Linear regression analysis using orange, Missing value treatment, outlier, Normality, box plot draw

In this video, you will learn about linear regression analysis using orange software. Before doing linear regression, there are several steps that need to follow for effective regression modelling.
First, Outlier was checked in the dataset using outlier widgets.
Second, variables normality checking has been done using the normality curve.
Third, Descriptive statistics were observed.
Fourth, a Scatter plot has been done.
Fifth, a linear regression model has been done and visualised with the data table.
Last, the R2 value was observed using test and score widgets.
In this way, you will be able to build a regression model using orange.
#Orange
#LinearRegressionAnalysis
#MultipleLinearRegression
#MissingDataHandling
#OutlierCheckingandExcluding
#ScatterPlot
#ModelTesting
#NormaliTest
#NormalDistributionCurve

[@juanalfredoblancasvelazque5553]

Thank you for share your example.

[@CaptainDande]

great video thank you !

[@atanumanna5974]

It is good analysis technique. I will try to use it.

[@reluctant_sarah2267]

Very helpful video

[@argun1928]

great for all

[@riska1537]

Thank you for your example. How we can know from coefficient value is significantly or not. Can you please answer?Thank you

[@reluctant_sarah2267]

Nice video❤

[@reluctant_sarah2267]

Nice video

[@arundhatimehendale1187]

Can we built autoencoder using orange..please reply

[@-Tharos-]

I didn't understand why did you use the concatenate widget. You can pick the inliers directly from the outliers widget, no further manipulation is required.

[@saniaparveen9428]

if we have a large dataset then is it possible to determine which explanatory variable we will consider in our regression equation and which we will not use. Can you please answer?

[@statisticsforall8738]

Hii,
It is depending on your literature and objectives. If literature says the variable is important then you can consider it. Frankly speaking, you can try regression with all the variables and exclude insignificant variables after first regression. Next, you need to compare both model results and you need to choose best model according to your study.

[@pearapon]

can you share an example input file for this tutorial ?
Thank you

[@statisticsforall8738]

Hii, the input file is secondary data. You can download it from website's. Like GEI means global entrepreneurship index, EFI means economic freedom index and EDB means easy of doing business index. Just search on google and you will find the data. My article is not published yet and for this reason I am unable to provide the dataset. But you can make the dataset easily as I said. Thanks for your inquiry and sorry for the inconvenience.

[@kiekuibear.2587]

@@statisticsforall8738 i can't find the source man, it's not as easy as you said