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