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Statistical inference
Published in Tiffany Timbers, Trevor Campbell, Melissa Lee, Data Science, 2022
Tiffany Timbers, Trevor Campbell, Melissa Lee
A more practical approach would be to make measurements for a sample, i.e., a subset of individuals collected from the population. We can then compute a sample estimate—a numerical characteristic of the sample—that estimates the population parameter. For example, suppose we randomly selected ten undergraduate students across North America (the sample) and computed the proportion of those students who own an iPhone (the sample estimate). In that case, we might suspect that proportion is a reasonable estimate of the proportion of students who own an iPhone in the entire population. Figure 10.1 illustrates this process. In general, the process of using a sample to make a conclusion about the broader population from which it is taken is referred to as statistical inference.
Biostatistics and Bioaerosols
Published in Harriet A. Burge, Bioaerosols, 2020
Lynn Eudey, H. Jenny Su, Harriet A. Burge
Statistical inference is the process of using sample data to draw conclusions about a population parameter (or population parameters). Statistical inference takes two basic forms, estimation and hypothesis testing. There are also two basic approaches to inference, parametric and nonparametric. Parametric inference is based on knowing the form of the population distribution, or on the use of a CLT. Nonparametric inference does not rely on knowing the underlying distribution of the population. Estimation is described below as used in parametric statistics; hypothesis testing is described using each of the two approaches.
Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics
Published in International Journal of Production Research, 2023
Kuen-Suan Chen, Yuan-Lung Lai, Ming-Chieh Huang, Tsang-Chuan Chang
In the context of poor performance, and within index can provide information on means of improving the process performance of (Huang, Chang, and Chen 2021). In confirming the effectiveness of improvement measures, observations of increases in is the simplest and most direct approach. However, sampling error makes it possible to misjudge the effectiveness of performance improvement. Hypothesis testing is a method of statistical inference that utilises a set of sample data to draw conclusions regarding the population parameter. For this reason, hypothesis testing of is employed to develop the judgement model for the effectiveness of the performance improvement. First, we let and represent the PCI values of processing characteristic before and after improvements. When the value of is lower than required value , then it is necessary to implement a process improvement plan. Thus, hypothesis testing can be applied as follows:
Optimal Probabilistic Scheduling of a Proposed EH Configuration Based on Metaheuristic Automatic Data Clustering
Published in IETE Journal of Research, 2022
Hadi Hosseinnejad, Sadjad Galvani, Payam Alemi
Statistical inference is the method by which data processing is used to deduce the properties of a probability distribution. The observed data collection is known to be sampled from a larger population. Descriptive statistics should be contrasted to inferential statistics. Statisticians distinguish between three types of fully parametric, nonparametric, and semi-parametric assumptions [40]. By considering the mentioned methods the results analyzed by descriptive statistics in (Table 8). Results show the standard deviation (STD) 5.76 for electrical input’s mean which is 71.63 MW, and 3.01 for gas input’s mean that is 121.19 MW. This means the usage tolerance is for of periods are between 71.635.76 MW for electricity input and 121.193.01 MW for gas, because of the normal distribution of demands which mentioned before. Considering the STDs and applying the cost function by using the gas and electricity price as mentioned, the total difference of uncertainty will $12,135.19 (which is 14.59% increase in total cost that was $83,157.13). Also, it should notice that the proposed configuration in comparison to the base hub will cost $48.74 lower in every single day.
A new method in introducing the uniformly most accurate confidence set
Published in International Journal of Mathematical Education in Science and Technology, 2022
Lin-An Chen, Chu-Lan Michael Kao
We have two concerns related to the classical UMA confidence set (interval). Our first concern relates to the confusion that arises from how it is traditionally described. Statistical inference is to draw conclusions about the parameter based on the evidence provided by data. Now, the current textbooks and the existing literature interpret this confidence set to maximize the accuracy of coverage when it is, by nature, designed to minimize the false coverage probability, contrary to the reader’s expectation. See Section 2.2 for further details. Our first concern highlights the fact that the complexity of this description can lead to a diluted understanding of the ideas and methods of UMA, since it disregards the fundamental argument.