diff --git a/06-Multivariate-Kalman-Filters.ipynb b/06-Multivariate-Kalman-Filters.ipynb
index fbdb92d..a8525e2 100644
--- a/06-Multivariate-Kalman-Filters.ipynb
+++ b/06-Multivariate-Kalman-Filters.ipynb
@@ -1057,7 +1057,7 @@
     "\n",
     "Both the measurement $\\mathbf{z}$ and state $\\mathbf{x}$ are vectors so we need to use a matrix to perform the conversion. The Kalman filter equation that performs this step is:\n",
     "\n",
-    "$$\\textbf{y} = \\mathbf{z} - \\mathbf{H \\bar{x}}$$\n",
+    "$$\\mathbf{y} = \\mathbf{z} - \\mathbf{H \\bar{x}}$$\n",
     "\n",
     "where $\\textbf{y}$ is the residual, $\\mathbf{x^-}$ is the prior, $\\textbf{z}$ is the measurement, and $\\textbf{H}$ is the measurement function. So we take the prior, convert it to a measurement, and subtract it from the measurement our sensor gave us. This gives us the difference between our prediction and measurement in measurement space!\n",
     "\n",
@@ -1692,7 +1692,6 @@
     "\n",
     "I want you to compare the equation for the system uncertainty and the covariance\n",
     "\n",
-    "\n",
     "$$\\begin{aligned}\n",
     "\\mathbf{S} &= \\mathbf{H\\bar{P}H}^\\mathsf{T} + \\mathbf{R}\\\\\n",
     "\\mathbf{\\bar{P}} &= \\mathbf{FPF}^\\mathsf{T} + \\mathbf{Q}\n",
@@ -2893,7 +2892,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.4.1"
   }
  },
  "nbformat": 4,