Simple Random Sample

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	1_2 Morningside, MATH 150 Excerpt: ... There are four techniques of sampling: simple random sampling, stratified sampling, systematic sampling, and cluster sampling. We will only discuss simple random sampling in this section. A sample of size n from a population of size N is obtained through simple random sampling if every possible sample of size n has an equally likely chance of occurring. The following steps describe how to create a simple random sample . 1. Assign each member of the population a number between 1 and N . 2. Use either a random number table or some sort of technology to select n numbers between 1 and N . 3. For each number randomly selected, the population member assigned that number will be in the sample. 1 2 ACTIVITIES: 1. Consider the following research objective (p. 10 #39): "The Gallup Organization contacts 1028 teenagers who are 13 to 17 years of age and live in the United States and asked whether or not they had been prescribed medications for any mental disorders, such as depression or anxiety." Is this an observ ...
	summ.11.21 Texas A&M, STAT 652 Excerpt: ... Summary of November 21 Lecture Began discussing sampling Census vs. sampling Probability sampling Simple random sampling 1 ...
	lecture9 UCLA, STATS 110 Excerpt: ... Lecture Outline 9 Stat 110a/Sanchez Reading: Review textbook pages 84-93 and pages 112-117 Textbook pages 227-251 excluding section 5.1 and least absolute errors Textbook pages 253-258 excluding exact confidence intervals for population proportion. Reader pages: 85-86. In particular, the t-table there better than book's. Statistical Inference for (or obtaining information about the population using only a simple random sample from that population) I.- Introduction Why don't we use censuses to answer questions about the average of some measurement in the populations we want to study? Statistical inference: drawing conclusions about the population at large given a sample. Assumes measurement Y in the population we are studying follow some probability model like those we studied in chapter 4. But of Y is unknown. need a simple random sample Y1, Y2, .Yn from this population to estimate . For the methods that we are going to learn in this course, a simple random sample from the population distribu ...
	Sampling_Lectures__Schedule_of_Sessions UNC, BIOS 162 Excerpt: ... LECTURES ON SAMPLING IN POPULATION-BASED STUDIES BIOS 162 Fall 2004 Lecturer: William D. Kalsbeek PN=962-3249 bill_kalsbeek@unc.edu Learning Objectives: 1. To develop a basic familiarity with sampling terminology as well as those design strategies ...
	Sampling Lectures_ Schedule of Sessions 2005 UNC, BIOS 162 Excerpt: ... LECTURES ON SAMPLING IN POPULATION-BASED STUDIES BIOS 162 Fall 2005 Lecturer: William D. Kalsbeek PN=962-3249 bill_kalsbeek@unc.edu Learning Objectives: 1. To develop a basic familiarity with sampling terminology as well as those design strategies u ...
	Sampling Lectures_ Schedule of Sessions 2006 UNC, BIOS 662 Excerpt: ... LECTURES ON SAMPLING IN POPULATION-BASED STUDIES BIOS 662 Fall 2006 Lecturer: William D. Kalsbeek PN=962-3249 bill_kalsbeek@unc.edu Learning Objectives: 1. To develop a basic familiarity with sampling terminology as well as those design strategies u ...
	3360ss7 U. Houston, TECH 132 Excerpt: ... Study Sheet for Chapter 7 Note that this chapter is very important to the rest of the material - especially sections 7.1 through 7.5. Sections 7.1 and 7.2 1. What is a sampling plan? 2. 3. What is a simple random sample ? Example 7.1 demonstrates how to find a simple random sample Define the following terms. 1) 2) 3) 5. Stratified random sample Cluster sample Systematic random sample Use statistical software to select a simple random sample and a systematic sample. Section 7.3, 7.4 and 7.5 1. What is a statistic? What is a sampling distribution? See page 241. 2. 3. Study Example 7.3 for a demonstration of how a sampling distribution can be established. Be sure you understand the Central Limit Theorem. It is very important. It is stated on the top of page 243 and explained on the bottom of that page. Study figures 7.4-7.6 to see the effects of sample size on the sampling distribution. What is the sampling distribution of the mean? See page 247. What is the standard error of the mean? How is it symbolized? How ...
