May 1, 2024
3 minute read
Statistical variables are mathematical characteristics of a population or sample. They can be used to describe the central tendency, variability, and distribution of data. Statistical variables are often used in research and analysis to make inferences about a population from a sample.
Types of Statistical Variables
There are two main types of statistical variables: categorical and numerical. Categorical variables are variables that can be divided into distinct categories, such as gender, race, or political affiliation. Numerical variables are variables that can take on any value within a range, such as age, height, or income.
Measures of Central Tendency
Measures of central tendency are used to describe the average or typical value of a data set. The most common measures of central tendency are the mean, median, and mode.
- The mean is the sum of all the values in a data set divided by the number of values.
- The median is the middle value in a data set when the values are arranged in order from smallest to largest.
- The mode is the value that occurs most frequently in a data set.
Measures of Variability
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Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Statistical Variables.
Provides a comprehensive overview of probability and mathematical statistics, including topics such as probability theory, statistical inference, and regression analysis.
Provides a comprehensive overview of statistical methods used in psychology, including descriptive and inferential statistics, as well as advanced topics such as multivariate analysis and structural equation modeling.
Provides a comprehensive overview of Bayesian statistics, including topics such as Bayesian inference, Bayesian modeling, and Markov chain Monte Carlo methods.
Provides a comprehensive overview of statistical concepts and methods used in the social and behavioral sciences. It covers descriptive and inferential statistics, as well as special topics such as nonparametric tests and Bayesian statistics.
Provides a comprehensive overview of statistical analysis of categorical data, including topics such as chi-square tests, logistic regression, and log-linear models.
Provides a comprehensive overview of statistical analysis of longitudinal data, including topics such as generalized estimating equations, mixed effects models, and marginal models.
Provides a comprehensive overview of data analysis using regression and multilevel/hierarchical models, including topics such as linear regression, logistic regression, and Bayesian regression.
Provides a comprehensive overview of statistical methods used in bioinformatics, including topics such as sequence analysis, microarrays, and next-generation sequencing data analysis.
Provides a comprehensive overview of statistical methods used in medical research, including topics such as clinical trials, survival analysis, and meta-analysis.
Provides a comprehensive overview of statistical methods for survival data analysis, including topics such as Kaplan-Meier survival curves, Cox proportional hazards models, and competing risks models.
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