# Probability and discrete distributions

# Linear Algebra – Matrices

# Linear Algebra – Vectors

# Handling imbalanced classes

# IIMB Business Analytics and Intelligence

# The math behind ANN (ANN- Part 2)

# Artificial Neural Network – Part 1

# CART classification

# Curse of dimensionality

# CHAID decision trees

# K means clustering

# Exploratory factor analysis

# Stationarity tests

# Hierarchical Clustering

# Stationarity

# ARIMA

# Analytic Hierarchy Process

# Adoption of new product

# Customer Lifetime Value

# Inventory planning model

# Linear Programming

# Linear regression

# Part and partial correlation

# Logistic Regression

# KNN imputation

# Recommendation systems

# Chi Square test of independence

# Chi-Square goodness of fit test

# Analysis of variance (Anova)

# Hypothesis test for population parameters

# All you ‘really’ need to know | Python Notebook | Advanced – Pandas

# Table of Contents

# Why are basics important?

# Time series EDA

# A Not-so-Quick-but-Conceptual guide to Python | Notebook | Intermediate — Part 2

# Multicollinear analysis

# A Not-so-Quick-but-Conceptual guide to Python — Notebook | Intermediate | Part 1

# Multivariate Analysis

# Handling Google maps location data

# Get started with Python | Notebook for Beginner’s level

# Class size paradox

# Univariate Analysis on in-time

# Exporting dimensions to Excel from NX

This blog is the third part of a 3 part series on NX journalling

# Getting the dimensions of the selected Part

This blog is the second part of a 3 part series on NX journalling

# Selecting a part in NX Journaling

This blog post is the first part of 3 part series on NX Journaling

# Extracting data from mechanical models

Recently I came across problems trying to automate simple engineering routines, and the first step in a large number of these problems is extracting data from already existing models for further analysis.

# The boy girl paradox

Imagine that a family has two children, one of whom we know to be a boy. What then is the probability that the other child is a boy? The obvious answer is to say that the probability is 1/2—after all, the other child can only be *either* a boy *or* a girl, and the chances of a baby being born a boy or a girl are (essentially) equal. In a two-child family, however, there are actually four possible combinations of children: two boys (MM), two girls (FF), an older boy and a younger girl (MF), and an older girl and a younger boy (FM). We already know that one of the children is a boy, meaning we can eliminate the combination FF, but that leaves us with three equally possible combinations of children in which *at least* one is a boy—namely MM, MF, and FM. This means that the probability that the other child *is* a boy—MM—must be 1/3, not 1/2.