	8_1 Morningside, MATH 150 Excerpt: ... 8.1 Lecture Notes Math 150 07/03/08 Mammenga DISTRIBUTION OF THE SAMPLE MEAN A. What is the sampling distribution of the sample mean? The sampling distribution of the sample mean is the probability distribution of all possible values of the random variable x computed from a sample of size n from a population with mean and standard deviation . To obtain the sampling distribution of the mean: 1. Obtain a simple random sample of size n. 2. Compute the sample mean. 3. Assuming that we are sampling from a nite population, repeat Steps 1 and 2 until all simple random sample s of size n have been obtained. It is impractical to obtain all simple random sample s of a given size of a data set for most real-life studies. However, the theory is important so that we can infer certain characteristics about the probability distribution of the sample statistic. B. How can we describe the distribution of the sample mean obtained from a population that is normal? The sampling distribution of x has the fol ...
	notes7 SUNY Stony Brook, AMS 102 Excerpt: ... are allowed to make choices, and it is really difficult for people to be impartial. Most people would agree that using a coin toss to select one person out of two would be a fair selection method. Why this is thought as fair? Each person has the same the chance to be selected. So this gives the basic idea of probability sampling method. If the right sampling methods are used, even a relatively small sample could accurately reflect the responses of a large population. Definition 2.1 A sampling method that gives each unit in the population a known non-zero chance of being selected is called a probability sampling method. And, the simplest way of probability sampling method is called a simple random sample . A simple random sample of size n is a sample n units selected in such a way that every possible sample of the given size n has the same chance of being selected as any other sample of the same size. Coin flipping is a simple random sampling method for population size of 2. For any larger size we can imagine i ...
	notes9 SUNY Stony Brook, AMS 102 Excerpt: ... AMS 102.7 Spring 2006 Jingyu Zou Elements of Statistics Lecture Notes # 9 1 Stratified random sampling Sometimes, a population of units fall into some natural subgroups, called strata. Think about we want to study on the most popular kind of video games played by college students. It is unfair to sample the same number of male students as the female students, because boys' answers will be much more diversified than girls'. With random sampling we expect all groups of the population to be approximately proportionately represented. When when it is not the case, say some groups maybe more important than the other groups. Or some group might have almost the same characteristic for each unit within the group, and another group might have very diversified characteristics. We definitely want to take more sample from the second group. A stratified random sample is selected by stratifying the population into disjoint strata and taking a simple random sample units from each stratum. The units sampled from each str ...
	math concepts notes lecture 25 Union, MAT 110 Excerpt: ... Lectur e 25 Reading s 8.1-8.5 Objectives Sampling distributions of sample proportion and sample mean Calculations with sampling distributions Assignment Stats: P526 1-15 odd P551 17-25 odd DEFINITION: The sampling distribution of the sample proportion is the distribution of values of the sample proportion in all possible random samples of the same size n taken from the same population. Example Proportion of Women We assume that 50% of the population are women, p = 0.50. Take a simple random sample of size n=4 people from this population and observe the proportion of women in the sample. Then this sampling process will be repeated many times to examine the possible values for the sample proportion and see how these possible values vary. ( seed 91, 1= woman, and 0= man) NUMBER OF WOMEN SAMPLE PROPORTION TALLY FREQUENCY PROPORTION OF ALL TRIALS 0 1 2 3 4 0.00 0.25 0.50 0.75 1.00 4 16 16 10 4 50 40 4/50=.08 16/50=.32 16/50=.32 10/50=.20 4/50=.08 50/50=1.00 TOTAL Chapter 8 - 1 ...
	Public Opinion - Study Guide Chadron, PS PS 231 Excerpt: ... Unit 8: Public Opinion - Study Guide Unit 8 Study Guide Study questions on Public Opinion You should print out a copy of these questions and make notes as you read the chapter.* Bardes Chapter 6: Public Opinion 1. Are political scientists mainly interested in studying the attitudes of the public at large or attitudes of individuals that can be aggregated by groups? 2. What is the formal and traditional way that public opinion is determined in a democracy? 3. What is divisive (polarized) and consensual public opinion? What is non-opinions (from lecture)? 4. What is the role of intensity in public opinion? 5. What is the importance of relevance (salience) in public opinion? 6. What is the quality of public opinion in terms of stability and knowledge? 7. What is a simple random sample ? Why is randomness so important in survey research? 8. About how many people are usually interviewed for scientific surveys? 9. What is a margin of error? What is the confidence level? What is sampling error? 10. What types of n ...
	chapter3 Maple Springs, MATH 2560 Excerpt: ... Summary of Lecture Notes: Chapter 3 1. Some Concepts Population, sample, parameter, statistics, statistical inference, sampling variability, and sampling distribution. Observational study, experiment, randomization, control, replication. Sampling variability: the results based on different samples (from the same population) may be different. Examples. 2. Design of Experiments Design of experiment: the choice of treatments and the manner in which the individuals are assigned to the treatments. Principles of experimental design: (i) control lurking variables; (ii) randomization; (iii) repeat each treatment on many individuals to reduce variability. How to choose a sample? How to control lurking variables? How to do randomization? A simple random sample (SRS) not only gives each individual in the population an equal chance to be chosen but also gives every possible sample an equal chance to be chosen. Stratified random sample. To reduce bias, the sample should be randomly selected, and the individuals in the sam ...
