使用移动设备传感器教学生错误分析
Using mobile-device sensors to teach students error analysis
摘要：
理科学生必须处理所有物理测量所固有的误差，并意识到需要将它们表示为最佳估计和不确定性范围。错误通常分为统计性错误和系统性错误。虽然统计错误通常在科学研究的头几年处理，但典型的方法是基于手动执行重复观察。我们的工作提出了一套实验室实验，教导错误和不确定性的基础上记录的数据与传感器在许多移动设备。讨论的主要方面是平均值和标准差的物理意义，以及直方图和分布的解释。波动的正态性进行了定性分析，比较直方图与正态曲线，定量比较间隔中的观测值数量与根据正态分布预期的数量，并进行卡方检验。我们发现，该分布通常遵循正态分布，然而，当传感器被放置在扬声器上播放纯音显着差异与正态分布观察。作为日常情况的应用，我们讨论了不同情况下的波动强度，例如将设备放在桌子上或以不同的方式用手握住它。其他活动的重点是根据加速度计记录的波动量化道路的平滑度。本建议有助于深入了解现代技术和统计错误，并最终激励和鼓励工程和科学学生。
英文摘要：
Science students must deal with the errors inherent to all physical measurements and be conscious of the need to expressvthem as a best estimate and a range of uncertainty. Errors are routinely classified as statistical or systematic. Although statistical errors are usually dealt with in the first years of science studies, the typical approaches are based on manually performing repetitive observations. Our work proposes a set of laboratory experiments to teach error and uncertainties based on data recorded with the sensors available in many mobile devices. The main aspects addressed are the physical meaning of the mean value and standard deviation, and the interpretation of histograms and distributions. The normality of the fluctuations is analyzed qualitatively comparing histograms with normal curves and quantitatively comparing the number of observations in intervals to the number expected according to a normal distribution and also performing a Chi-squared test. We show that the distribution usually follows a normal distribution, however, when the sensor is placed on top of a loudspeaker playing a pure tone significant differences with a normal distribution are observed. As applications to every day situations we discuss the intensity of the fluctuations in different situations, such as placing the device on a table or holding it with the hands in different ways. Other activities are focused on the smoothness of a road quantified in terms of the fluctuations registered by the accelerometer. The present proposal contributes to gaining a deep insight into modern technologies and statistical errors and, finally, motivating and encouraging engineering and science students.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
