Keyword | CPC | PCC | Volume | Score | Length of keyword |
---|---|---|---|---|---|

sas proc surveylogistic model selection | 0.69 | 0.8 | 5813 | 74 | 39 |

sas | 1.36 | 0.8 | 1316 | 56 | 3 |

proc | 1.37 | 0.6 | 9260 | 50 | 4 |

surveylogistic | 0.03 | 0.5 | 5273 | 98 | 14 |

model | 0.83 | 0.3 | 3959 | 34 | 5 |

selection | 1.22 | 0.6 | 3778 | 33 | 9 |

Keyword | CPC | PCC | Volume | Score |
---|---|---|---|---|

sas proc surveylogistic model selection | 0.19 | 0.9 | 6691 | 6 |

sas proc surveylogistic output | 0.5 | 0.6 | 8673 | 45 |

sas proc logistic model selection | 1.02 | 0.2 | 8280 | 90 |

proc surveyselect in sas | 1.49 | 0.7 | 8713 | 56 |

proc surveyselect sas example | 1.78 | 0.3 | 5680 | 1 |

sas proc logistic model statement | 1.86 | 0.4 | 2903 | 5 |

proc surveyselect sous sas | 0.05 | 0.6 | 7227 | 47 |

sas proc survey example | 0.63 | 0.6 | 3084 | 86 |

sas proc surveymeans options | 1.32 | 0.1 | 1376 | 86 |

sas proc surveyselect pps | 1.12 | 0.1 | 1414 | 48 |

sas proc surveyselect seed | 1.52 | 0.4 | 9308 | 26 |

sas proc surveyselect sampling weight | 1.27 | 1 | 965 | 40 |

sas proc logistic forward selection | 0.09 | 0.5 | 31 | 49 |

proc model in sas | 0.63 | 0.9 | 8681 | 72 |

sas proc surveyfreq options | 0.12 | 0.2 | 3113 | 18 |

sas proc surveyreg class statement | 0.99 | 0.3 | 6207 | 14 |

sas proc logistic example | 0.94 | 1 | 8354 | 63 |

sas proc surveymeans output | 0.95 | 0.2 | 9464 | 23 |

sas proc logistic roc curve | 0.44 | 0.1 | 7348 | 90 |

survey procedures in sas | 0.96 | 0.8 | 455 | 89 |

survey logistic regression sas | 0.5 | 1 | 9115 | 44 |

See Chapter 51, The LOGISTIC Procedure, for general information about how to perform logistic regression by using SAS. PROC SURVEYLOGISTIC is designed to handle sample survey data, and thus it incorporates the sample design information into the analysis.

For statistical inferences, PROC SURVEYLOGISTIC incorporates complex survey sample designs, including designs with stratification, clustering, and unequal weighting. The following are highlights of the SURVEYLOGISTIC procedure's features: enables you to import or export SAS data sets containing replicate weights for BRR or jackknife methods

For SELECTION=SCORE, PROC LOGISTIC uses the branch-and-bound algorithm of Furnival and Wilson (1974) to find a specified number of models with the highest likelihood score (chi-square) statistic for all possible model sizes, from 1, 2, 3 effect models, and so on, up to the single model containing all of the explanatory effects.

By default, PROC SURVEYLOGISTIC completely excludes an observation from analysis if that observation has a missing value, unless you specify the MISSING option. Note that the NOMCAR option has no effect on a classification variable when you specify the MISSING option, which treats missing values as a valid nonmissing level.