improves text and title

new_order_prediction_tutorial.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Introduction\n",
+    "# Order prediction tutorial\n",
     "\n",
     "This tutorial will guide your through predicting orders in ERP5 using an ARIMA model. The orders will be exported by ERP5 in CSV format (comma-sepparated values), and accessed and processed localy in a Jupyter Notebook.\n",
     "\n",
@@ -337,7 +337,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Looks like partial auto correlation goes to 0 after lag 3. So **ARMA(3,0)** looks like an adequated model for our data. Auto correlation graph shows information about another model that might be good but require a different analysis, which isn't the real focus on this tutorial.\n",
+    "Looks like partial auto correlation goes to 0 after lag 3. So **ARMA(3,0)** looks like an adequated model for our data. Auto correlation graph shows information about another model that might be good but require a different analysis, which isn't the real focus of this tutorial.\n",
     "\n",
     "An important thing to note is that there are mathmetical methods to have suggestion of model orders for the data. You will get to know them in the next section."
    ]