	lec18 UC Davis, STATS 13 Excerpt: ... le from each stratum Cluster Sampling : Divide the population into subgroups called clusters; select a sample of clusters by SRS and then take a census of every element in the selected clusters 1-in-k Systematic Sampling : Randomly select one among the first k elements in an ordered population, and then select every k-th element thereafter Example For a survey about governor election in CA Divide California into counties and take a simple random sample of eligible voters within each county. Stratified Divide California into counties and take a simple random sample of 5 counties and then interview all eligible voters in these 5 counties. Cluster Choose an entry at random from the registered voters' list, and select every 100th listed voter thereafter. 1-in-100 Systematic Non-Random Sampling Plans There are several other sampling plans that do not involve randomization. They should NOT be used for statistical inference! Convenience sample: A sample that can be taken easily without random selection. For ...
	ReadingNotes02 UVA, STAT 212 Excerpt: ... nt. One item not covered in lecture is Simpson's Paradox, so read this subsection carefully. Sect. 3.1: This section is not covered in lecture, but is very readable. Pay particular attention to: The distinction between population and sample this will come up over and over throughout the course. The meaning of`voluntary response sample and the bias that can come with it. The definition of simple random sample . This is the type of sample that we shall use in the analysis in this course. The discussion of how to use a random digit table to generate a simple random sample can be ignored. See the videoclip in the Resources section for a quick demonstration of how to use Excel to generate a simple random sample . The meanings of probability sample, stratified random sample, and multistage sample. The mean of undercoverage and nonresponse. The subsection on capture-recapture sampling is interesting but not required. Sect. 3.2: This section is also not covered in lecture, but is qualitative and not h ...
	StudyDesignProposal Puget Sound, M 160 Excerpt: ... The strongest study designs will be based on using a simple random sample . Be clear on whether or not you will be selecting a simple random sample from the population. If your plan will not produce a simple random sample , comment on any bias this might introduce in your results. Be specic and detailed in your plans for gathering data. For example, if you plan to survey people, provide a draft of specic questions. If you think it best to make a signicant change to your initial proposal, check with me in person or by e-mail. You will need to stay organzied as you start collecting data in a pilot study and the full study. Consider buying a bound notebook to record everything you do on this project, from notes on your ideas at this stage to data from the full study and everything in between. Come talk with me or send e-mail if you have questions or want help thinking things through. Looking ahead Heres a rough outline of project steps with tentative deadlines: Study design prop ...
	b.lect2 Penn State, STAT 401 Excerpt: ... Basic Statistical Concepts Outline Some Sampling Concepts Random Variables and Statistical Populations Simple Random and Stratied Sampling Sampling With and Without Replacement Non-representative Sampling Another sampling method for obtaining a representative sample is called stratied sampling. Michael Akritas Lecture 2 Chapter 1: Basic Statistical Concepts Outline Some Sampling Concepts Random Variables and Statistical Populations Simple Random and Stratied Sampling Sampling With and Without Replacement Non-representative Sampling Another sampling method for obtaining a representative sample is called stratied sampling. Denition A stratied sample consists of simple random sample s from each of a number of groups (which are non-overlapping and make up the entire population) called strata. Michael Akritas Lecture 2 Chapter 1: Basic Statistical Concepts Outline Some Sampling Concepts Random Variables and Statistical Populations Simple Random and Stratied Sampling Sampling Wi ...
	feb12 Calvin, M 243 Excerpt: ... Statistics Lincolns Birthday February 12, 2008 Outline 1. Task: to study a population by using a sample 2. Key terms: population, parameter, sample, statistic 3. Problem: to choose a representative sample 4. simple random sample s 5. sampling error 6. non-sampling error Homework - due Thursday, February 14 1. Read sections 2.1, 2.2. 2. Do problems 2.1,2,3,4,5. Useful R > sample(x,5,replace=F) Miscellaneous Notes Maryland primary poll referenced in class http:/www.usaelectionpolls.com/2008/polls/ pdfs/surveyusa-maryland-democrats-feb7to8.pdf Gallup Poll aspx http:/www.gallup.com/poll/104269/Gallup-Daily-Tracking-Election-2008. ...
