Marla AllentoffStatistical consultant, statistical tutor *

Bio

Accomplished professional, statistics online tutoring expert, from introductory to advanced courses. Statistical and mathematical instruction and statistical consulting. Accomplished in teaching with specialization in fields related to applied probability, applied statistics, and mathematics. Applied statistician and mathematician, with higher education in applied mathematics and statistics, devoted to excellence in teaching and learning. I'll provide professional high quality tutoring. Good knowledge of branches of mathematics. I am passionate about helping students. Accomplished master of science and received special Applied Mathematics and Statistics award from Stony Brook University. Great sense of community. Value intellectual activity, learning and teaching. Professional associations include Member of American Statistical Association and Member of National Council of Teachers of Mathematics.

I believe the correct answer is B. We are looking at the relative frequency of letters, and you are choosing a random sample of books to count it.

With respect to a random sample, we sample from a population. We like to randomly select a sample from a population. Thank you.

Good skills are public speaking, leadership and coaching. Thank you.

This is interesting. My specialty is statistics and I love metrics. You could look at online retail metrics. For example, metrics are: average order size, percent of returning customers and percent of new customers. There is average number of items per purchase, and percent of canceled checkouts. There is a wonderful site, kpilibrary.com. It has wonderful metrics. For SOM, metrics are conversion rates for a website, number of retweets and cost per action. For SAM, a KPI is words to sale. Thank you.

Generating primes

From Wikipedia, the free encyclopedia

In computational number theory, a variety of algorithms make it possible to generate prime numbers efficiently. These are used in various applications, for example hashing, public-key cryptography, and search of prime factors in large numbers.

For relatively small numbers, it is possible to just apply trial division to each successive odd number. Prime sieves are almost always faster.

Prime sieves

A prime sieve or prime number sieve is a fast type of algorithm for finding primes. There are many prime sieves. The simple sieve of Eratosthenes (250s BCE), the sieve of Sundaram (1934), the still faster but more complicated sieve of Atkin[1] (2004), and various wheel sieves[2] are most common.

A prime sieve works by creating a list of all integers up to a desired limit and progressively removing composite numbers (which it directly generates) until only primes are left. This is the most efficient way to obtain a large range of primes; however, to find individual primes, direct primality tests are more efficient[citation needed]. Furthermore, based on the sieve formalisms, some integer sequences (sequence A240673 in the OEIS) are constructed which they also could be used for generating primes in certain intervals.

Large primes[edit]

For the large primes used in cryptography, it is usual to use a modified form of sieving: a randomly chosen range of odd numbers of the desired size is sieved against a number of relatively small primes (typically all primes less than 65,000). The remaining candidate primes are tested in random order with a standard probabilistic primality test such as the Baillie-PSW primality test or the Miller-Rabin primality test for probable primes.

Alternatively, a number of techniques exist for efficiently generating provable primes. These include generating prime numbers p for which the prime factorization of p − 1 or p + 1 is known, for example Mersenne primes, Fermat primes and their generalizations.

There is a wonderful write up From Amstat News that could be of great value. Amstat News, Statisticians Have Large Role to Play in Web Analytics 1 SEPTEMBER 2011

An example of a web analytics application that will benefit from statistical technology is estimating the value (CPC, or cost-per-click) and volume of a search keyword depending on market, position, and match type—a critical problem for Google and Bing advertisers, as well as publishers. Currently, if you use the Google API to get CPC estimates, Google will return no value more than 50% of the time. This is a classic example of a problem that was addressed by smart engineers and computer scientists, but truly lacks a statistical component—even as simple as naïve Bayes—to provide a CPC estimate for any keyword, even those that are brand new. Statisticians with experience in imputation methods should solve this problem easily and help their companies sell CPC and volume estimates (with confidence intervals, which Google does not offer) for all keywords.

Statistics plays a large role in web analytics. It would be important to look at Google Analytics, and look at the way they measure the indicators. Maybe other factors such as id based on mobile applications matter. It depends on the way it is measured. I can look into this more, but business analytics have become increasingly popular. Thank you.

With wishes for great success.

Marla A.

It is up to you. It is good to have a goal and achieve that goal. What is important is that those that participate know what the money is going toward. Looks like you have done great work!!!!!

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