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Give the y-intercept and slope for the line. $$y=2 x+3$$

Short Answer

Expert verified
Answer: The slope (m) is 2, and the y-intercept (b) is 3.

Step by step solution

01

Identify the Slope-Intercept Form

The given equation is in the form of $$y = mx + b$$, which is the slope-intercept form of a linear equation. In this form, m represents the slope and b represents the y-intercept.
02

Identify the Slope (m)

In the given equation, the slope (m) is the coefficient of x. In this case, the equation is $$y = 2x + 3$$. Thus, the slope (m) is 2.
03

Identify the Y-Intercept (b)

In the given equation, the y-intercept (b) is the constant term. In this case, the equation is $$y = 2x + 3$$. Thus, the y-intercept (b) is 3.
04

Present the Slope and Y-Intercept

After analyzing the given equation, we find that the slope (m) is 2 and the y-intercept (b) is 3.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Linear Equations
Linear equations form the foundation for understanding how variables relate to each other in a straight line. These equations can always be written in the form of \( y = mx + b \), which is known as the slope-intercept form. The simplicity of this form lies in its direct representation of the line's slope \( m \) and y-intercept \( b \), providing a quick way to graph the line and understand its behavior.

Every linear equation represents a straight line when graphed on a coordinate plane. The variable \( y \) represents the dependent variable, while \( x \) is the independent variable. The slope \( m \) indicates how steep the line is, and the y-intercept \( b \) signifies the point where the line crosses the y-axis. Understanding these elements is crucial for solving problems involving linear relationships, whether in algebra or in real-world applications such as calculating rates.
Y-Intercept
The y-intercept is a key characteristic of a linear equation representing where the line crosses the y-axis on a graph. Put simply, it's the value of \( y \) when \( x \) is zero. In the slope-intercept form \( y = mx + b \), the \( b \) directly provides this value.

In the context of a real-world scenario, the y-intercept can represent a starting value or initial condition. For instance, if \( y \) represents the total cost and \( x \) represents the quantity of items purchased, then the y-intercept \( b \) might represent a base fee charged no matter the quantity. Understanding the concept of the y-intercept offers vital insights about where a linear function starts and how it will proceed as the value of \( x \) changes.
Slope of a Line
The slope of a line is a measure of its steepness, often represented by the letter \( m \) in the slope-intercept form of a linear equation, \( y = mx + b \). The slope is a ratio that describes how much the \( y \) value (often referred to as the 'rise') changes for every unit change in the \( x \) value (the 'run').

A positive slope means the line rises as it moves from left to right, a negative slope means it falls, and a slope of zero implies a horizontal line. A vertical line, on the other hand, has an undefined slope. Calculating the slope is straightforward when you have two points on a line, \( (x_1, y_1) \) and \( (x_2, y_2) \), by using the formula \( m = \frac{y_2 - y_1}{x_2 - x_1} \). Knowing how to determine the slope is essential for analyzing trends and making predictions, such as in studying rates of change in economics, sciences, and various fields where data visualization is necessary.

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Most popular questions from this chapter

Calculate the sums of squares and cross-products, \(S_{x x}\) and \(S_{x x}\) $$(3,6) \quad(5,8) \quad(2,6) \quad(1,4) \quad(4,7) \quad(4,6)$$

Use the data given in Exercises 6-7 (Exercises 17-18, Section 12.1). Construct the ANOVA table for a simple linear regression analysis, showing the sources, degrees of freedom, sums of squares, and mean sauares. $$\begin{aligned}&\text { Six points have these coordinates: }\\\&\begin{array}{l|llllll}x & 1 & 2 & 3 & 4 & 5 & 6 \\\\\hline y & 9.7 & 6.5 & 6.4 & 4.1 & 2.1 & 1.0\end{array}\end{aligned}$$ a. Find the least-squares line for the data. b. Plot the six points and graph the line. Does the line appear to provide a good fit to the data points? c. Use the least-squares line to predict the value of \(y\) when \(x=3.5\) d. Fill in the missing entries in the MS Exce/ analysis of variance table.

The number of miles of U.S. urban roadways (millions of miles) for the years \(2000-2015\) is reported below. \({ }^{6}\) The years are simplified as years 0 through \(15 .\) $$ \begin{array}{l|cccccccc} \text { Miles of Road- } & & & & & & & & \\ \text { ways (millions) } & 0.85 & 0.88 & 0.89 & 0.94 & 0.98 & 1.01 & 1.03 & 1.04 \\ \hline \text { Year }-2000 & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \end{array} $$ $$ \begin{array}{l|cccccccc} \begin{array}{l} \text { Miles of Road- } \\ \text { ways (millions) } \end{array} & 1.07 & 1.08 & 1.09 & 1.10 & 1.11 & 1.18 & 1.20 & 1.21 \\ \hline \text { Year }-2000 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \end{array} $$ a. Draw a scatterplot of the number of miles of roadways in the U.S. over time. Describe the pattern that you see. b. Find the least-squares line describing these data. Do the data indicate that there is a linear relationship between the number of miles of roadways and the year? Test using a \(t\) statistic with \(\alpha=.05\). c. Construct the ANOVA table and use the \(F\) statistic to answer the question in part b. Verify that the square of the \(t\) statistic in part \(\mathrm{b}\) is equal to \(F\). d. Calculate \(r^{2}\). What does this value tell you about the effectiveness of the linear regression analysis?

Is there any relationship between these two variables? To find out, we randomly selected 12 people from a data set constructed by Allen Shoemaker (Journal of Statistics Education) and recorded their body temperature and heart rate. \({ }^{19}\) $$\begin{array}{lllllll}\hline \text { Person } & 1 & 2 & 3 & 4 & 5 & 6 \\\\\hline \text { Temperature } & 96.3 & 97.4 & 98.9 & 99.0 & 99.0 & 96.8 \\ \text { (degrees) } & & & & & & \\\\\text { Heart Rate } & 70 & 68 & 80 & 75 & 79 & 75 \\\\\text { (beats per } & & & & & & \\\\\text { minute) } & & & & & & \\\ \hline\end{array}$$ $$\begin{array}{lllllll}\hline \text { Person } & 7 & 8 & 9 & 10 & 11 & 12 \\\\\hline \text { Temperature } & 98.4 & 98.4 & 98.8 & 98.8 & 99.2 & 99.3 \\\\\multicolumn{2}{l} {\text { (degrees) }} & & & & & & \\\\\text { Heart Rate } & 74 & 84 & 73 & 84 & 66 & 68 \\\\\text { (beats per } & & & & & & \\\\\text { minute) } & & & & & & & \\\& & & & \\\\\hline\end{array}$$ a. Find the correlation coefficient \(r\), relating body temperature to heart rate. b. Is there sufficient evidence to indicate that there is a correlation between these two variables? Test at the 5\% level of significance.

Give the equation and graph for a line with y-intercept and slope given in Exercises. $$y \text { -intercept }=-2.5 ; \text { slope }=5$$

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