From 60a450d3743bd0cf102f8d03869deae5b55aaf18 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 2 Jun 2021 18:25:49 -0400
Subject: [PATCH] Regression 3 quiz

---
 Regression/3-Linear/README.md            |  2 +-
 quiz-app/src/assets/translations/en.json | 60 ++++++++++++++----------
 2 files changed, 35 insertions(+), 27 deletions(-)

diff --git a/Regression/3-Linear/README.md b/Regression/3-Linear/README.md
index 95326e82b..2dd98428c 100644
--- a/Regression/3-Linear/README.md
+++ b/Regression/3-Linear/README.md
@@ -32,7 +32,7 @@ As you learned in Lesson 1, the goal of a linear regression exercise is to be ab
 >
 > You can observe the method of calculation for these values on the [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) web site.
 >
-> A common method of regression is **Least-Squares Regression** which means that all the datapoints surounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`. We do so since we want to model a line that has the least cumulative distance from all of our data points. We also square the terms before adding them since we are concerned with its magnitude rather than its direction.
+> A common method of regression is **Least-Squares Regression** which means that all the datapoints surrounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`. We do so since we want to model a line that has the least cumulative distance from all of our data points. We also square the terms before adding them since we are concerned with its magnitude rather than its direction.
 >
 > One more term to understand is the **Correlation Coefficient** between given X and Y variables. For a scatterplot, you can quickly visualize this coefficient. A plot with datapoints scattered in a neat line have high correlation, but a plot with datapoints scattered everywhere between X and Y have a low correlation.
 >
diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json
index 65342254d..2d4097906 100644
--- a/quiz-app/src/assets/translations/en.json
+++ b/quiz-app/src/assets/translations/en.json
@@ -555,48 +555,52 @@
 				"title": "Linear and Polynomial Regression: Pre-Lecture Quiz",
 				"quiz": [
 					{
-						"questionText": "q1",
+						"questionText": "Matplotlib is a ",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "drawing library",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
+								"answerText": "data visualization library",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "c",
+								"answerText": "lending library",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q2",
+						"questionText": "Linear Regression uses the following to plot relationships between variables",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "a straight line",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
+								"answerText": "a circle",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "a curve",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q3",
+						"questionText": "A good Linear Regression model has a ___ Correlation Coefficient",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "low",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
+								"answerText": "high",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "c",
+								"answerText": "flat",
 								"isCorrect": "false"
 							}
 						]
@@ -608,49 +612,53 @@
 				"title": "Linear and Polynomial Regression: Post-Lecture Quiz",
 				"quiz": [
 					{
-						"questionText": "q1",
+						"questionText": "If your data is nonlinear, try a ___ type of Regression",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "linear",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
-								"isCorrect": "true"
+								"answerText": "spherical",
+								"isCorrect": "false"
 							},
 							{
-								"answerText": "c",
-								"isCorrect": "false"
+								"answerText": "polynomial",
+								"isCorrect": "true"
 							}
 						]
 					},
 					{
-						"questionText": "q2",
+						"questionText": "These are all types of Regression methods",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "Falsestep, Ridge, Lasso and Elasticnet",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Stepwise, Ridge, Lasso and Elasticnet",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
+								"answerText": "Stepwise, Ridge, Lariat and Elasticnet",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q3",
+						"questionText": "Least-Squares Regression means that all the datapoints surrounding the regression line are:",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "squared and then subtracted",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
-								"isCorrect": "true"
+								"answerText": "multiplied",
+								"isCorrect": "false"
 							},
 							{
-								"answerText": "c",
-								"isCorrect": "false"
+								"answerText": "squared and then added up",
+								"isCorrect": "true"
 							}
 						]
 					}