This document discusses sampling from a population. A population includes all items related to an inquiry, while a sample is a representative subset of the population. Simple random sampling (SRS) is the process of drawing a sample from a population where each unit has an equal chance of being selected. There are two types of SRS: with replacement, where selected units can be selected again; and without replacement, where selected units are not returned before selecting the next unit. Random number tables and lottery methods are two common techniques used to select simple random samples from large populations.
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING (SIMPLE RANDOM SAMPLING)Navya Jayakumar
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING
(SIMPLE RANDOM SAMPLING)
Sampling means the process of selecting a part of the population
A population is a group people that is studied in a research. These are the members of a town, a city, or a country.
It is difficult for a researcher to study the whole population due to limited resources
E.G.. Time, cost and energy
Hence the researcher selects a part of the population for his study, rather than selecting the whole population. This process is known as sampling
Also known as Random Sampling
A type of sampling where each member of the population has a known probability of being selected in the sample
When a population is highly homogeneous, its each member has a known chance of being selected in the sample
The extend of homogeneity of a population usually depends upon the nature of the research. E.g.: who are the target respondents of the research
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING (SIMPLE RANDOM SAMPLING)Navya Jayakumar
SAMPLING ; SAMPLING TECHNIQUES – RANDOM SAMPLING
(SIMPLE RANDOM SAMPLING)
Sampling means the process of selecting a part of the population
A population is a group people that is studied in a research. These are the members of a town, a city, or a country.
It is difficult for a researcher to study the whole population due to limited resources
E.G.. Time, cost and energy
Hence the researcher selects a part of the population for his study, rather than selecting the whole population. This process is known as sampling
Also known as Random Sampling
A type of sampling where each member of the population has a known probability of being selected in the sample
When a population is highly homogeneous, its each member has a known chance of being selected in the sample
The extend of homogeneity of a population usually depends upon the nature of the research. E.g.: who are the target respondents of the research
Systematic sampling in probability sampling Sachin H
This is a systematic sample in probability sampling which is consider to be one of the technics of sampling . It is most useful in certain circumstances in Random sampling.
Systematic sampling in probability sampling Sachin H
This is a systematic sample in probability sampling which is consider to be one of the technics of sampling . It is most useful in certain circumstances in Random sampling.
Know the types of Random Sampling method and how it is being used.
Simple random sampling
Systematic sampling
Stratified Sampling
Cluster or Area sampling
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SAMPLING METHODS ( PROBABILITY SAMPLING).pptxPoojaSen20
SAMPLING
SAMPLING IS THE PROCESS OF SELECTING A SMALL NUMBER OF ELEMNTS FROM A LARGER DEFINED TARGET GROUP OF ELEMNTS SUCH THAT THE INFORMATION GATHERDED FROM THE SMALL GROUP WILL ALLOW JUDEN=MENT TO BE MADE ABOUT THE LARGER GROUPS.
IN SIMPLE WORDS A PROCEDURE BY WHICH SOME MEMBERS OF A GIVEN POPULATION ARE SELECTED AS REPRESENTATION OF THE ENTIRE POPULATION .
PURPOSE OF SAMPLING
To gather data about the population in order to make an inference that can be generalized to the populations. .
PROBABILITY SAMPLING
Probability sampling is a type of sampling where each member of the population has a known probability of being selected in the sample .
In probability sampling some elements of randomness is involved in selection of units ,so that personal judgement or bias is not there.
NON- PROBABILITY SAMPLING
Non- Probability sampling is a type of sampling where each member of the population does not have known probability of being selected in the sample.
In this each member of the population does not get equal chance of being selected in the sample.
This sampling methods is adopted when each member of the population can not be selected or the researcher deliberately wants to choose member selectively
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Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
5. Population Possible Samples
TV’s produced by a factory.. Every 20th TV
Children’s pants made in a factory. Every 30th pair
Punctuality of buses in a city.
Check punctuality for
10 different routes
Tire produced by manufacturer. 5 tyres produced
6. Why do we take a Sample?
whySampling?
Too expensive and too
time consuming to
survey an entire
populationpopulation
7. SIMPLE RANDOM SAMPLING
(Unrestricted sampling) is
process of drawing a
sample from population in
which each and every unit
has equal chance of
included in sample.and are
divided to two SRS WITH
REPLACEMENT AND WITHOUT
REPLACEMENT
8. Simple Random Sample
Every subset of a specified size n
from the population has an equal
chance of being selected
9. If selected unit is not replaced
before second draw then
sampling method is called (s.r.s)
without replacement
10. After a unit has been selected by
this method it may or may not
replaced before second draw if
replaced before next draw it is
simple random sampling with
replacement
11. SOME EXAMPLE
For example, if we catch fish,
measure them, and immediately
return them to the water before
continuing with the sample, this is a
WR design, because we might end
up catching and measuring the same
fish more than once. However, if we
do not return the fish to the water
(e.g. if we eat the fish), this
becomes a WOR design.
12. DIFFERENCE OF S.R.S WITH
REPLACEMENT AND WITHOUT
REPLACEMENT
When we sample with replacement the
two values are independent.This means
first result doesnt affect second result.but
when we sample without replacement the
first result affect happening of second
result.
13. TYPES OF S.R.S
THEREARE 2TYPE OF METHOD OF
SELECTING SIMPLE RANDOM SAMPLING
1>LOTTERY METHOD
2>RANDOM NUMBERTABLE
14. In this method all items in population are
named or numbered serially on separate slips
of paper having identical shape and size and
are mixed in bowl then required number of
slips are selected at random
15. Hat
Simple Random Sampling
Method 1
Example: Out of a CLASS of 50
students 15 are to be selected to
take part in a FEST.
1. Assign ROLL number from 1 to 50 to
each student.
2. Write each number on a piece of paper
(or use raffle tickets), place in a hat and
mix up.
3. Draw the 15numbers from the hat.
16. RANDOM NUMBER TABLES
Lottery method is difficult to adopt if
size of population is very large.In
such case random number tables are
used.Of the tables pippett table is
most popular one, which consists of
10400(ten thousand four hundred)
sets of four digit random numbers
17. Eg:suppose we want to select a sample of
size ‘15’ from a population of 5000 then
number then number all 5000 item from 1to
5000.select apage at random from table and
choose first 15 numbers which are less than
or equal to 15.