Sustain2Green is a unique platform to bring forth articles, thoughts, surveys, news, discussions about Politics, Economics, Environment, Climate change, carbon, resources, waste, sustainability, social issues, clean technologies, energy, CSR etc. Our aim is to bring people on this platform and make a sustainable impact . Indexed by The Huffington Post.Ranked in the Top 50. Contact us at "sustain2green(at)sustain2green(dot)com". It does not claim copyrights or originality of posts.
Primarily Line, Table, Column, Bar, Pie, Scatter Chart, Histogram etc. are used.
Categorical Data: The only measure which could be used on them is counts for each Categories. Accordingly, we can use Table, Bar(Column) or Pie Chart for them.
Continuous Data: The most widely used techniques are Histogram (also called Frequency Distribution) and Line Charts (mostly used with data stamped with Time).
Please load Sample data set Country_Car_Sales on Training Page and use Descriptive Statistics option to learn different techniques.
Example: Read variables AnnualCarSales, GDP, SizeofEconomy etc. to generate different charts. Depending on whether the selected variable is Continuous or Categorical, appropriate charts are generated.
Combination: We often encounter a mix of Categorical and Continuous data, which in Analytics parlance are also called Dimension andMeasure respectively.
When we combine two Dimensions we get what is known as a Contingency Table. We can combine two continuous variables by a Scatter plot. We can combine Categorical and Continuous data by aggregating Continuous values for Categories by means of Bar or Column Charts.
Line, Bar etc. can be used to see distribution of a Measure for a particular Dimension. The measure can be either a sum or mean for a Category.
Scatter/Bubble Plot: Scatter Plot is used to find relationship (correlation) between two Continuous(numeric) variables. It can be an important insight for detailed Analysis like Variable Screening or Predictive Modelling. Bubble Plots are advanced Scatter Plots which use an additional Size variable. It can accomodate an ID variable too, which has to be a Categorical(Dimension) Variable.