ABI Bioinformatics Guide 2024
  • INTRODUCTION
    • How to use the guide
  • MOLECULAR BIOLOGY
    • The Cell
      • Cells and Their Organelles
      • Cell Specialisation
      • Quiz 1
    • Biological Molecules
      • Carbohydrates
      • Lipids
      • Nucleic Acids (DNA and RNA)
      • Quiz 2
      • Proteins
      • Catalysis of Biological Reactions
      • Quiz 3
    • Information Flow in the Cell
      • DNA Replication
      • Gene Expression: Transcription
      • Gene Expression: RNA Processing
      • Quiz 4
      • Chromatin and Chromosomes
      • Regulation of Gene Expression
      • Quiz 5
      • The Genetic Code
      • Gene Expression: Translation
    • Cell Cycle and Cell Division
      • Quiz 6
    • Mutations and Variations
      • Point mutations
      • Genotype-Phenotype Interactions
      • Quiz 7
  • PROGRAMMING
    • Python for Genomics
    • R programming (optional)
  • STATISTICS: THEORY
    • Introduction to Probability
      • Conditional Probability
      • Independent Events
    • Random Variables
      • Independent, Dependent and Controlled Variables
    • Data distribution PMF, PDF, CDF
    • Mean, Variance of a Random Variable
    • Some Common Distributions
    • Exploratory Statistics: Mean, Median, Quantiles, Variance/SD
    • Data Visualization
    • Confidence Intervals
    • Comparison tests, p-value, z-score
    • Multiple test correction: Bonferroni, FDR
    • Regression & Correlation
    • Dimentionality Reduction
      • PCA (Principal Component Analysis)
      • t-SNE (t-Distributed Stochastic Neighbor Embedding)
      • UMAP (Uniform Manifold Approximation and Projection)
    • QUIZ
  • STATISTICS & PROGRAMMING
  • BIOINFORMATICS ALGORITHMS
    • Introduction
    • DNA strings and sequencing file formats
    • Read alignment: exact matching
    • Indexing before alignment
    • Read alignment: approximate matching
    • Global and local alignment
  • NGS DATA ANALYSIS & FUNCTIONAL GENOMICS
    • Experimental Techniques
      • Polymerase Chain Reaction
      • Sanger (first generation) Sequencing Technologies
      • Next (second) Generation Sequencing technologies
      • The third generation of sequencing technologies
    • The Linux Command-line
      • Connecting to the Server
      • The Linux Command-Line For Beginners
      • The Bash Terminal
    • File formats, alignment, and genomic features
      • FASTA & FASTQ file formats
      • Basic Unix Commands for Genomics
      • Sequences and Genomic Features Part 1
      • Sequences and Genomic Features Part 2: SAMtools
      • Sequences and Genomic Features Part 3: BEDtools
    • Genetic variations & variant calling
      • Genomic Variations
      • Alignment and variant detection: Practical
      • Integrative Genomics Viewer
      • Variant Calling with GATK
    • RNA Sequencing & Gene expression
      • Gene expression and how we measure it
      • Gene expression quantification and normalization
      • Explorative analysis of gene expression
      • Differential expression analysis with DESeq2
      • Functional enrichment analysis
    • Single-cell Sequencing and Data Analysis
      • scRNA-seq Data Analysis Workflow
      • scRNA-seq Data Visualization Methods
  • FINAL REMARKS
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  1. STATISTICS: THEORY
  2. Random Variables

Independent, Dependent and Controlled Variables

PreviousRandom VariablesNextData distribution PMF, PDF, CDF

Last updated 9 months ago

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Independent and Dependent Random Variables

Independent Random Variables:

  • Definition: Random variables XXX and YYY are independent if the occurrence of one does not affect the probability distribution of the other.

  • Example: Let XXX be the outcome of rolling a fair six-sided die, and YYY be the outcome of flipping a fair coin. These events are independent since the outcome of the die roll does not influence the coin flip.

Dependent Random Variables:

  • Definition: Random variables XXX and YYY are dependent if the occurrence of one affects the probability distribution of the other.

  • Example: Let XXX be the amount of rainfall on a given day, and YYY be the water level in a nearby river. These variables are dependent because more rainfall likely increases the river's water level.

In a science experiment, there are three main types of variables, besides independent and dependent variables there is also the controlled variables.

The independent variable is the one that the researcher deliberately changes or manipulates. Its purpose is to observe the effect it has on another variable. For example, in an experiment testing the effect of light on plant growth, the amount of light is the independent variable.

The dependent variable is the one that is measured or observed in response to changes in the independent variable. Its purpose is to assess the effect of the independent variable. In the same plant growth experiment, the height of the plants is the dependent variable.

Controlled variables are variables that are kept constant throughout the experiment to ensure that the test results are reliable. The purpose of controlling these variables is to make sure that any observed changes in the dependent variable are due to the manipulation of the independent variable alone. In the plant growth experiment, controlled variables might include the type of plant, the amount of water given, the soil type, and the ambient temperature.