A Different Method of Solving a Problem of IMO
Issue:
Volume 5, Issue 1, March 2020
Pages:
1-3
Received:
30 July 2019
Accepted:
10 September 2019
Published:
1 April 2020
Abstract: The IMO performs once a year, and has become an important activity in the field of mathematics. Because the problems in IMO are very difficult, and in general needs two days to finish the test of only six problems, therefore, it is significant to study how to solve and solve those IMO problems with various methods. With respect to question (a) of the problem of discussing, at first, using the so-called “exhaustive method” and the mathematical induction, the paper gets the conclusion of that if n is the integral multiple of 3, subtracting 1 from the nth power of 2 must be divisible by 7. Furthermore, it also proves by use of the disprove method that if n is not the integral multiple of 3, subtracting 1 from the nth power of 2 is impossible to be divisible by 7. The way of solving question (b) is similar to that of solving (a), in order to use the result of question (a) for the third step of the mathematical induction, the paper firstly consider the third power of that 1 added to (k+1)th power of 2 and applying the disprove method proves that it and hence that 1 added to the (k+1)th power of 2 are not divisible by 7, namely the question (b) is true.
Abstract: The IMO performs once a year, and has become an important activity in the field of mathematics. Because the problems in IMO are very difficult, and in general needs two days to finish the test of only six problems, therefore, it is significant to study how to solve and solve those IMO problems with various methods. With respect to question (a) of t...
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Review of Outlier Detection and Identifying Using Robust Regression Model
Getnet Bogale Begashaw,
Yordanos Berihun Yohannes
Issue:
Volume 5, Issue 1, March 2020
Pages:
4-11
Received:
25 October 2019
Accepted:
23 November 2019
Published:
13 April 2020
Abstract: Outliers are observations that have extreme value relations. Herewith leverage is a measure of how an independent variable deviates from its mean. An observation with an extreme value on a predictor variable is a point with high leverage. The presence of outliers can lead to inflated error rates and substantial distortions of parameter and statistic estimates when using either parametric or nonparametric tests. Casual observation of the literature suggests that researchers rarely report checking for outliers of any sort and taking remedial measures for outliers. Outliers can have positive deleterious effects on statistical analyses. For instance, they serve to increase error variance and reduce the power of statistical tests; they can decrease normality, altering the odds of making both Type I and Type II errors for non- randomly distributed; and they can seriously bias or influence estimates that may be of substantive interest. These outliers are cased from incorrect recording data, intentional or motivated mis-reporting, sampling error and Outliers as legitimate cases sampled from the correct population. According to some literatures; Point outliers, Contextual Outliers and Collective Outliers are the three types of outliers. Robust regression estimators can be a powerful tool for detection and identifying outliers in complicated data sets. Robust regression, deals with the problem of outliers in a regression and produce different coefficient estimates than OLS does.
Abstract: Outliers are observations that have extreme value relations. Herewith leverage is a measure of how an independent variable deviates from its mean. An observation with an extreme value on a predictor variable is a point with high leverage. The presence of outliers can lead to inflated error rates and substantial distortions of parameter and statisti...
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