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What is the domain of \(h(x, y)=\sqrt{x-y} ?\)

Short Answer

Expert verified
Answer: The domain of the function h(x, y) is the set of all ordered pairs (x, y) where y is less than or equal to x, or formally, \(\{(x, y) \in \mathbb{R}^2 \mid y \leq x\}\).

Step by step solution

01

Set the expression under the square root to be greater than or equal to zero

Since we want the value under the square root to be greater than or equal to 0, we have the inequality: \(x - y \geq 0\).
02

Solve the inequality for y

Now, we want to solve the inequality for y. To do this, add y to both sides and subtract the value on the right side from both sides of the inequality. We obtain: \(y \leq x\).
03

Write the domain in set notation

The inequality we found in step 2 gives us information about the relationship between x and y. We can express the domain of the function in set notation: \(\{(x, y) \in \mathbb{R}^2 \mid y \leq x\}\).
04

Interpret the domain

The domain we found in step 3 means that the function h(x, y) is defined for any ordered pair \((x, y)\) where \(y\) is less than or equal to \(x\). In other words, the function is defined for all \((x, y)\) in the plane that are below (or on) the line \(y = x\).

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Most popular questions from this chapter

In its many guises, the least squares approximation arises in numerous areas of mathematics and statistics. Suppose you collect data for two variables (for example, height and shoe size) in the form of pairs \(\left(x_{1}, y_{1}\right),\left(x_{2}, y_{2}\right), \ldots,\left(x_{n}, y_{n}\right)\) The data may be plotted as a scatterplot in the \(x y\) -plane, as shown in the figure. The technique known as linear regression asks the question: What is the equation of the line that "best fits" the data? The least squares criterion for best fit requires that the sum of the squares of the vertical distances between the line and the data points is a minimum. Let the equation of the best-fit line be \(y=m x+b,\) where the slope \(m\) and the \(y\) -intercept \(b\) must be determined using the least squares condition. First assume that there are three data points \((1,2),(3,5),\) and \((4,6) .\) Show that the function of \(m\) and \(b\) that gives the sum of the squares of the vertical distances between the line and the three data points is $$ \begin{aligned} E(m, b)=&((m+b)-2)^{2}+((3 m+b)-5)^{2} \\ &+((4 m+b)-6)^{2} \end{aligned}. $$ Find the critical points of \(E\) and find the values of \(m\) and \(b\) that minimize \(E\). Graph the three data points and the best-fit line.

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