Answer:
4
9
2
−
5
6
−
5
6
+
6
4
Step-by-step explanation:
It seems like the details of what p and q <em>are </em>in this context aren't all that important; it's the logical structure of the statement "p⇒q" we need to look at. We read that logical statement as "p implies q," where p is our <em>hypothesis</em> and q is our <em>conclusion</em>. When we take the converse of a logical statement, we reverse the hypothesis and the conclusion. In this case, <em>p </em>wouldn't imply <em>q</em>, but <em>q </em>would imply <em>p</em> in the converse of p⇒q. We'd write this statement as:
q⇒p
That would be 7 because it is placed in front of the y
9514 1404 393
Answer:
- no square roots: -1000, -8
- one square root: 0
- two square roots: 8, 64, 1000
- no cube roots: <none>
- one cube root: -1000, -8, 0, 8, 64, 1000
- two cube roots: <none>
Step-by-step explanation:
The attached graph shows the square root relation (red) and the cube root function (blue). The function values are shown for x=0 and x=±8.
You can see that there are 2 square roots for positive numbers, one square root for 0, and 0 square roots for negative numbers. There is exactly 1 cube root for any number.
- no square roots: -1000, -8
- one square root: 0
- two square roots: 8, 64, 1000
- no cube roots: <none>
- one cube root: -1000, -8, 0, 8, 64, 1000
- two cube roots: <none>
_____
<em>Additional comment</em>
We call the square root curve a "relation" because it is <em>not a function</em>. A relation that is a function will have only one y-value for each x-value. For positive x-values, there are two square roots.
Answer and Step-by-step explanation:
This regression can generate an omitted variable bias, because the researcher did not determine any parameters or real data to measure it. In this case, the researcher determined the crime rate as a dependent variable, but did not establish the facts that influence it and that promote an observable result. In this way, he omitted the independent variable and his research will be inaccurate and incorrect. To control this bias, it is necessary for the researcher to provide a dependent variable such as the level of education in the region, the income distribution in the region, the unemployment rate, the fraction of young men, among others.
