VCU STAT 210 - Lecture4_sec005 (78 pages)

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Lecture4_sec005



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Lecture4_sec005

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Pages:
78
School:
Virginia Commonwealth University
Course:
Stat 210 - Basic Practice of Statistics
Basic Practice of Statistics Documents
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STAT 210 Lecture 4 Sampling September 1 2017 Example to think about Of interest is to determine the proportion of all public buildings in Pittsburgh Pennsylvania that have central air conditioning What is the population of interest What is the parameter of interest Of interest is to determine the proportion of all public buildings in Pittsburgh Pennsylvania that have central air conditioning What is the population of interest A All buildings B All public buildings C All public buildings in Pittsburgh Pennsylvania D All public buildings in Pittsburgh Pennsylvania with central air conditioning Of interest is to determine the proportion of all public buildings in Pittsburgh Pennsylvania that have central air conditioning What is the population of interest All public buildings in Pittsburgh Pennsylvania What is the parameter of interest Of interest is to determine the proportion of all public buildings in Pittsburgh Pennsylvania that have central air conditioning What is the population of interest All public buildings in Pittsburgh Pennsylvania What is the parameter of interest The proportion of all public buildings in Pittsburgh Pennsylvania that have central air conditioning Test 1 Friday September 8 Questions from 1 00 to 1 10 Test from 1 10 to 1 50 papers due at 1 50 Covers chapters 1 and 2 pages 1 42 Combination of multiple choice questions and a few written questions Practice tests posted on Blackboard Practice Problems Pages 38 through 42 Relevant problems II 2 II 6 Recommended problems II 2 II 5 and II 6 Additional Reading and Examples Pages 26 through 29 Top Hat Sampling Procedures We select a sample of the population and only measure or contact the subjects in the sample Representative Sample The sample should be as representative of the population as possible meaning that the characteristics of the sample should mimic the characteristics of the population Bias Bias exists when some subjects or individuals are systematically favored over others A sample which is representative of the population should be free of bias If the sample is not representative then the results will be biased in favor of the responses of those which are overrepresented Selection Bias Selection bias occurs when one or more types of subjects are systematically excluded from the sample Nonresponse Bias When an individual randomly chosen to be a part of the sample cannot be contacted or fails or refuses to respond then we have a nonresponse bias Response Bias When respondents give inaccurate information or if the interviewer influences the subject to respond in a certain way due to the way the questions are phrased this is response bias Haphazard Samples A haphazard sample involves selecting a sample by some convenient mechanism that does not involve randomization A mall survey in which questionnaires are distributed to people as they walk through the mall or a campus survey in which students are questioned as they walk across campus are two examples of haphazard samples Volunteer Response Sample A volunteer response sample exists when subjects volunteer to be part of the study Examples include telephone call in polls internet surveys newspaper surveys call in talk show surveys etc The problem with volunteer response samples is that often those who choose to respond often have strong opinions most often negative opinions and hence volunteer response samples over represent those with strong opinions Bias Haphazard and volunteer response samples are particularly prone to bias particularly nonresponse bias Random Samples Samples in which the subjects are chosen randomly to be in the sample are often representative of the population and are for the most part free of bias When each subject of the population has a positive and equal probability of being selected for the sample then we are using a probability sampling design to select our sample This will reduce or eliminate bias Simple Random Sampling With simple random sampling we make a list of all possible individuals in the population and randomly choose n of the subjects in such a way that every set of n subjects has an equal chance of being selected for the sample This procedure is impartial meaning the interviewer has no discretion as to whom is to be included in the sample Simple Random Sampling 1 Label the individuals in the population from 1 to N Examples 1 N 8 Label 1 2 3 8 2 N 80 Label 01 02 03 79 80 3 N 636 Label 001 002 003 635 636 4 N 2198 Label 0001 0002 0003 2197 2198 Simple Random Sampling 1 Label the individuals in the population from 1 to N 2 Use a Table of Random Digits like on page 337 to randomly select a sample of n numbers between 1 and N These numbers correspond to the individuals who are selected to be in the sample Table of Random Digits Line 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 03316 81868 95761 51025 11937 84826 86382 75532 61181 32592 70915 11776 45785 39950 08708 41979 18883 14893 47487 59335 03547 48501 88006 76630 69618 90449 98253 25280 95725 94953 18510 22851 44592 32812 58104 35749 64951 55157 40719 90707 89868 74952 90322 78188 64952 56941 32307 12952 42880 54613 88692 52573 90056 36290 82853 32337 45403 22725 19909 90597 43163 69680 88604 24870 50842 00360 48394 79177 98481 79140 28918 85475 89579 96301 99562 77921 37570 42167 24130 85393 25595 46573 22368 10480 15011 01536 02011 81647 91646 69179 14194 62590 36207 20969 99570 91291 90700 99505 58629 16379 53340 87151 04312 18132 31685 45195 87201 00795 81982 85117 48409 62103 27611 41842 87136 62224 43742 98624 65956 41448 63553 09429 10365 07119 51085 02368 01011 52162 07056 48663 54164 32639 29334 02488 81525 29676 00742 05366 91921 00582 00725 69011 23306 69884 65795 25972 29576 20591 57392 50421 64121 50490 31893 02938 11486 84898 27649 76688 83485 87964 94383 50211 17297 48767 82226 82425 42514 79367 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