Skip to content
  • HOME
  • BASIC STATISTICS
    • BASIC STATISTICS THEORY
      • DESCRIPTIVE STATISTICS
        • What is Measures of Frequency in Statistics?
        • what are the Measures of Central Tendency? -Mean, Median & Mode
        • What Are Measures of Variability With Examples
        • Measures of Shape – Skewness And Kurtosis
      • INFERENTIAL STATISTICS
        • IMPORTANCE OF INFERENTIAL STATISTICS
          • Standard Error (SE), Standard Error of Mean, and Central Limit Theorem (CLT) 
          • Z scores, Z test, and Probability Distribution 
          • Brief Intro to T Test 
          • HYPOTHESIS TESTING 
        • CORRELATION COEFFICIENTS 
        • T-TESTS 
        • F TESTS
          • ONE WAY ANOVA 
          • FACTORIAL ANOVA 
          • ANOVA REPEATED MEASURES 
        • CHI-SQUARE 
    • BASIC STATISTICS APPLICATION
      • Descriptive Statistics in Python
      • inferential statistics in python
  • DATA EXPLORATION & PREPRATION
    • DATA EXPLORATION AND PREPRATION – THEORY
      • MISCELLANEOUS METHODS 
        • CONSOLIDATION OF DATASETS 
        • UNIVARIATE & BIVARIATE ANALYSIS 
        • OUTLIER TREATMENT 
        • MISSING VALUE TREATMENT 
      • FEATURE ENGINEERING 
        • FEATURE TRANSFORMATION 
        • FEATURE SCALING 
        • FEATURE CONSTRUCTION 
          • BINNING 
          • ENCODING 
          • OTHER DERIVED VARIABLES 
        • FEATURE REDUCTION 
          • FEATURE EXTRACTION 
          • FEATURE SELECTION
            • FILTER METHODS 
            • WRAPPER METHODS 
            • EMBEDDED METHODS 
    • DATA EXPLORATION AND PREPRATION – APPLICATION
      • Miscellaneous Methods In Python
  • MODELING
    • MODELING THEORY
      • SUPERVISED LEARNING MODELS
        • REGRESSION PROBLEMS
          • ENSEMBLE METHODS 
            • What Is Bagging In Machine Learning – Its Types & Limitations
            • STACKING 
            • BOOSTING 
          • LINEAR REGRESSION 
          • What Is Regularized Linear Regression In Machine Learning
          • DECISION TREES 
          • K NEAREST NEIGHBORS 
        • CLASSIFICATION PROBLEMS
          • LOGISTIC REGRESSION 
          • What Is Regularized Logistic Regression In Machine Learning
          • DECISION TREES 
          • Support Vector Machine ( Svm ) Algorithm In Machine Learning
          • ARTIFICIAL NEURAL NETWORKS 
          • K NEAREST NEIGHBORS 
          • Naive Bayes 
      • UNSUPERVISED LEARNING MODELS
        • CLUSTERING PROBLEMS
          • Hierarchical Clustering – How Does It Works And Its Types
          • What Is Dbscan Clustering Algorithm In Machine Learning
          • K-means Clustering In Machine Learning 
        • DIMENSIONALITY REDUCTION
          • Principal Component Analysis ( PCA ) – A Detailed Overview
        • ANOMALY DETECTION
          • Unsupervised Anomaly Detection Using Python 
      • TIME SERIES ANALYSIS
        • Exponential Smoothing Method – An Overview
        • What Is Time Series Data – Types, Usage & Components
        • A Quick Introduction To Averaging Methods
        • A Quick Overview To Arima Family
  • BUY OUR COURSE NOW
  • HOME
  • BASIC STATISTICS
    • BASIC STATISTICS THEORY
      • DESCRIPTIVE STATISTICS
        • What is Measures of Frequency in Statistics?
        • what are the Measures of Central Tendency? -Mean, Median & Mode
        • What Are Measures of Variability With Examples
        • Measures of Shape – Skewness And Kurtosis
      • INFERENTIAL STATISTICS
        • IMPORTANCE OF INFERENTIAL STATISTICS
          • Standard Error (SE), Standard Error of Mean, and Central Limit Theorem (CLT) 
          • Z scores, Z test, and Probability Distribution 
          • Brief Intro to T Test 
          • HYPOTHESIS TESTING 
        • CORRELATION COEFFICIENTS 
        • T-TESTS 
        • F TESTS
          • ONE WAY ANOVA 
          • FACTORIAL ANOVA 
          • ANOVA REPEATED MEASURES 
        • CHI-SQUARE 
    • BASIC STATISTICS APPLICATION
      • Descriptive Statistics in Python
      • inferential statistics in python
  • DATA EXPLORATION & PREPRATION
    • DATA EXPLORATION AND PREPRATION – THEORY
      • MISCELLANEOUS METHODS 
        • CONSOLIDATION OF DATASETS 
        • UNIVARIATE & BIVARIATE ANALYSIS 
        • OUTLIER TREATMENT 
        • MISSING VALUE TREATMENT 
      • FEATURE ENGINEERING 
        • FEATURE TRANSFORMATION 
        • FEATURE SCALING 
        • FEATURE CONSTRUCTION 
          • BINNING 
          • ENCODING 
          • OTHER DERIVED VARIABLES 
        • FEATURE REDUCTION 
          • FEATURE EXTRACTION 
          • FEATURE SELECTION
            • FILTER METHODS 
            • WRAPPER METHODS 
            • EMBEDDED METHODS 
    • DATA EXPLORATION AND PREPRATION – APPLICATION
      • Miscellaneous Methods In Python
  • MODELING
    • MODELING THEORY
      • SUPERVISED LEARNING MODELS
        • REGRESSION PROBLEMS
          • ENSEMBLE METHODS 
            • What Is Bagging In Machine Learning – Its Types & Limitations
            • STACKING 
            • BOOSTING 
          • LINEAR REGRESSION 
          • What Is Regularized Linear Regression In Machine Learning
          • DECISION TREES 
          • K NEAREST NEIGHBORS 
        • CLASSIFICATION PROBLEMS
          • LOGISTIC REGRESSION 
          • What Is Regularized Logistic Regression In Machine Learning
          • DECISION TREES 
          • Support Vector Machine ( Svm ) Algorithm In Machine Learning
          • ARTIFICIAL NEURAL NETWORKS 
          • K NEAREST NEIGHBORS 
          • Naive Bayes 
      • UNSUPERVISED LEARNING MODELS
        • CLUSTERING PROBLEMS
          • Hierarchical Clustering – How Does It Works And Its Types
          • What Is Dbscan Clustering Algorithm In Machine Learning
          • K-means Clustering In Machine Learning 
        • DIMENSIONALITY REDUCTION
          • Principal Component Analysis ( PCA ) – A Detailed Overview
        • ANOMALY DETECTION
          • Unsupervised Anomaly Detection Using Python 
      • TIME SERIES ANALYSIS
        • Exponential Smoothing Method – An Overview
        • What Is Time Series Data – Types, Usage & Components
        • A Quick Introduction To Averaging Methods
        • A Quick Overview To Arima Family
  • BUY OUR COURSE NOW
Read more about the article Z Scores: Definition, Formula & Uses
BASIC STATISTICS / IMPORTANT CONCEPTS / INFERENTIAL STATISTICS

