Detailed information and calculation of Pearson’s Correlation using Excel, Python, R and SPSS

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What is Pearson Correlation?

Pearson Correlation or Pearson Product Moment Correlation of (PPMC) or Bivariate correlation is the standard measure of correlation in statistics. It shows the linear relation between two sets of data. It answers the question in simple terms: can I draw a line graph to represent the data?


When we have a big dataset and excited to get started with analyzing it and building your machine learning model. Our machine gives an “out of memory” error while trying to load the dataset.


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Clustering is a statistical classification approach for the supervised learning. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters).


Artificial intelligence and Machine learning related concepts , applications touches every part of our day to day lives.

AI Application in day to day Life . Reference: link

Your smartphone uses artificial intelligence to comprehend human language and answer questions or act in response to your commands, but the potential of Artificial Intelligence goes above and beyond that. Apple introduced AI Powered software called SIRI, few people around the world understood its mainstream significance and thus it didn’t gain popularity immediately, also owing to the fact that being AI based, it needed to learn and evolve which required time. …


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Nowadays, the manufacturing industry faces significant transformations. Due to the rapid growth of the digital world and the broad application of data science, different human activity fields are pursuing improvement. Modern manufacturing is also referred to as Industry 4.0, manufacturing under the conditions of the fourth industrial revolution that resulted in data robotization, automation, and widespread use.
Every day, the amount of data to be stored and processed is increasing. Today’s manufacturing companies, therefore, need to find new solutions and use cases for this knowledge. Data, of course, brings its advantages to manufacturing businesses as it helps them automate large-scale processes…


In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (Label or dependent variable) and one or more exploratory variables (Features or response or independent variables). The case of one explanatory variable is called a simple linear regression. For more than one explanatory variable or response, the process is called multiple linear regression.


Logistic Regression is a statistical approach which is used for the classification problems. In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be combined to model several classes of events such as determining whether an image contains a cat, dog, lion, etc… Each object is detected in the image would be assigned a probability between 0 and 1 and the sum adding to one.

Difference between linear regression and logistic regression

Types of logistic regression:

  1. Binary (eg. Tumor Malignant or Benign)
  2. Multi-linear functions fails Class (eg. Cats, dogs or Sheep’s)


Interaction plots are used to understand the behavior of one variable depends on the value of another variable. Interaction effects are analyzed in regression analysis, DOE (Design of Experiments) and ANOVA (Analysis of variance).

Suresha HP

Machine Learning & Artificial Intelligence developer, researcher and educator with over 16 years experience in the Automotive and Manufacturing industry

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