-income economic model estimation with ordinary least squares feedback and correlated errors estimation with two-stage least squares. 13. Weighted Least-Squares Regression. Diagnosing the problem estimating weights examining the log-likelihood function the WLS solution estimating weights from replicates diagnostics from the linear regression procedure. 14. Multidimensional Scaling. Data, models, and multidimensional scaling analysis nature of data analyzed in MDS measurement level of data shape of data conditionality of data missing data multivariate data classical MDS Euclidean model details of CMDS Replicated MDS Weighted MDS geometry of the weighted Euclidean model algebra of the weighted Euclidean model matrix algebra of the weighted Euclidean model Weirdness index flattened weights.