python: which title is a statistically significant predictor of customer contact
时间: 2024-06-07 21:05:57 浏览: 135
To determine which title is a statistically significant predictor of customer contact, you could conduct a logistic regression analysis. In this analysis, you would use customer contact as the dependent variable and title as the independent variable. The logistic regression analysis would determine which titles have a significant effect on customer contact after controlling for other factors. The statistical significance of each title could be determined by examining the p-value associated with its coefficient in the logistic regression model. Titles with lower p-values would be considered more statistically significant predictors of customer contact.
相关问题
Which variables have statistically significant effects on survival experience?这个问题怎么解决,R语言代码
这是一个统计学问题,需要进行生存分析(Survival Analysis)。在R语言中,可以使用Survival包来进行生存分析。
假设我们有一个数据集df,其中包含了生存时间(time)和生存状态(status),以及一些可能影响生存的变量,比如性别(sex)、年龄(age)、治疗方式(treatment)等等。那么,我们可以使用Survival包中的coxph()函数来进行Cox比例风险回归分析,进而得出哪些变量对生存经验有显著影响。
具体的R语言代码如下:
``` r
library(survival)
# 假设我们的数据集包含了以下变量
# time:生存时间
# status:生存状态(0表示存活,1表示死亡)
# sex:性别
# age:年龄
# treatment:治疗方式
# 读入数据集
df <- read.csv("data.csv")
# 进行Cox比例风险回归分析
coxph_model <- coxph(Surv(time, status) ~ sex + age + treatment, data = df)
# 输出回归结果
summary(coxph_model)
```
输出的结果会给出每个变量的系数(coef)和显著性水平(p-value),如果p-value小于0.05,则说明该变量对生存经验有显著影响。
翻译 To ascertain that the improvement of dipIQ is statistically significant, we carry out a two sample T-test (with a 95% confidence) between PLCC values obtained by different models on LIVE [86]. After comparing every possible pairs of OU-BIQA models, the results are summarized in Table V, where a symbol “1” means the row model performs signifi- cantly better than the column model, a symbol “0” means the opposite, and a symbol “-” indicates that the row and column models are statistically indistinguishable. It can be observed that dipIQ is statistically better than dipIQ∗, which is better than all previous OU-BIQA models.
为了确保 dipIQ 的改进在统计上具有显著性,我们在 LIVE 数据集 [86] 上对不同模型得到的 PLCC 值进行了双样本 T 检验(置信度为95%)。在比较了 OU-BIQA 模型的所有可能配对后,结果总结如表 V 所示。其中,“1”表示行模型显著优于列模型,“0”表示相反,而“-”表示行和列模型在统计上无法区分。可以观察到 dipIQ 在统计上优于 dipIQ∗,后者优于所有先前的 OU-BIQA 模型。