Measures of Variability/Central Tendency For Certain Categories

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SUMMARY

The discussion focuses on identifying the highest-level measures of central tendency and variability for three specific data types: ordinal (Response to light), continuous (HDL levels), and nominal (Smoker status). Key measures include mean, median, and mode for central tendency, while variance and standard deviation are used for variability. The conversation emphasizes the importance of data type classification, noting that nominal data does not support mean or median calculations, while ordinal data can utilize the median. Additionally, continuous data allows for both mean and median calculations, along with standard deviation and variance.

PREREQUISITES
  • Understanding of data types: nominal, ordinal, and continuous
  • Familiarity with measures of central tendency: mean, median, mode
  • Knowledge of measures of variability: variance and standard deviation
  • Basic concepts of regression analysis and error terms
NEXT STEPS
  • Explore the application of Box-Cox transformations for normality adjustments
  • Study Least-Absolute deviations versus Least-Squares criteria in regression analysis
  • Research the implications of using median point estimates in statistical analysis
  • Learn about the classification of data types and their impact on statistical measures
USEFUL FOR

Statisticians, data analysts, and researchers involved in data analysis and interpretation, particularly those working with mixed data types in statistical modeling.

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Homework Statement



Suppose that I have the following groups/categories with how each is measured next to them:

Response to light – measured on a scale of 1 to 4
HDL levels – continuous value from 1 to 100
Smoker status - smoker or non-smoker

The question is as follows:

What are the highest-level measures of central tendency and variability that can be used?



Homework Equations



If descriptive, equations would not apply.

The Attempt at a Solution



I know that central tendency includes mean, mode, and median and variability includes variance and standard deviation, but could someone please tell me what is meant by “highest-level measures”? Do I include the descriptive term (for example, central tendency answer "mean") or an actual measurement?

Thank you.
 
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By “highest-level measures” I would assume that all the data is incorporated in the mean/variance estimate.

Solutions:

Answer 1: Use standard regression theory although the error terms (difference between fitted and observed) is not precisely normal (a mixture of dummy variables, categorical data and continuous). The Standard Error of Regression is the risk measure, the mean is given by the linear regression equation.

Answer 2: Use the error terms from answer 1 as an input to a Box-Cox Analysis of transformation. Use the proposed transformation on the y-variable to correct (or approximately adjust) for normality issues.

Answer 3: Use Least-Absolute deviations in place of a Least-Squares criteria. Apply risk measures (like average median deviation) and a median point estimate as discussed in the literature.
 
Last edited:
Soaring Crane said:

Homework Statement



Suppose that I have the following groups/categories with how each is measured next to them:

Response to light – measured on a scale of 1 to 4
HDL levels – continuous value from 1 to 100
Smoker status - smoker or non-smoker

The question is as follows:

What are the highest-level measures of central tendency and variability that can be used?



Homework Equations



If descriptive, equations would not apply.

The Attempt at a Solution



I know that central tendency includes mean, mode, and median and variability includes variance and standard deviation, but could someone please tell me what is meant by “highest-level measures”? Do I include the descriptive term (for example, central tendency answer "mean") or an actual measurement?

Thank you.

I'm not sure why the previous poster gave what he/she did - not relevant.
The question is essentially assessing your ability to determine the type of data that would be recorded. If the data is nominal (strictly categories) you wouldn't use mean or median for central tendency, nor would standard deviation qualify for variability. If the data is ordinal (can be arranged low to high) you could use median for central tendency. If the data were continuous (numerical) both mean and median would apply for central tendency, and standard deviation/variance for variability.
 

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