Merge pull request #11 from virtual-labs/dev

Update procedure.md
virtual-labs · May 14, 2024 · 9016a63 · 9016a63
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 ##### In order to perform the experiment, one needs to go through the following steps sequentially:
-###### Step 1:  In the simulation step, Select the distribution of the random variable (Discrete or Continuous).
-###### Step 2:  Set all the pmf or pdf according to the selected distribution (for example, For poison, set lambda > 0).
-###### Step 3:  For PMF, after setting an individual entry, click save. Click show data for the complete distribution table. 
-###### Step 4:  For PDF, set the interval for which the graph of the PDF needs to be visualized.
-###### Step 5:  Click the 'Draw' button to start the experiment.
-###### Step 6:  Hence, the graphical representation of the selected distribution can be viewed. 
+
+###### Step 1: Select the Distribution
+In the simulation step, choose the type of distribution of the random variable, which can be either Discrete or Continuous.
+
+###### Step 2: Set Probability Mass Function (PMF) or Probability Density Function (PDF)
+Set all the parameters according to the selected distribution. For example, for the Poisson distribution, ensure that the lambda parameter is set to a value greater than 0.
+
+###### Step 3: Set PMF or PDF Entries
+For Probability Mass Function (PMF), set the individual probabilities for each outcome and click 'save'. Click 'show data' to view the complete distribution table.
+For the Probability Density Function (PDF), set the interval for which the graph of the PDF needs to be visualized.
+
+###### Step 4: Click the 'Draw' Button
+Initiate the experiment by clicking the 'Draw' button. This will generate the graphical representation of the selected distribution based on the parameters and entries you've set.
+
+###### Step 5: View the Graphical Representation
+After clicking 'Draw,' observe the graphical representation of the selected distribution. This graph will illustrate the probabilities associated with different outcomes or events within the distribution.