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  • Cleveree

  • 問題数 36 • 9/27/2024

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    問題一覧

  • 1

    means selecting the group that you will actually collect data from in your research.

    Sampling

  • 2

    is a variable that represents a quantity that is being manipulated in an experiment.

    INDEPENDENT VARIABLE (THE CAUSE)

  • 3

    a number that summarizes some aspect of the population as a whole.

    Parameter

  • 4

    is a variable that represents a quantity that is being manipulated in an experiment.

    DEPENDENT VARIABLE (THE EFFECT)

  • 5

    involves using existing data collected by someone else for a purpose different from the original intent. Researchers analyze and interpret this data to extract relevant information.

    SECONDARY COLLECTION

  • 6

    a listing of each data value or class of data values along with their relative frequencies.

    RELATIVE FREQUENCY DISTRIBUTION

  • 7

    The mean represents the average value of the dataset. It can be calculated as the sum of all the values in the dataset divided by the number of values.

    MEAN

  • 8

    is where the population is split into groups called strata, then a random sample is taken from each stratum.

    STRATIFIED SAMPLING

  • 9

    All data that are the result of measuring are quantitative continuous data assuming that we can measure accurately.

    CONTINUOUS DATA

  • 10

    is a listing of each data value or class of data values along with their frequencies.

    FREQUENCY DISTRIBUTION

  • 11

    stated as the statistical measure that represents the single value of the entire distribution or a dataset.

    CENTRAL TENDENCY

  • 12

    is the number of times a data value or groups of data values (called classes) occur in a data set.

    FREQUENCY

  • 13

    means selecting a sample size of "n" objects from the population so that every sample of the same size n has equal probability of being selected as every other possible sample of the same size from that population.

    SIMPLE RANDOM SAMPLING

  • 14

    is the branch of mathematics that deals with the techniques for collecting, analyzing, and drawing conclusions from data.

    Statistics

  • 15

    study where a researcher records or observes the observations or measurements without manipulating any variables. These studies show that there may be a relationship but not necessarily a cause-and-effect relationship.

    OBSERVATIONAL STUDY

  • 16

    where we list the entire population, then randomly pick a starting point at the nth object, and then take every nth value until the sample size is reached.

    SYSTEMATIC SAMPLING

  • 17

    is a table with two columns. One column lists the categories, and another column gives the frequencies with which the items in the categories occur (how many data fit into each category).

    FREQUENCY TABLE

  • 18

    involves the collection of original data directly from the source or through direct interaction with the respondents.

    PRIMARY DATA COLLECTION

  • 19

    is a number or attribute computed for each member of a population or of a sample.

    Measurement

  • 20

    is picking a sample that is conveniently at hand. For example, asking other students in your statistics course or using social media to take your survey. Most convenience samples will give biased views and are not encouraged.

    CONVENIENCE SAMPLING

  • 21

    All data that are the result of counting are called quantitative discrete data. These data take on only certain numerical values.

    DISCRETE DATA:

  • 22

    are always numbers. Quantitative data are the result of counting or measuring attributes of a population.

    QUANTITATIVE DATA

  • 23

    is ordinal, but you can now subtract one value from another and that subtraction makes sense. You can do arithmetic on this data.

    INTERVAL DATA

  • 24

    Median is the middle value of the dataset in which the dataset is arranged in the ascending order or in descending order.

    MEDIAN

  • 25

    is the frequency divided by n, the size of the sample. This gives the proportion of the entire data set represented by each value or class.

    RELATIVE FREQUENCY

  • 26

    are the result of categorizing or describing attributes of a population. Hair color, blood type, ethnic group, the car a person drives, and the street a person lives on are examples of qualitative data.

    QUALITATIVE DATA

  • 27

    where the population is split up into groups called clusters, then one or more clusters are randomly selected and all individuals in the chosen clusters are sampled.

    CLUSTER SAMPLE

  • 28

    is the process of collecting and evaluating information or data from multiple sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities

    DATA COLLECTION

  • 29

    the entire group that you want to draw conclusions about.

    Population

  • 30

    A study that involves some random assignment* of a treatment; researchers can draw cause and effect (or causal) conclusions. An experimental study may also be called a scientific study or an experiment.

    EXPERIMENTAL STUDY

  • 31

    Is categorical data that has no order or rank

    NOMINAL DATA

  • 32

    is numeric data that has a true zero, meaning when the variable is zero nothing is there. Most measurement data are ratio data.

    RATIO DATA

  • 33

    is a branch of statistics that deals with summarizing and describing the main features of a dataset.

    DESCRIPTIVE STATISTICS

  • 34

    is categorical data that has a natural order to it

    ORDINAL DATA

  • 35

    the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

    Sample

  • 36

    It involves making inferences, predictions, or generalizations about a larger population based on data collected from a sample of that population.

    INFERENTIAL STATISTICS