diff --git a/README.md b/README.md
index 4ec9cb9..95f6efc 100644
--- a/README.md
+++ b/README.md
@@ -33,8 +33,7 @@ Let’s suppose, you have two variables (Time series) Y1 and Y2, and you need to
 To calculate Y1(t), VAR will use the past values of both Y1 as well as Y2. Likewise, to compute Y2(t), the past values of both Y1 and Y2 be used.
 
 For example, the system of equations for a VAR(1) model with two time series (variables `Y1` and `Y2`) is as follows:
-
-    ![VAR(1) Model Equations](https://www.machinelearningplus.com/wp-content/uploads/2019/07/Equation_VAR1_Model-min.png)
+    ![AR(p) Model Equations](https://www.machinelearningplus.com/wp-content/uploads/2019/07/Equation_VAR1_Model-min.png)
 
 Where, Y{1,t-1} and Y{2,t-1} are the first lag of time series Y1 and Y2 respectively.
 
@@ -42,12 +41,10 @@ The above equation is referred to as a VAR(1) model, because, each equation is o
 
 Since the Y terms in the equations are interrelated, the Y’s are considered as endogenous variables, rather than as exogenous predictors.
 
-Likewise, the second order VAR(2) model for two variables would include up to two lags for each variable (Y1 and Y2).
-
+Likewise, the second order VAR(2) model for two variables would include up to two lags for each variable (Y1 and Y2):
     ![VAR(2) Model Equations](https://www.machinelearningplus.com/wp-content/uploads/2019/07/Equation_VAR2_Model-min.png)
 
 Can you imagine what a second order VAR(2) model with three variables (Y1, Y2 and Y3) would look like?
-
     ![VAR(2) Model Three Variables](https://www.machinelearningplus.com/wp-content/uploads/2019/07/Equation_VAR2_Model_with_three_Ys-min.png)
 
 As you increase the number of time series (variables) in the model the system of equations become larger.