minot formatting updates

Author: drbenvincent

docs/source/notebooks/graded_intervention_time_series_single_channel_ols.ipynb

@@ -46,7 +46,7 @@
     "\n",
     "#### Introduction: Understanding the Problem\n",
     "\n",
-    "**Autocorrelation** occurs when observations in a time series are correlated with their own past values. In causal inference with time series data, this creates a fundamental challenge:\n",
+    "Autocorrelation occurs when observations in a time series are correlated with their own past values. In causal inference with time series data, this creates a fundamental challenge:\n",
     "\n",
     "**What is autocorrelation?**\n",
     "- Today's outcome is influenced by yesterday's (and last week's, and last month's...)\n",
@@ -60,10 +60,10 @@
     "\n",
     "Standard regression assumes **independent errors** — that the unexplained variation at time $t$ is unrelated to time $t-1$. When this assumption fails (as it almost always does in time series):\n",
     "\n",
-    "1. **Coefficient estimates remain unbiased** ✓ (still correct on average)\n",
-    "2. **Standard errors are WRONG** ✗ (typically too small, leading to overconfident inference)\n",
-    "3. **Hypothesis tests are invalid** ✗ (false positives, misleading p-values)\n",
-    "4. **Confidence intervals are too narrow** ✗ (underestimate true uncertainty)\n",
+    "1. ✅ **Coefficient estimates remain unbiased:** still correct on average\n",
+    "2. ❌ **Standard errors are WRONG:** typically too small, leading to overconfident inference\n",
+    "3. ❌ **Hypothesis tests are invalid:** false positives, misleading p-values\n",
+    "4. ❌ **Confidence intervals are too narrow:** underestimate true uncertainty\n",
     "\n",
     "This means you might conclude an intervention \"works\" when it actually doesn't, or claim high precision when you're actually quite uncertain!\n",
     "\n",