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Population - EDA
Civil Engineering (BSCE 01)
Ateneo de Davao University
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NO.: population Raw Data DATE: Non. Prohability Sampling techniques 0 Convenience Sampling Sample Samples are chosen based on converience Statistic 2 Voluntary Response Sampling Participants Want to be included in the Definition of Terms Estimation EX. Election voting. Population in the area of interest 3 Purposive Sampling. Parameter something that the entirety Knowing what participants you want in your should have. sample and making sure that they are part of the say Sample A portion of the population E snowball sampling Statistic Value that gives a glood estimation When participant is mavailable and asking for of the bigger picture (paramera) a recommendation for other possible participants. Biased Sample Sample taken not in equal chances Biases In favor of som ething (D selection Bias Prejudices with the population There is a personal stereotype against a Randomness Participants in the population have characteristic of the participant. an equal chance of being in the sample. Ev. Job discrimination Uncertainly Discrepancy in randum representation 2 Confirmation Blas Representative Sample sample from Strattled R. Only fixated on the characteristic which Randam Sampling Teanniques affinms your beliefs you 1 Simple Random Samp ling 3 surviviship Blas An participants has an equal chance of Data sample are only those that survived being selected. EX. Fishbuwl Airplane Annu Dilemina 2 systematic Random Sampling Descriptive Statistics Describes the data there is a pattern in sampling selection. does not make generalization for other set of data Ex. Every is droscin from the whole, Inferential Statistics Inferencing the data B Cluster Random Sampling. Makes generalizations for other sets of data, felection of one group to represent the Central Tendency Sample mean (statistics entire popula tim Ex. Geographical Location a) Mean population Mean (parameta) 4 Stratified Random Sampling b) Median selection is based a propertions of c) Mode samplean population X mean intro M the population to avoid M as underrepresentation overrepresentation n 00 sample Strata sampling units are not homo geneous size VICTORY that divide themselves into homogeneous groups NO.: population Raw Data DATE: Non. Prohability Sampling techniques 0 Convenience Sampling Sample Samples are chosen based on converience Statistic 2 Voluntary Response Sampling Participants Want to be included in the Definition of Terms Estimation EX. Election voting. Population in the area of interest 3 Purposive Sampling. Parameter something that the entirety Knowing what participants you want in your should have. sample and making sure that they are part of the say Sample A portion of the population E snowball sampling Statistic Value that gives a glood estimation When participant is mavailable and asking for of the bigger picture (paramera) a recommendation for other possible participants. Biased Sample Sample taken not in equal chances Biases In favor of som ething (D selection Bias Prejudices with the population There is a personal stereotype against a Randomness Participants in the population have characteristic of the participant. an equal chance of being in the sample. Ev. Job discrimination Uncertainly Discrepancy in randum representation 2 Confirmation Blas Representative Sample sample from Strattled R. Only fixated on the characteristic which Randam Sampling Teanniques affinms your beliefs you 1 Simple Random Samp ling 3 surviviship Blas An participants has an equal chance of Data sample are only those that survived being selected. EX. Fishbuwl Airplane Annu Dilemina 2 systematic Random Sampling Descriptive Statistics Describes the data there is a pattern in sampling selection. does not make generalization for other set of data Ex. Every is droscin from the whole, Inferential Statistics Inferencing the data B Cluster Random Sampling. Makes generalizations for other sets of data, felection of one group to represent the Central Tendency Sample mean (statistics entire popula tim Ex. Geographical Location a) Mean population Mean (parameta) 4 Stratified Random Sampling b) Median selection is based a propertions of c) Mode samplean population X mean intro M the population to avoid M as underrepresentation overrepresentation n 00 sample Strata sampling units are not homo geneous size VICTORY that divide themselves into homogeneous groups
Population - EDA
Course: Civil Engineering (BSCE 01)
University: Ateneo de Davao University
