The mean value is commonly used as a measure of central tendency. It is frequently reported along with either the STANDARD DEVIATION (SD) or the STANDARD error of the mean (SEM). While the SD reflects the dispersion of the data in both the sample and population, SEM indicates the precision of the mean. SEM is not commonly used in reporting science; however, the 95% confidence interval, which is calculated based on SEM, is frequently reported in scientific literature.

The use of variance as a risk measure is limited by its non-coherentnature. On the other hand, STANDARD DEVIATION has been demonstrated as acoherent and effective measure of market volatility. This paper suggests theuse of STANDARD DEVIATION in portfolio optimization problems with cardinalityconstraints and short selling, specifically in the mean-conditional value-at riskframework. It is shown that, subject to certain conditions, this approach leadsto lower STANDARD DEVIATION. Empirical results obtained from experiments onthe SP index data set from 2016-2021 using various numbers of stocks andconfidence levels indicate that the proposed model outperforms existing modelsin terms of Sharpe ratios.

The aim of this study was to evaluate the impact of non-interest income on asset returns, STANDARD DEVIATION of return on assets, return on assets adjusted, is. This study literature study and analytical reason and based on panel data analysis (panel data) is. In this study, 117 financial companies listed on the Tehran Stock Exchange during the period 1389 to 1393. Software to analyze the results of the study, 7 Eviews used. The results confirming the first hypothesis suggests that the non-interest income and a return on assets of the company, there was a significant relationship. Also according to the analysis made in connection with the second hypothesis study came to the conclusion that Non-interest income and STANDARD DEVIATION of returns between corporate assets, there was a significant relationship. Following the results of verification third hypothesis suggests that the non-interest income and adjusted return on assets of the company, there was a significant relationship.

Meta-analysis is known as a statistical analysis that combines the results of multiple scientific studies. Meta-analysis can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error (1). . .

Outlier detection is a technique to identify and remove significantly different data from the more correct and consistent data in a data set. Outlier data can have negative impact on classification and clustering performance; that should be identified and removed to improve the classification efficiency. Regardless of whether a classifying technique classifies an outlier correctly, the very notion of identifying a data as outlier is of great significance.   In this paper, a new approach is proposed for outlier data detection within a test data set along with unsupervised training set selection. The selected training set is used for two-step classification. After unsupervised clustering the training set, the closest cluster to a test sample is selected using the Euclidean distance measure. Then, the outlier in the test sample is identified with the concepts of STANDARD DEVIATION and mean value.  The results showed by evaluating the distance of each sample of the test set with the new selected data set. the accuracy of the classifiers is enhanced after detection and elimination of outlier data.