Intro tutorial: Add general steps for analysing results

docs/tutorials/intro_tutorial.ipynb

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+    "It can be clearly seen that the lower the number of agents, the higher the number of consecutive rounds without a transaction. This is because the agents have fewer interactions with each other and therefore the wealth of an agent is less likely to change."
+   ],
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+   "source": [
+    "#### General steps for analyzing results\n",
+    "\n",
+    "Many other analysis are possible based on the policies, scenarios and uncertainties that you might be interested in. In general, you can follow these steps to do your own analysis:\n",
+    "\n",
+    "1. Determine which metrics you want to analyse. Add these as model and agent reporters to the datacollector of your model.\n",
+    "2. Determine the input parameters you want to vary. Add these as parameters to the batch_run function, using ranges or lists to test different values.\n",
+    "3. Determine the hyperparameters of the batch_run function. Define the number of iterations, the number of processes, the number of steps, the data collection period, etc.\n",
+    "4. Run the batch_run function and save the results.\n",
+    "5. Transform, filter and aggregate the results to get the data you want to analyze. Make sure it's in long format, so that each row represents a single value.\n",
+    "6. Choose a plot type, what to plot on the x and y axis, which columns to use for the hue. Seaborn also has an amazing [Example Gallery](https://seaborn.pydata.org/examples/index.html).\n",
+    "7. Plot the data and analyze the results."
+   ],
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+   }
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