Replication Data for: Recalibration of Predicted Probabilities Using the 'Logit Shift': Why does it work, and when can it be expected to work well?
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Find herein simulation scripts for generating the three tables in our manuscript. Each script corresponds to a particular table (Table 1, Table 2, and Table 3). The Table 1 script is used to demonstrate the close adherence between the logit shift and the exact Poisson-Binomial posterior update. We consider two different sample sizes (n = 100 and n = 1000) and six data-generating distributions for a set of initial Democratic support scores. We assume that the true number of Democratic votes is 20% lower than predicted, and compute updated scores via both the logit shift and the exact Poisson-Binomial posterior probabilities. We show that the two sets of updated scores are virtually identical under several metrics. The Table 2 script demonstrates one of the drawbacks of the logit shift: namely, that it can skew the sc' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["elections","logit shift","Poisson-Binomial distribution","recalibration"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Find herein simulation scripts for generating the three tables in our manuscript. Each script corresponds to a particular table (Table 1, Table 2, and Table 3). The Table 1 script is used to demonstrate the close adherence between the logit shift and the exact Poisson-Binomial posterior update. We consider two different sample sizes (n = 100 and n = 1000) and six data-generating distributions for a set of initial Democratic support scores. We assume that the true number of Democratic votes is 20% lower than predicted, and compute updated scores via both the logit shift and the exact Poisson-Binomial posterior probabilities. We show that the two sets of updated scores are virtually identical under several metrics. The Table 2 script demonstrates one of the drawbacks of the logit shift: namely, that it can skew the sc' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Find herein simulation scripts for generating the three tables in our manuscript. Each script corresponds to a particular table (Table 1, Table 2, and Table 3). The Table 1 script is used to demonstrate the close adherence between the logit shift and the exact Poisson-Binomial posterior update. We consider two different sample sizes (n = 100 and n = 1000) and six data-generating distributions for a set of initial Democratic support scores. We assume that the true number of Democratic votes is 20% lower than predicted, and compute updated scores via both the logit shift and the exact Poisson-Binomial posterior probabilities. We show that the two sets of updated scores are virtually identical under several metrics. The Table 2 script demonstrates one of the drawbacks of the logit shift: namely, that it can skew the sc
elections; logit shift; Poisson-Binomial distribution; recalibration