اس فایل

مرجع دانلود فایل ,تحقیق , پروژه , پایان نامه , فایل فلش گوشی

اس فایل

مرجع دانلود فایل ,تحقیق , پروژه , پایان نامه , فایل فلش گوشی

The Elderly and the Internet: A Case Study

اختصاصی از اس فایل The Elderly and the Internet: A Case Study دانلود با لینک مستقیم و پر سرعت .

Objective: To investigate the impact of training the elderly to use the Internet in order to become more active in their health care. Methods: This study utilized semi-structured interviews and surveys. Results: Five themes emerged from the interviews that described participants' motivations and barriers they experienced when using the Internet. Survey data revealed that levels of computer anxiety decreased and levels of efficacy increasing after training. Conclusion: Training the elderly to use the Internet lowered levels of computer anxiety. In addition, participants used the information retrieved from the Internet to become more active in their own health care.


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The Elderly and the Internet: A Case Study

ASME B16.34-2013 (Valves — Flanged, Threaded, and Welding End)

اختصاصی از اس فایل ASME B16.34-2013 (Valves — Flanged, Threaded, and Welding End) دانلود با لینک مستقیم و پر سرعت .

ASME B16.34-2013 (Valves — Flanged, Threaded, and Welding End)


استاندارد B16.34-2013  (Valves — Flanged, Threaded, and Welding End)

استاندارد شیرها در صنعت نفت و گاز

استاندارد B16.34-2013  (Valves — Flanged, Threaded, and Welding End)


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ASME B16.34-2013 (Valves — Flanged, Threaded, and Welding End)

Recovering from Selection Bias in Causal and Statistical Inference مقاله هوش مصنوعی 2014

اختصاصی از اس فایل Recovering from Selection Bias in Causal and Statistical Inference مقاله هوش مصنوعی 2014 دانلود با لینک مستقیم و پر سرعت .

Recovering from Selection Bias in Causal and Statistical Inference مقاله هوش مصنوعی 2014


Recovering from Selection Bias in Causal and Statistical Inference مقاله هوش مصنوعی 2014

Abstract


Selection bias is caused by preferential exclusion of units from the samples and represents a major obstacle to valid causal and statistical inferences; it cannot be removed by randomized experiments and can rarely be detected in either experimental or observational studies. In this paper, we
provide complete graphical and algorithmic conditions for
recovering conditional probabilities from selection biased
data. We also provide graphical conditions for recoverability
when unbiased data is available over a subset of the variables.
Finally, we provide a graphical condition that generalizes
the backdoor criterion and serves to recover causal effects
when the data is collected under preferential selection.


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Recovering from Selection Bias in Causal and Statistical Inference مقاله هوش مصنوعی 2014