docs: Fixes typos and header level error in introductory tutorial (#2087

)

This fixes
- some minor grammatical errors,
- an incorrect header indentation level,

docs/tutorials/intro_tutorial.ipynb

@@ -136,7 +136,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Import Dependencies\n",
+    "### Import Dependencies\n",
     "This includes importing of dependencies needed for the tutorial."
    ]
   },
@@ -243,7 +243,7 @@
     "\n",
     "**Model-specific information:** A new class is named `RandomActivationByAgent` is created which extends `mesa.time.RandomActivation` creating a subclass of  the `RandomActivation` class from Mesa. This class activates all the agents once per step, in random order. Every agent is expected to have a ``step`` method. The step method is the action the agent takes when it is activated by the model schedule. We add an agent to the schedule using the `add` method; when we call the schedule's `step` method, the model shuffles the order of the agents, then activates and executes each agent's ```step``` method. The scheduler is then added to the model.\n",
     "\n",
-    "**Code implementation:** The technical details about the timer object can be found in the [mesa repo](https://github.com/projectmesa/mesa/blob/main/mesa/time.py). Mesa offers a few different built-in scheduler classes, with a common interface. That makes it easy to change the activation regime a given model uses, and see whether it changes the model behavior. The details pertaining to the scheduler interface can be located the same [mesa repo](https://github.com/projectmesa/mesa/blob/main/mesa/time.py).\n",
+    "**Code implementation:** The technical details about the timer object can be found in the [mesa repo](https://github.com/projectmesa/mesa/blob/main/mesa/time.py). Mesa offers a few different built-in scheduler classes, with a common interface. That makes it easy to change the activation regime a given model uses, and see whether it changes the model behavior. The details pertaining to the scheduler interface can be located in the same [mesa repo](https://github.com/projectmesa/mesa/blob/main/mesa/time.py).\n",
     "\n",
     "With that in mind, the `MoneyAgent` code is modified below to visually show when a new agent is created. The MoneyModel code is modified by adding the RandomActivation method to the model. with the scheduler added looks like this:"
    ]
@@ -543,7 +543,7 @@
     "\n",
     "Mesa has two main types of grids: `SingleGrid` and `MultiGrid`*. `SingleGrid` enforces at most one agent per cell; `MultiGrid` allows multiple agents to be in the same cell. Since we want agents to be able to share a cell, we use `MultiGrid`.\n",
     "\n",
-    "*However there are more types of space to include `HexGrid`, `NetworkGrid`, and the previously mentioned `ContinuousSpace`. Similar to `mesa.time` context is retained with `mesa.space.[enter class]`. You can see the different classes as [mesa.space](https://github.com/projectmesa/mesa/blob/main/mesa/space.py) "
+    "*However there are more types of space to include `HexGrid`, `NetworkGrid`, and the previously mentioned `ContinuousSpace`. Similar to `mesa.time` context is retained with `mesa.space.[enter class]`. You can inspect the different classes at [mesa.space](https://github.com/projectmesa/mesa/blob/main/mesa/space.py)."
    ]
   },
   {
@@ -829,9 +829,9 @@
    "source": [
     "At every step of the model, the datacollector will collect and store the model-level current Gini coefficient, as well as each agent's wealth, associating each with the current step.\n",
     "\n",
-    "We run the model just as we did above. Now is when an interactive session, especially via a Notebook, comes in handy: the DataCollector can export the data its collected as a pandas\\* DataFrame, for easy interactive analysis. \n",
+    "We run the model just as we did above. Now is when an interactive session, especially via a Notebook, comes in handy: the DataCollector can export the data its collected as a pandas* DataFrame, for easy interactive analysis. \n",
     "\n",
-    "\\*If you are new to Python, please be aware that pandas is already installed as a dependency of Mesa and that [pandas](https://pandas.pydata.org/docs/) is a \"fast, powerful, flexible and easy to use open source data analysis and manipulation tool\". pandas is great resource to help analyze the data collected in your models "
+    "*If you are new to Python, please be aware that pandas is already installed as a dependency of Mesa and that [pandas](https://pandas.pydata.org/docs/) is a \"fast, powerful, flexible and easy to use open source data analysis and manipulation tool\". pandas is great resource to help analyze the data collected in your models."
    ]
   },
   {
@@ -1288,7 +1288,7 @@
    "cell_type": "markdown",
    "source": [
     "### Analyzing model reporters: Comparing 5 scenarios\n",
-    "Other insight might be gathered when we compare the Gini coefficient of different scenarios. For example, we can compare the Gini coefficient of a population with 25 agents to the Gini coefficient of a population with 400 agents. While doing this, we increase the number of iterations to 25 to get a better estimate of the Gini coefficient for each population size and get usable error estimations."
+    "Other insights might be gathered when we compare the Gini coefficient of different scenarios. For example, we can compare the Gini coefficient of a population with 25 agents to the Gini coefficient of a population with 400 agents. While doing this, we increase the number of iterations to 25 to get a better estimate of the Gini coefficient for each population size and get usable error estimations."
    ],
    "metadata": {
     "collapsed": false