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Data: Types and Distribution01:19

Data: Types and Distribution

2.1K
In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
47.0K
Review and Preview01:13

Review and Preview

12.0K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
12.0K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
40.0K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

302
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
302
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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Data: Big and Small.

Jan Jones-Schenk

    Journal of Continuing Education in Nursing
    |January 31, 2017
    PubMed
    Summary
    This summary is machine-generated.

    Leaders need to understand organizational data for effective forecasting. Integrating big data with small data and triangulating multiple sources ensures better decision-making for professional development.

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    Area of Science:

    • Healthcare Leadership
    • Data Analytics
    • Professional Development

    Background:

    • Big data is a prevalent topic in leadership discussions.
    • Effective organizational planning and forecasting rely on understanding available data.
    • Professional development leaders must grasp data's role in strategic decision-making.

    Purpose of the Study:

    • To highlight the importance of data literacy for leaders in professional development.
    • To emphasize the need for integrating various data types for robust decision-making.
    • To guide leaders on leveraging data for improved forecasting and planning.

    Main Methods:

    • Review of current leadership trends in data utilization.
    • Discussion on the integration of diverse data sets (big and small data).
    • Emphasis on data triangulation for enhanced predictive accuracy.

    Main Results:

    • Big data alone is insufficient for informed decision-making.
    • Collaboration and data integration significantly enhance predictive capabilities.
    • Accessing and triangulating small data alongside big data is crucial.

    Conclusions:

    • Leaders must develop a comprehensive understanding of organizational data.
    • Effective forecasting and planning require a strategic approach to data integration.
    • Triangulating multiple data sources leads to more reliable and effective decisions.