7.1Sampling Methods 7.2Introduction to Sampling Distribution 7.0 Sampling and Sampling Distribution.

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7.1Sampling Methods 7.2Introduction to Sampling Distribution 7.0 Sampling and Sampling Distribution Slide 2 BQT 173 Institut Matematik Kejuruteraan, UniMAP 2 SamplingWhy?FramePlanTypes Slide 3 WHY?? less time consuming less costly less cumbersome more practical BQT 173 Institut Matematik Kejuruteraan, UniMAP 3 Slide 4 listing of items that make up the population data sources such as population lists, directories, or maps Sampling Frame The way a sample is selected determines the quantity of information in the sample allow to measure the reliability or goodness of your inference Sampling Plan BQT 173 Institut Matematik Kejuruteraan, UniMAP 4 Slide 5 Types of Samples BQT 173 Institut Matematik Kejuruteraan, UniMAP 5 Samples Non probability Samples ConvenientJudgement Probability Samples Simple Random SystematicStratifiedCluster Slide 6 BQT 173 Institut Matematik Kejuruteraan, UniMAP 6 Selection of elements is left primarily to the interviewer. Easy, inexpensive, or convenient to the sample limitations not representative of the population. Recommended for pre testing Q, generating ideas, insight @ hypotheses. Eg: a survey was conducted by one local TV stations involving a small number of housewives, white collar workers & blue collar workers. The survey attempts to elicit the respondents response towards a particular drama series aired over the channel. Slide 7 BQT 173 Institut Matematik Kejuruteraan, UniMAP 7 The population elements are selected based on the judgment of the researcher. From the judgment, the elements are representative of the population of interest. Eg: testing the consumers response towards a brand of instant coffee, Indocafe at a wholesale market. Slide 8 BQT 173 Institut Matematik Kejuruteraan, UniMAP 8 Definition: If a sample of n is drawn from a population of N in such a way that every possible sample of size n has the same chance of being selected, the sample obtained is called a simple random sampling. N number of units in the population n number of units in sample Slide 9 BQT 173 Institut Matematik Kejuruteraan, UniMAP 9 Do not have any bias element (every element treated equally). Target population is homogenous in nature (the units have similar characteristics) Eg: canteen operators in primary school, operators in cyber cafes, etc.. Disadvantages: Sampling frame are not updated. Sampling frame are costly to produce. Impractical for large study area. Slide 10 Definition: a sample obtained by randomly selecting one element from the 1 st k elements in the frame & every k th element there is called a 1ink systematic sample, with a random start. k interval size BQT 173 Institut Matematik Kejuruteraan, UniMAP 10 Slide 11 Systematic Sampling BQT 173 Institut Matematik Kejuruteraan, UniMAP 11 eg: Let say, there are a total of N=500 primary school canteen operators in the Klang Valley in 1997 who are registered with the Ministry of Education. We required a sample of n=25 operators for a particular study. Step 1: make sure that the list is random(the name sorted alphabetically). Step 2: divide the operators into interval contain k operators. k = population size = 500/25 = 20 for every 20 operators sample size selected only one to represent that interval Step 3: 1 st interval only, select r at random. Let say 7. operators with id no.7 will be 1 st sample. The rest of the operators selected in remaining intervals will depend on this number. Step 4: after 7 has been selected, the remaining selection will be operators with the following id no. Slide 12 Stratified Sampling BQT 173 Institut Matematik Kejuruteraan, UniMAP 12 Definition: obtained by separating the population elements into non overlapping groups, called strata, & then selecting a random sample from each stratum. Large variation within the population. Eg: lecturers that can be categorized as lecturers, senior lecturers, associate prof & prof. Slide 13 Stratified Sampling Step 1: segregate population units into individual characteristics. Each unit appears in into1 group/stratum. This what meant by non overlapping. Denote the units as N h (population stratum size). Step 2: obtain random sampling frames which are current for each stratum. Step 3: each frame select a random sample using one of method that has been discussed. Select the unit proportionately, i.e large stratum size should be represented with more units compared to small stratum size. Denote the represent unit of a particular stratum as n h (sample stratum size). Slide 14 Cluster Sampling BQT 173 Institut Matematik Kejuruteraan, UniMAP 14 Definition: probability sample in which each sampling unit is a collection, @ cluster of elements. Advantages can be applied to a large study areas  practical & economical.  