Fitting a straight line to a set of data yields the following prediction line. Complete (a) through (c) below. Ý; =2+4X; a. Interpret the meaning of the Y-intercept, bo. Choose the correct answer below. O A. The Y-intercept, bo = 2, implies that the average value of Y is 2. O B. The Y-intercept, bo = 2, implies that for each increase of 1 unit in X, the value of Y is expected to increase by 2 units. O C. The Y-intercept, bo = 2, implies that when the value of X is 0, the mean value of Y is 2. O D. The Y-intercept, bo = 4, implies that when the value of X is 0, the mean value of Y is 4.

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**Title: Interpreting the Y-intercept in Linear Regression**

**Introduction:**

When fitting a straight line to a set of data, the resulting prediction model can help us understand the relationship between two variables. In this context, we examine the linear equation:

\[
\hat{Y}_i = 2 + 4X_i
\]

**Objective:**

Interpret the meaning of the Y-intercept (\(b_0\)) in this linear equation.

**Question:**

Choose the correct interpretation of the Y-intercept, \(b_0\), from the options below.

- **A.** The Y-intercept, \(b_0 = 2\), implies that the average value of Y is 2.

- **B.** The Y-intercept, \(b_0 = 2\), implies that for each increase of 1 unit in X, the value of Y is expected to increase by 2 units.

- **C.** The Y-intercept, \(b_0 = 2\), implies that when the value of X is 0, the mean value of Y is 2.

- **D.** The Y-intercept, \(b_0 = 4\), implies that when the value of X is 0, the mean value of Y is 4.

**Explanation:**

In a linear equation of the form \(\hat{Y} = b_0 + b_1X\), the Y-intercept \(b_0\) is the value of \(\hat{Y}\) when \(X = 0\). It represents the predicted value of the dependent variable (Y) when the independent variable (X) is zero. 

**Answer:**

The correct interpretation is **C.** The Y-intercept, \(b_0 = 2\), implies that when the value of X is 0, the mean value of Y is 2.

Understanding the Y-intercept is crucial for data analysis as it provides insight into the starting point of the relationship represented by the linear model.
Transcribed Image Text:**Title: Interpreting the Y-intercept in Linear Regression** **Introduction:** When fitting a straight line to a set of data, the resulting prediction model can help us understand the relationship between two variables. In this context, we examine the linear equation: \[ \hat{Y}_i = 2 + 4X_i \] **Objective:** Interpret the meaning of the Y-intercept (\(b_0\)) in this linear equation. **Question:** Choose the correct interpretation of the Y-intercept, \(b_0\), from the options below. - **A.** The Y-intercept, \(b_0 = 2\), implies that the average value of Y is 2. - **B.** The Y-intercept, \(b_0 = 2\), implies that for each increase of 1 unit in X, the value of Y is expected to increase by 2 units. - **C.** The Y-intercept, \(b_0 = 2\), implies that when the value of X is 0, the mean value of Y is 2. - **D.** The Y-intercept, \(b_0 = 4\), implies that when the value of X is 0, the mean value of Y is 4. **Explanation:** In a linear equation of the form \(\hat{Y} = b_0 + b_1X\), the Y-intercept \(b_0\) is the value of \(\hat{Y}\) when \(X = 0\). It represents the predicted value of the dependent variable (Y) when the independent variable (X) is zero. **Answer:** The correct interpretation is **C.** The Y-intercept, \(b_0 = 2\), implies that when the value of X is 0, the mean value of Y is 2. Understanding the Y-intercept is crucial for data analysis as it provides insight into the starting point of the relationship represented by the linear model.
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