From b9990c7ef1d728776423fb4301621c573f085eba Mon Sep 17 00:00:00 2001
From: xxl4tomxu98 <tomxu@udel.edu>
Date: Thu, 1 Jul 2021 16:40:39 -0400
Subject: [PATCH] correct equations in readme

---
 .DS_Store                                        | Bin 0 -> 6148 bytes
 ...-9604-4dd6-b177-c1154ded2ece-checkpoint.ipynb |   6 ++++++
 README.md                                        |   2 +-
 3 files changed, 7 insertions(+), 1 deletion(-)
 create mode 100644 .DS_Store
 create mode 100644 .ipynb_checkpoints/default-91a2517e-9604-4dd6-b177-c1154ded2ece-checkpoint.ipynb

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
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diff --git a/.ipynb_checkpoints/default-91a2517e-9604-4dd6-b177-c1154ded2ece-checkpoint.ipynb b/.ipynb_checkpoints/default-91a2517e-9604-4dd6-b177-c1154ded2ece-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/default-91a2517e-9604-4dd6-b177-c1154ded2ece-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index 3e5b4f8..57e49ee 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ The primary difference is those models are uni-directional, where, the predictor
 If you remember in Autoregression models, the time series is modeled as a linear combination of it’s own lags. That is, the past values of the series are used to forecast the current and future.
 
 A typical AR(p) model equation looks something like this:
-    https://www.machinelearningplus.com/wp-content/uploads/2019/07/Equation_ARp_Model-min.png
+    ![AR(p) Model Equations](https://www.machinelearningplus.com/wp-content/uploads/2019/07/Equation_ARp_Model-min.png)
 where α is the intercept, a constant and β1, β2 till βp are the coefficients of the lags of Y till order p.
 
 Order ‘p’ means, up to p-lags of Y is used and they are the predictors in the equation. The ε_{t} is the error, which is considered as white noise.