	Chapter 01 - Sections 1 & 2 George Mason, STAT 250 Excerpt: ... ions & Sampling Observational studies and designed experiments have some fundamental differences Observational studies do not control the variable under analysis while designed experiments do Because variables are uncontrolled in an observational study, the results can only be associations Because variables are controlled in a designed experiment, the results can be conclusions of causation Helpful Hint: See discussion on Lurking Variables on p.15 of your textbook. Fall 2008 29 Studies, Observations & Sampling Obtaining a Simple Random Sample Usually only a part of the population can be analyzed A simple random sample is when every possible sample of size n out of a population of N has an equally likely chance of occurring Examples For a simple random sample of size n = 2 from a population size of N = 4, each of the 6 possible samples has an equally likely chance of occurring Fall 2008 30 Studies, Observations & Sampling Obtaining a Simple Random Sample Simple rando ...
	notes8 SUNY Stony Brook, AMS 102 Excerpt: ... AMS 102.7 Spring 2006 Jingyu Zou Elements of Statistics Lecture Notes # 8 Today Simple random sampling 1 Simple random sampling First remember that in this sampling method, every possible sample of the the same size has the same chance to be se ...
	lec5 Penn State, STAT 100 Excerpt: ... Types of samples Last time, we began to cover the various types of samples described in Sections 4.4-4.5. Thus far, we discussed the simple random sample . In a simple random sample , we select n units from this list in such a way that every unit in the population has an equal chance of being chosen. This kind of sample is easy to understand, but it's not always possible or practical to use it. There are many other sampling schemes. n = (100 2 .0)2 = 2500 So we would need a sample size of at least 2500. Given the sample size, make sure that you can find the margin of error. And, given the margin of error, make sure that you can find the sample size. Stratified random sample Divide the sample frame into "strata" and take a SRS within each stratum May be used to ensure that there is enough representation in certain small groups (e.g. states) Often used when data collection procedures must vary for different parts of the population. For example, we may need different procedures for civilian, noninstitutionali ...
	lec5_color Penn State, STAT 100 Excerpt: ... . Types of samples Last time, we began to cover the various types of samples described in Sections 4.4-4.5. Thus far, we discussed the simple random sample . In a simple random sample , we select n units from this list in such a way that every unit in the population has an equal chance of being chosen. This kind of sample is easy to understand, but its not always possible or practical to use it. There are many other sampling schemes. n = (100 2 .0)2 = 2500 So we would need a sample size of at least 2500. Given the sample size, make sure that you can find the margin of error. And, given the margin of error, make sure that you can find the sample size. Stratified random sample Divide the sample frame into strata and take a SRS within each stratum May be used to ensure that there is enough representation in certain small groups (e.g. states) Often used when data collection procedures must vary for different parts of the population. For example, we may need different procedures for civilian, noninst ...
	Lecture 12 - Simple Random Sampling and Stratif... Mich Tech, FW 2050 Excerpt: ... FW2050: Measuring Forest Resources Lecture 12: Sampling Lecture Note 2. Simple random sampling Assumptions Every possible combination of sampling units has an equal and independent chance of being selected. The selection of a particular unit to b ...
	lec18 UC Davis, STATS 13 Excerpt: ... STATISTICS 13 Lecture 18 May 12 Review Types of Sampling -Observational Study -Controlled Experiments Sampling Methods -Simple Random Sampling -Stratified, Clustering, 1-k-Sytematic Sampling Sampling Distribution of a Statistic Example: Sampling Distribution of Sample Mean Suppose in a family there are 3 kids with ages: 8, 10, 12. A simple random sample with replacement of 2 kids is chosen and the sample mean x of the age is calculated The possible values for x is 8, 9, 10, 11, 12 with probability distribution: p(8)=1/9, p(9)=2/9, p(10)=3/9, p(11)=2/9, p(12)=1/9 Possible sample 8,8 8,10 8,12 10,8 10,10 10,12 12,8 12,10 12,12 probability 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 8 9 10 9 10 11 10 11 12 x Example: Sampling Distribution of Sample Proportion Suppose in an urn there are three red balls: r1, r2,r3 and two black balls: b1, b2. A simple random sample without replacement of 3 balls is chosen, and the ^ sample proportion p of the red balls is calculated ^ Possible values of p are 1, 2/3, 1/3 with ...
	Chapter 1 notes Georgia Perimeter, MATH 1431 Excerpt: ... Statistics Two kinds of Statistics Data consist of information coming from observations, counts, measurements, or responses. Statistics is the science of collecting, organizing, and interpreting data in order to make decisions. Descriptive Statisticsthis involves the organization, summarization, and display of data Inferential statistics-this involves using a sample to draw conclusions about a population Statistics Two kinds of Statistics Population-collection of all outcomes, responses, measurements, or counts that are of interest. Sample-a subset (part) of a population Population Sample Statistics Simple Random Sampling Censusinformation on the entire population Samplinginformation on part of the population Probability samplinguse of a coin or die to determine the sample Simple random samplinga sampling procedure for which each possible sample of a given size is equally likely to the one obtained Simple random sample a sample obtained by simple random sampling With replacementme ...