SPSS for Starters and 2nd Levelers - Tapa blanda

Sinopsis

1: One-Sample Continuous Data (One-Sample T-Test, One-Sample Wilcoxon Signed Rank Test, 10 Patients).- 2: Paired Continuous Data (Paired T-Test, Wilcoxon Signed Rank Test, 10 Patients).- 3: Paired Continuous Data with Predictors (Generalized Linear Models, 50 Patients).- 4: Unpaired Continuous Data (Unpaired T-Test, Mann-Whitney, 20 Patients).- 5: Linear Regression (20 Patients).- 6: Multiple Linear Regression (20 Patients).- 7: Automatic Linear Regression (35 Patients).- 8: Linear Regression with Categorical Predictors (60 Patients).- 9: Repeated Measures Analysis of Variance, Friedman (10 Patients).- 10: Repeated Measures Analysis of Variance Plus Predictors (10 Patients).- 11: Doubly Repeated Measures Analysis of Variance (16 Patients).- 12:Repeated Measures Mixed-Modeling (20 Patients).- 13:  Unpaired Continuous Data with Three or More Groups (One Way Analysis of Variance, Kruskal-Wallis, 30 Patients).- 14: Automatic Nonparametric Testing (30 Patients).- 15: Trend Test for Continuous Data (30 Patients).- 16: Multistage Regression (35 Patients).- 17: Multivariate Analysis with Path Statistics (35 Patients).- 18: Multivariate Analysis of Variance (35 and 30 Patients).- 19: Missing Data Imputation (35 Patients).- 20: Meta-regression (20 and 9 Studies).- 21: Poisson Regression for Outcome Rates (50 Patients).- 22: Confounding (40 Patients).- 23: Interaction, Random Effect Analysis of Variance (40 Patients).- 24: General Loglinear Models for Identifying Subgroups with Large Health Risks (12 Populations).- 25: Curvilinear Estimation (20 Patients).- 26: Loess and Spline Modeling (90 Patients).- 27: Monte Carlo Tests for Continuous Data (10 and 20 Patients).- 28: Artificial Intelligence Using Distribution Free Data (90 Patients).- 29: Robust Testing (33 Patients).- 30: Nonnegative Outcomes Assessed with Gamma Distribution (110 Patients).- 31: Nonnegative Outcomes Assessed with Tweedie Distribution (110 Patients).- 32: Validating Quantitative Diagnostic Tests (17 Patients).- 33: Reliability Assessment of Quantitative Diagnostic Tests (17 Patients).- 34: One-Sample Binary Data (One-Sample Z-Test, Binomial Test, 55 Patients).- 35: Unpaired Binary Data (Chi-Square Test, 55 Patients).- 36: Logistic Regression with a Binary Predictor (55 Patients).- 37: Logistic Regression with a Continuous Predictor (55 Patients).- 38: Logistic Regression with Multiple Predictors (55 Patients).- 39: Logistic Regression with Categorical Predictors (60 Patients).- 40: Trend Tests for Binary Data (106 Patients).- 41: Paired Binary (McNemar Test) (139 General Practitioners).- 42: Paired Binary Data with Predictor (139 General Practitioners).- 43: Repeated Measures Binary Data (Cochran's Q Test), (139 Patients).- 44: Multinomial Regression for Outcome Categories (55 Patients).- 45: Random Intercept for Categorical Outcome and Predictor Variables (55 Patients).- 46: Comparing the Performance of Diagnostic Tests (650 and 588 Patients).- 47: Poisson Regression for Binary Outcomes (52 Patients).- 48: Ordinal Regression for Data with Underpresented Outcome Categories (450 Patients).- 49: Probit Regression, Binary Data as Response Rates (14 Tests).- 50: Monte Carlo Tests for Binary Data (139 Physicians and 55 Patients).- 51: Loglinear Models, Logit Loglinear Models (445 Patients).- 52: Loglinear Models, Hierarchical Loglinear Models (445 Patients).- 53: Validating Qualitative Diagnostic Tests (575 Patients).- 54: Reliability Assessment of Qualitative Diagnostic Tests (17 Patients).- 55: Log Rank Testing (60 Patients).- 56: Cox Regression With/Without Time Dependent Variables (60 Patients).- 57: Segmented Cox Regression (60 Patients).- 58: Assessing Seasonality (24 Averages).- 59:&nb

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