[color=#222222]问题是这样的：A retailer of hand-held organizers wants to relate the ownership of the devices with annual income of the purchaser. Data is collected on 20 people and can be found in the file
Palmorg.xls . The data includes ownership of a handheld is indicated by y=1 and purchaser annual income x.[color=#222222]一个手持设备零售商想分析顾客收入和是否持有设备之间的关系，采集了20个顾客样本，数据中包括两个变量：持有设备用y=1表示，没有则是y=0；还有顾客的年收入income
如果我不将收入分类，直接run出来的结果是income的相关性不高的，但是我又不知该如何分类。我试过按收入区间20000-30000,30000-40000这样分类，但结果还是不行。的确样本量太小了，但如果非要计算，该如何做呢？在此求助了！！谢谢！！
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[color=#222222]数据：
[table=100%][tr][td][tr][td=1,1,72]purchaser[/td][td=1,1,72]Ownership[/td][td=1,1,72]income[/td][/tr][tr][td]1[/td][td]0[/td][td]36300[/td][/tr][tr][td]2[/td][td]0[/td][td]31200[/td][/tr][tr][td]3[/td][td]0[/td][td]56500[/td][/tr][tr][td]4[/td][td]1[/td][td]4170[/td][/tr][tr][td]5[/td][td]1[/td][td]60200[/td][/tr][tr][td]6[/td][td]0[/td][td]32400[/td][/tr][tr][td]7[/td][td]0[/td][td]35000[/td][/tr][tr][td]8[/td][td]0[/td][td]29200[/td][/tr][tr][td]9[/td][td]1[/td][td]56700[/td][/tr][tr][td]10[/td][td]0[/td][td]82000[/td][/tr][tr][td]11[/td][td]1[/td][td]42400[/td][/tr][tr][td]12[/td][td]0[/td][td]30600[/td][/tr][tr][td]13[/td][td]0[/td][td]41400[/td][/tr][tr][td]14[/td][td]0[/td][td]28300[/td][/tr][tr][td]15[/td][td]1[/td][td]47500[/td][/tr][tr][td]16[/td][td]0[/td][td]35700[/td][/tr][tr][td]17[/td][td]0[/td][td]32100[/td][/tr][tr][td]18[/td][td]1[/td][td]79600[/td][/tr][tr][td]19[/td][td]1[/td][td]40200[/td][/tr][tr][td]20[/td][td]0[/td][td]53100[/td][/tr][/td][td]
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[color=#222222]我直接run出来结果如下：[color=#222222]
The LOGISTIC Procedure
Model Information
Data Set _PROJ_.PALMORG
Response Variable Ownership Ownership
Number of Response Levels 2
Number of Observations 20
Model binary logit
Optimization Technique Fisher's scoring
Response Profile
Ordered Total
Value Ownership Frequency
1 1 7
2 0 13
Probability modeled is Ownership=1.
Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 27.898 29.193
SC 28.894 31.184
-2 Log L 25.898 25.193
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 0.7051 1 0.4011
Score 0.7045 1 0.4013
Wald 0.6778 1 0.4104
Analysis of Maximum Likelihood Estimates
Standard Wald
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 -1.6008 1.3041 1.5068 0.2196
income 1 0.000022 0.000027 0.6778 0.4104
1 17:47 Saturday, February 15, 2003 2
The LOGISTIC Procedure
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
income 1.000 1.000 1.000
Association of Predicted Probabilities and Observed Responses
Percent Concordant 70.3 Somers' D 0.418
Percent Discordant 28.6 Gamma 0.422
Percent Tied 1.1 Tau-a 0.200
Pairs 91 c 0.709
