Correlation & Regression: Learn With Practical Examples

Hello Friends,

We can use scatter plots to understand the relationships between variables, but it is applied only for obvious relationships like Temperature and Viscosity. Sometimes, it is not possible to comment about relationship between variables only looking at the graph.

The “CORRELATION & REGRESSION” are very important mathematical concepts to define relationship between variables. This is the topic for video.

I have tried to explain these concepts with the help of practical examples which will be very easy to understand. I have also explained the procedure about how to create a “CORRELATION & REGRESSION ANALYSIS” in Microsoft Excel. Everything is with steps, snapshots and examples, which will be very easy to understand.

I have also covered statistics part like how to read and understand “SIGNIFICANCE F and P-values”

I am sure, you will liked it.

Hello Friends,

            By considering all your valuable comments, I am advancing topic of “Nonlinear Regression” in this video, mainly comments from the video on “Correlation and Regression”. This video is mainly focused on Regression analysis, it’s types and Nonlinear Regression in very detailed along with practical example.

            Nonlinear Regression analysis is used to mathematically describe the nonlinear relationship between a response variable and one or more predictor variables. Specifically, use nonlinear regression instead of “ordinary least squares regression” when you cannot adequately model the relationship with linear parameters.

            I am going to explain this tool with practical example for easy understanding and better clarity. This video contains following topics:

1) What is Regression Analysis and their types?

2) Brief explanation all types of Regression Analysis methods

3) When to use Nonlinear Regression Analysis?

4) Data considerations for Nonlinear Regression

5) Nonlinear Regression Analysis with Practical Example in Microsoft Excel

6) Interpretation of results from Regression analysis including R-Square, Significance F and p-values, Coefficients, Residuals and Best Fit Model for Nonlinear Regression

            I am sure, you will like it.

Hello Friends,

            By considering your valuable voting on community to select a topic that is important for you, we are going to continue the 2nd part of Nonlinear Regression in this video. In the 1st part of Nonlinear Regression, we had seen “What is nonlinear regression” and “It’s detailed illustration in Microsoft excel with practical example”.

            We are going to learn Nonlinear Regression Analysis in Minitab with the same practical example, we had seen in the last video. Nonlinear Regression analysis is used to mathematically describe the nonlinear relationship between a response variable and one or more predictor variables.

            I am going to explain this tool with practical example for easy understanding and better clarity. This video contains following topics:

1) Data considerations for Nonlinear Regression in Minitab

2) Nonlinear Regression Analysis with Practical Example in Minitab

3) Detailed Interpretation of results from Regression analysis in Session Window including Iterations of Convergence Criteria, Null hypothesis in Nonlinear Regression, Confidence Interval, Lack of fit test, S (Standard Deviation of Residuals) value and warning message

4) Detailed Interpretation of results from Regression analysis in Graph Window including Fitted Line plot, Residual plot, Histogram, Normal Probability Plot, Residuals versus the fitted values Plot, Residuals versus order Plot.

            I am sure, you will like it.

Hello Friends,

            We are continuing to learn the topic of Regression analysis by considering your valuable comments. So far, as a part of Regression Analysis, we had seen detailed study of Ordinary Least Square Regression and Nonlinear Regression.

            In this video, we are going to learn next type of Regression analysis i.e. Logistic Regression Analysis. There will be series of videos explaining Logistic Regression Analysis with its application, various types of it and detailed illustration of each type with practical example for easy understanding from this video onward.

            This video contains following topics:

1) What is Logistic Regression Analysis?

2) Types of Logistic Regression Analysis with Example

3) Link Function in Logistic Regression Analysis

4) Data Considerations for Logistic Regression Analysis

            I am sure, you will like it.

Hello Friends,

            We had started learning of Logistic Regression analysis from last video along with its introduction and various types of it.

            In this video, we are going to learn 1st type of Logistic Regression analysis i.e. Binary Logistic Regression. The Binary Logistic Regression is used to perform logistic regression on a binary response variable.

            A binary variable only has two possible values, such as Pass or Fail.

            This video contains following topics:

1) What is Binary Logistic Regression Analysis?

2) Data considerations for Binary Logistic Regression

3) Detailed Illustration of Binary Logistic Regression Analysis with Practical Example

4) Detailed Procedure for Analysis in Minitab

5) Interpretation of Results in Session Window

6) Interpretation of Results in Graph Window

            I am sure, you will like it.

Hello Friends,

             From this video, we are going to learn the next important tool in Regression Analysis, that is, Multiple Regression. This is a widely used tool in the Analyze phase to see the relationship between predictors and response.

             Therefore, I am going to explain this important tool with the help of a practical example in Minitab for easy understanding and better clarity.

             Multiple regression is used to see the impact of multiple variables, predictors, or factors on a single response.

             If the number of predictors is large, then before fitting a regression model with all the predictors, you should use stepwise or best subsets model-selection techniques. These techniques are used to screen out predictors that are not associated with the responses.

            This video contains the following topics:

• What is Multiple Regression?

• Data considerations for Multiple Regression

• Example of Multiple Regression

• What is the Best Subsets Regression?

• Best Subsets Regression with Practical Example in Minitab

• Detailed interpretation of results from Best Subsets Regression

Hello Friends,

             In the last video on Multiple Regression Analysis, we had seen Best Subsets Regression to identify models that adequately fit your data, with as few predictors as possible by a Practical Example.

             In this video, we are going to learn the use of Multiple Regression Analysis to analyze the impact of multiple predictors on our response Heat Flux in Minitab.

            This video contains the following topics:

• How to perform Multiple Regression Analysis in Minitab?

• Practical Example of Multiple Regression

• Detailed interpretation of results in the session window, and

• Detailed interpretation of results in the graph window.

This is a complete recording to perform Correlation and Regression in Microsoft Excel with the help of practical examples. This also contains a complete Excel Template for Correlation and Regression.