diff --git a/TimeSeries/2-ARIMA/README.md b/TimeSeries/2-ARIMA/README.md
index 9bce07744..53d7fb666 100644
--- a/TimeSeries/2-ARIMA/README.md
+++ b/TimeSeries/2-ARIMA/README.md
@@ -155,8 +155,8 @@ It's time to implement ARIMA! You'll now use the `statsmodels` library that you
 Now you need to follow several steps
 
 1. Define the model by calling `SARIMAX()` and passing in the model parameters: p, d, and q parameters, and P, D, and Q parameters. 
-1. The model is prepared on the training data by calling the fit() function. 
-2. Predictions can be made by calling the `forecast()` function and specifying the number of steps (the `horizon`) to forecast
+1. The model is prepared on the training data by calling the fit() function.
+1. Predictions can be made by calling the `forecast()` function and specifying the number of steps (the `horizon`) to forecast
 
 > 🎓 What are all these parameters for? In an ARIMA model there are 3 parameters that are used to help model the major aspects of a time series: seasonality, trend, and noise. These parameters are:
 
diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json
index b323f0919..c00d3dee6 100644
--- a/quiz-app/src/assets/translations/en.json
+++ b/quiz-app/src/assets/translations/en.json
@@ -2193,49 +2193,53 @@
 				"title": "Time Series ARIMA: Pre-Lecture Quiz",
 				"quiz": [
 					{
-						"questionText": "q1",
+						"questionText": "ARIMA stands for",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "AutoRegressive Integral Moving Average",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
-								"isCorrect": "true"
+								"answerText": "AutoRegressive Integrated Moving Action",
+								"isCorrect": "false"
 							},
 							{
-								"answerText": "c",
-								"isCorrect": "false"
+								"answerText": "AutoRegressive Integrated Moving Average",
+								"isCorrect": "true"
 							}
 						]
 					},
 					{
-						"questionText": "q2",
+						"questionText": "Stationarity refers to",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "data whose attributes does not change when shifted in time",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "data whose distribution does not change when shifted in time",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
+								"answerText": "data whose distribution changes when shifted in time",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q3",
+						"questionText": "Differencing",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "stabilizes trend and seasonality",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
-								"isCorrect": "true"
+								"answerText": "exacerbates trend and seasonality",
+								"isCorrect": "false"
 							},
 							{
-								"answerText": "c",
-								"isCorrect": "false"
+								"answerText": "eliminates trend and seasonality",
+								"isCorrect": "true"
 							}
 						]
 					}
@@ -2246,48 +2250,52 @@
 				"title": "Time Series ARIMA: Post-Lecture Quiz",
 				"quiz": [
 					{
-						"questionText": "q1",
+						"questionText": "ARIMA is used to make a model fit the special form of time series data",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "as flat as possible",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
+								"answerText": "as closely as possible",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "c",
+								"answerText": "via scatterplots",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q2",
+						"questionText": "Use SARIMAX to",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "manage seasonal ARIMA models",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
+								"answerText": "manage special ARIMA models",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "manage statistical ARIMA models",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q3",
+						"questionText": "'Walk-Forward' validation involves",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "re-evaluating a model progressively as it is validated",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
+								"answerText": "re-training a model progressively as it is validated",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "c",
+								"answerText": "re-configuring a model progressively as it is validated",
 								"isCorrect": "false"
 							}
 						]