Question: What number comes next in the sequence? 249, 254, 127, 132Īnswer: 66. For example, the first number may be added by 5, then divided by 2, then added by 5, then divided by 2, and so on. Quite often a series will switch between adding, subtracting, multiplying or dividing by a given number. Each number is multiplied by 4 then divided by 2. Question: What number comes next? 2, 8, 4, 16, 8Īnswer: 32. Each number in the sequence is divided by 3. Question: What number comes next? 3, 9, 27, 81Īnswer: 243. Numbers may be divided or multiplied in a set or alternating series. Question: What number comes next? 48, 42, 36, 30.Īnswer: 24 – Each number in the sequence is reduced by 6 Division & Multiplication Series Each number in the sequence increases by 10 Question: What number comes next? 11, 21, 31, 41.Īnswer: 51. These are one of the simplest and easiest to identify in a number series, where numbers are added or subtracted in a set order. The following are some number sequence examples that commonly appear on aptitude tests, going from simplest to more complicated. You can keep these number patterns in the back of your head, which will make it easier to identify the number sequence and solve the problems faster. Luckily, many questions follow numerical patterns that you can identify with practice. Number sequence questions are featured on a wide range of aptitude tests such as the GCSE and Wonderlic. Take a Number Sequence Test Sample Number Sequence Test Questions Number sequence questions are also great for puzzle fanatics and others looking a way to challenge their minds. Finally, we show how to compute confidence sequences for the difference between quantiles of two arms in an A/B test, along with corresponding always-valid p-values.This number sequence test is a great way to prepare for number sequence questions found on your upcoming aptitude test. Simulations demonstrate that our method stops with fewer samples than existing methods by a factor of five to fifty. We apply our results to the problem of selecting an arm with an approximately best quantile in a multi-armed bandit framework, proving a state-of-the-art sample complexity bound for a novel allocation strategy. This inequality directly yields sequential analogues of the one- and two-sample Kolmogorov-Smirnov tests, and a test of stochastic dominance. Specifically, we provide explicit expressions with small constants for intervals whose widths shrink at the fastest possible rate, as determined by the law of the iterated logarithm (LIL).Īs a byproduct, we give a non-asymptotic concentration inequality for the empirical distribution function which holds uniformly over time with the LIL rate, thus strengthening Smirnov’s asymptotic empirical process LIL, and extending the famed Dvoretzky-Kiefer-Wolfowitz (DKW) inequality to hold uniformly over all sample sizes while only being about twice as wide in practice. We give two methods for tracking a fixed quantile and two methods for tracking all quantiles simultaneously. We propose new, theoretically sound and practically tight confidence sequences for quantiles, that is, sequences of confidence intervals which are valid uniformly over time. Consider the problem of sequentially estimating quantiles of any distribution over a complete, fully-ordered set, based on a stream of i.i.d.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |