Skip to content
MMK TECHNOLOGIES

MMK TECHNOLOGIES

https://www.mmktech.io

  • Flat No.6h Orchid Block,6th Floor of Ceebros Garden,

    No.1/2,Vembuliamman Koil Street,Virugambakkam,Chennai – 600 092, Tamil Nadu

  • +91 9840922213

    Mon-Fri 10:00am-7:30pm

  • km301252@gmail.com

    24 X 7 online support
  • Home
  • Statistics
    • Back to Basics
      • Using Excel
    • Frequency Distribution
    • Theoretical Distributions
      • Discrete Prob. Distribution
      • Continuous Prob. Distribution
    • Skewness Calculation
    • Estimates(point & interval)
  • Hypothesis Testing
    • Using Z-Distribution
      • Infinite:n>30 & σ is known
      • Infinite: n >30 & σ is not known
      • Infinite: n < 30 & σ is known
      • Infinite: n < 30 & σ is not known
      • Finite: n>30 & σ is known
      • Finite: n >30 & σ is not known
    • Using t – Distribution
      • Infinite: n < 30 & σ is not known
      • Finite: n < 30 & σ is not known
      • Drawn from same: n < 30
      • Paired t :n<30 & σ unknown
    • Proportions
    • Diff between Parameters
    • Correlation Coefficient Test
  • Predictive Analytics
    • Learn EDA
    • Encoding techniques
      • Ordinal Encoder
      • Categorical Encoding
      • Binary Encoder
      • HashingEncoder
      • LeaveOneOut Encoder
      • TargetEncoder
      • ContrastEncoders
      • PolynomialEncoder
      • FrequencyEncoding
      • Other Encoders
    • Correlation
    • Least Square Method
    • Regression – Basics
  • Regressions
    • Simple Linear Regression
      • Using Excel
    • Multiple Linear Regression
      • MLR Problem
      • Using Excel
      • Using PSPP
      • Using SPSS
      • Using Python
      • Using Python-scipy.stats
      • Using R
      • Using Real Statistic Package
      • Using statsmodels.api
      • Using Scikit-learn
    • Logistic Regression
      • LR Problems
    • Decision Trees
      • CHAID
      • Py-DecisionTreeRegressor-1
      • Py-DecisionTreeRegressor-2
      • Python-DecisionTreeClassifier
    • LazyRegressor
    • Model Evaluation
  • Feature Selection
    • Filter Methods
      • 1.Variance
      • 2. Correlation based
      • 3 Anova (f Test)
      • 4.Chi-Square
      • 5.Univariate Selection
      • 6.Mutual Information
    • Embedded Method
      • 1. Decision Tree/RFC
      • 2. Lasso(L1 Regularization)
      • 3. Ridge (L2 Regularization)
    • Wrapper Method
      • 1.Forward Selection
      • 2. Backward Elimination
      • 3.Exhaustive Search
      • 3.Recursive Elimination
    • Hybrid Selection
  • Feature Scaling
    • Range based
      • tanH (Hyperbolic Tangent Activation)
    • Distribution based
      • Z-score Normalization
      • Robust Scaling
      • Quantile Transformation
    • Scaling to Shape
      • Square Transformation
      • Square Root Transformation
      • Exponential Transformation
    • Parametric Scaling
      • Transformations
    • Vector Normalizations
      • L1 Normalization – MLR
      • L1 Normalization + Logistic Regression
    • Other Scaling
      • Decimal Scaling
      • Winzorisation Scaling
  • About
  • Contacts
  • logout
  • Apply Now

Statistics

Copyright © All rights reserved. Theme Rasa Education by Creativ Themes