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MODULE 4
  • Angelica Solinap

  • 問題数 32 • 1/16/2025

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    問題一覧

  • 1

    2 types of Estimate

    Point Estimate and Interval Estimate

  • 2

    Is a collection, or set, of individuals, objects, or measurements whose properties are to be analyzed. It is the totality of the observation.

    Population

  • 3

    This type of sampling specified number of persons of certain types included in the sample. In many sectors of the population are represented. However, the representation is doubtful are no guidelines in the selection of the respondents

    Quota Sampling

  • 4

    It is sometimes called area sampling because it is applied on geographical basis. it will give more precise results particularly when each cluster contains a more varied mixture and when one cluster is nearly like the other.

    Cluster Sampling/Sample

  • 5

    measures the half width of a confidence interval for a population mean

    Margin of Error

  • 6

    3 Types of Probability Sampling

    Systematic Sampling, Stratified Random Sampling and Cluster Sample

  • 7

    has values that are uncountably infinite and form a continuous range of values. They can take on any value within a range. In fact, there are infinite values between any two values. This data type often occurs when you measure a quantity on a scale.

    Continuous Random Variable

  • 8

    is used to assess how precise some estimate is of a population proportion or a population mean.

    Margin of Error

  • 9

    It is based on certain criteria laid down by the researcher. People who satisfy the criteria are interviewed. it is determining the target population of those who will be taken for the study. The respondents are chosen on the basis of their knowledge of the information desired.

    Purposive Sampling

  • 10

    is a value of a sample statistics that is used as a single estimate of a population parameter

    Point Estimate

  • 11

    Characteristics such as the population mean, the population variance, and the population proportion

    Parameters of the Population

  • 12

    Is a numerical measurement describing some characteristic of a population

    Parameter

  • 13

    2 Types of Random Variable

    Discrete Random Variable and Continuous Random Variable

  • 14

    is a variable whose value depends on the outcome of a probabilistic experiment. Its value is a priori unknown, but it becomes known once the outcome of the experiment is realized.

    Random Variable

  • 15

    Is a subset of a population. It is a smaller group representing the population having identical characteristics from which it was taken. A sample is taken since the study of a complete population may be too costly, time-consuming, and full of unpredictable inaccuracies.

    Sample

  • 16

    (3) Different Types of Non-Probability Sampling

    Purposive Sampling, Convenience Sampling and Quota Sampling

  • 17

    Is a process of picking out people in the most convenient and fastest way to get reactions immediately.

    Convenience Sampling

  • 18

    is a formula for estimating a parameter

    Estimator

  • 19

    It is a more efficient sampling procedure wherein the population is grouped into a more or less homogeneous classes or strata in order to avoid the possibility of drawing samples whose members come from one stratum.

    Stratified Random Sampling

  • 20

    This is a technique of sampling in which every nth name in the list may be selected to be included in the sample which serves a random start.

    Systematic Sampling

  • 21

    is a range of values that brackets the population parameter with some probability

    Interval Estimate

  • 22

    the sample is not a proportion of the population and there is no system in selecting the sample. The selection depends on the situation

    Non-Probability Sampling

  • 23

    The process of using a sample to make inferences about a population

    Statistical Inference

  • 24

    Characteristics of the sample such as the sample mean, the sample variance, and the sample proportion

    Sample Statistics

  • 25

    the sample is a proportion of the population and such sample is selected from the population by means of systematic way in which every element of the population has a chance of being included in the sample.

    Probability Sampling

  • 26

    a variable where chance determines its value.

    Random Variable

  • 27

    measures the preciseness of an estimate of a population mean

    Standard Error

  • 28

    is the standard deviation of a sampling distribution

    Standard of Error

  • 29

    the sample size of each stratum is equal to the subgroupʼs proportion in the population as a whole.

    Proportionate Sampling

  • 30

    has distinct values that are countable and finite or countably infinite. This data type often occurs when you are counting the number of event occurrence

    Discrete Random Variable

  • 31

    Interval estimates of population parameters

    Confidence Interval

  • 32

    is a particular value that we calculate from a sample by using an estimator.

    Estimate