cost can be reducedinterviewer only need to stay within the specific area instead travelling across of the study area. Disadvantages higher sampling error. Slide 15 Sampling distribution is a probability distribution of a sample statistic based on all possible simple random sample of the same size from the same population. 7.2 Introduction to Sampling Distribution 7.0 Sampling and Sampling Distribution Slide 16 7.2.1Sampling Distribution of Mean ( ) BQT 173 Institut Matematik Kejuruteraan, UniMAP 16 Standard deviation of sampling distribution Mean of sample mean, Mean of sampling distribution = mean of population Variance of sampling distribution Slide 17 Central Limit Theorem BQT 173 Institut Matematik Kejuruteraan, UniMAP 17 If we are sampling from a population that has an unknown probability distribution, the sampling distribution of the sample mean will still be approximately normal with mean and standard deviation, if the sample size n is large. Slide 18 Properties and shape of sampling distribution of the sample mean BQT 173 Institut Matematik Kejuruteraan, UniMAP 18 n 30, the sampling distribution of the sample mean is normally distributed n < 30, the sampling distribution of the sample mean is normally distributed if the sample is from the normal population and variance is known Slide 19 BQT 173 Institut Matematik Kejuruteraan, UniMAP 19 tdistribution with n1 degree of freedom if the sample is from the normal population but the variance is unknown The value of Z Slide 20 Example BQT 173 Institut Matematik Kejuruteraan, UniMAP 20 The amount of time required to change the oil and filter of any vehicles is normally distributed with a mean of 45 minutes and a standard deviation of 10 minutes. A random sample of 16 cars is selected. What is the standard error of the sample mean to be? What is the probability of the sample mean between 45 and 52 minutes? What is the probability of the sample mean between 39 and 48 minutes? Find the two values between the middle 95% of all sample means. Slide 21 BQT 173 Institut Matematik Kejuruteraan, UniMAP 21 Slide 22 BQT 173 Institut Matematik Kejuruteraan, UniMAP 22 Slide 23 Sampling Distribution of the Sample Proportion where N = total number of elements in the population; X = number of elements in the population that possess a specific characteristic; n = total number of elements in the sample; and x = number of elements in the sample that possess a specific characteristic 23 and Slide 24 Sampling Distribution of Sample Proportion, for Infinite Population The probability distribution of the sample proportion, is called its sampling distribution. It gives various values that can assume and their probabilities. For the large values of n (n 30), the sampling distribution is very closely normally distributed. 24 Mean of the Sample Proportion The mean of the sample proportion, is denoted by and is equal to the population proportion, p. Slide 25 Standard Deviation of the Sample Proportion where p is the population proportion, and n is the sample size. 25 Slide 26 For a small values of n: the population is binomial distributed, 26 The value of Z : Slide 27 Example 4.2 The National Survey of Engagement shows about 87% of freshmen and seniors rate their college experience as good or excellent. Assume this result is true for the current population of freshmen and seniors. Let be the proportion of freshmen and seniors in a random sample of 900 who hold this view. Find the mean and standard deviation of. 27 Slide 28 Solution: Let p the proportion of all freshmen and seniors who rate their college experience as good or excellent. Then, p = 0.87 and q = 1 p = 1 0.87 = 0.13 The mean of the sample distribution of is: 28 The standard deviation of : Slide 29 Sampling Distribution for the Difference between Two Means 29 Suppose we have two populations, and which are normally distributed. and Sampling distribution for : and Slide 30 30 MEAN VARIANCE Slide 31 31 Therefore the distribution of can be written as: Slide 32 Sampling Distribution for the Difference between Two Proportions 32 Now say we have two binomial populations with proportion of successes and Slide 33 33 Variance Using the Central Limit Theorem, the distribution of is Slide 34 Example 4.4 BQT 173 Institut Matematik Kejuruteraan, UniMAP 34 A certain change in a process for manufacture of component parts was considered. It was found that 75 out of 1500 items from the existing procedure were found to be defective and 80 of 2000 items from the new procedure were found to be defective. If one random sample of size 49 items were taken from the existing procedure and a random sample of 64 items were taken from the new procedure, what is the probability that the proportion of the defective items from the new procedure exceeds the proportion of the defective items from the existing procedure? proportions differ by at most 0.015? the proportion of the defective items from the new procedure exceeds proportion of the defective items from the existing procedure by at least 0.02? Slide 35 BQT 173 Institut Matematik Kejuruteraan, UniMAP 35 Solution: Slide 36 BQT 173 Institut Matematik Kejuruteraan, UniMAP 36