Tag Archives: descriptive statistics

Statistics with R for Business Analysts – Binomial Distribution

(R Tutorials for Citizen Data Scientist) Statistics with R for Business Analysts – Binomial Distribution The binomial distribution model deals with finding the probability of success of an event which has only two possible outcomes in a series of experiments. For example, tossing of a coin always gives a head or a tail. The probability …

Statistics with R for Business Analysts – Normal Distribution

(R Tutorials for Citizen Data Scientist) Statistics with R for Business Analysts – Normal Distribution In a random collection of data from independent sources, it is generally observed that the distribution of data is normal. Which means, on plotting a graph with the value of the variable in the horizontal axis and the count of …

Statistics with R for Business Analysts – Logistic Regression

(R Tutorials for Citizen Data Scientist) Statistics with R for Business Analysts – Logistic Regression The Logistic Regression is a regression model in which the response variable (dependent variable) has categorical values such as True/False or 0/1. It actually measures the probability of a binary response as the value of response variable based on the …

Statistics with R for Business Analysts – Multiple Regression

(R Tutorials for Citizen Data Scientist) Statistics with R for Business Analysts – Multiple Regression Multiple regression is an extension of linear regression into relationship between more than two variables. In simple linear relation we have one predictor and one response variable, but in multiple regression we have more than one predictor variable and one …

Statistics with R for Business Analysts – Linear Regression

(R Tutorials for Citizen Data Scientist) Statistics with R for Business Analysts – Linear Regression Regression analysis is a very widely used statistical tool to establish a relationship model between two variables. One of these variable is called predictor variable whose value is gathered through experiments. The other variable is called response variable whose value …

How to get Statistical Summary of a Dataset | Jupyter Notebook | Python Data Science for beginners

How to get Statistical Summary of a Dataset   A statistical summary is a set of descriptive statistics that provide an overview of the key features of a dataset. In this essay, we will go over the steps needed to get a statistical summary of a dataset in Python. The first step is to load …

Applied Forecasting in Python | Air Quality Dataset | ARMA Model | Temperature Prediction

Applied Forecasting in Python | Air Quality Dataset | ARMA Model | Temperature Prediction   Python is a powerful programming language that is widely used for data analysis and scientific computing. It has a large ecosystem of libraries and packages that provide a wide range of forecasting algorithms and tools. In this example, we will …

Data Science Coding | H2O in Python with Grid Search Cross Validation | IRIS Dataset

Data Science Coding | H2O in Python with Grid Search Cross Validation | IRIS Dataset H2O.ai is an open-source platform that provides a wide range of machine learning algorithms and tools for building, deploying, and managing models. It is written in Java and has APIs for several programming languages, including Python. Grid Search Cross Validation …

SKLEARN Gradient Boosting Classifier with Monte Carlo Cross Validation

SKLEARN Gradient Boosting Classifier with Monte Carlo Cross Validation   Gradient Boosting Classifier is a machine learning technique used to classify items into different categories. It is an ensemble method that combines the predictions of multiple weak models, such as decision trees, to make a final prediction. The technique uses an iterative process where each …

SKLEARN Gradient Boosting Classifier with Grid Search Cross Validation

SKLEARN Gradient Boosting Classifier with Grid Search Cross Validation   Gradient Boosting Classifier is a machine learning technique used to classify items into different categories. It is an ensemble method that combines the predictions of multiple weak models, such as decision trees, to make a final prediction. The technique uses an iterative process where each …