Z Scores: Definition, Formula & Uses

Overview In n our previous post, concepts like the central Limit Theorem were discussed to give an understanding of how samples may take on a similar shape to the distribution…

0 Comments
September 6, 2022
Read more about the article Standard Error (Se) – Overview, Formula & Uses
INFERENTIAL STATISTICS

Standard Error (Se) – Overview, Formula & Uses

Overview In this blog post, three mathematical concepts are discussed to assist us in understanding how different Inferential Statistics work. They include Standard Error, Standard Error of Mean as well…

0 Comments
September 6, 2022
  • Go to the previous page
  • 1
  • 2
  • 3

Recent Posts

  • Factor Analysis – An Easy Overview With Example
  • A Quick Overview Of Boosting In Machine Learning
  • Inferential Statistical Analysis Using Python
  • A Quick Introduction To Averaging Methods
  • Unsupervised Anomaly Detection Using Python 
  • Principal Component Analysis ( PCA ) – A Detailed Overview
  • Hierarchical Clustering – How Does It Works And Its Types
  • What Is Dbscan Clustering Algorithm In Machine Learning
  • K-means Clustering In Machine Learning 
  • Support Vector Machine ( Svm ) Algorithm In Machine Learning

Categories

  • ANOMALY DETECTION
  • BASIC STATISTICS
  • BASIC STATISTICS APPLICATION
  • CLASSIFICATION PROBLEMS
  • CLUSTERING PROBLEMS
  • DATA EXPLORATION AND PREPRATION
  • DESCRIPTIVE STATISTICS
  • DIMENSIONALITY REDUCTION
  • ENSEMBLE METHODS
  • F-TEST
  • FEATURE CONSTRUCTION
  • FEATURE ENGINEERING
  • FEATURE REDUCTION
  • FEATURE SELECTION
  • IMPORTANT CONCEPTS
  • INFERENTIAL STATISTICS
  • MISCELLANEOUS METHODS
  • MODELING
  • REGRESSION PROBLEMS
  • SUPERVISED LEARNING
  • TIME SERIES ANALYSIS
  • Uncategorized
  • UNSUPERVISED LEARNING MODEL

Brainalyst provides services and online professional courses in Data Science, Machine Learning, Deep Learning and Artificial Intelligence.

Facebook Instagram Twitter Linkedin
USEFUL LINK
  • ABOUT US
  • SERVICES
  • BLOGS
  • CONTACT US
Menu
  • ABOUT US
  • SERVICES
  • BLOGS
  • CONTACT US
POLICIES
  • REFUND POLICY
  • TERM & CONDITION
  • PRIVACY POLICY
Menu
  • REFUND POLICY
  • TERM & CONDITION
  • PRIVACY POLICY
CONTACT INFORMATION
  • training@brainalyst.in
  • +91 7419915555