Product means multiply so:
5(2n)
=10n
Answer:
Step-by-step explanation:
A scalar is a constant value that is multiplied throughout a matrix.
e.g.
In number 1 the set up would look like this
3 * ![\left[\begin{array}{ccc}3&-1&5\\2&1&-4\\-6&3&2\end{array}\right]](https://tex.z-dn.net/?f=%5Cleft%5B%5Cbegin%7Barray%7D%7Bccc%7D3%26-1%265%5C%5C2%261%26-4%5C%5C-6%263%262%5Cend%7Barray%7D%5Cright%5D)
To solve this, you must distribute the 3 to each value within the matrix
The solution to #1 would be
M = ![\left[\begin{array}{ccc}9&-3&15\\6&3&-12\\-18&9&6\end{array}\right]](https://tex.z-dn.net/?f=%5Cleft%5B%5Cbegin%7Barray%7D%7Bccc%7D9%26-3%2615%5C%5C6%263%26-12%5C%5C-18%269%266%5Cend%7Barray%7D%5Cright%5D)
To write an equation you need a slop and y intercept. To find the slop use the formula y2-y1 /x2-x1. and for the y intercept use the formula y=mx+b
A statement which best describes the strength of the correlation, and the causation between the variables is that: D. it is a strong positive correlation, and it is likely causal.
<h3>What is a positive correlation?</h3>
A positive correlation can be defined as a terminology that is used to described a scenario (situation) in which two variables move in the same direction and are in tandem.
This ultimately implies that, a positive correlation exist when two variables have a linear relationship or are in direct proportion. Hence, when one variable increases, the other increases as well, and vice-versa.
By critically observing the scatter plot (see attachment) which models the data in the given table, we can infer and logically deduce that the value on the y-axis (circumference) increases as the value on the x-axis (radius) increases, so this is a strong positive correlation.
Also, we know that there exist a direct relationship between the circumference of a circle and its radius, so this relationship is most likely causal.
In conclusion, a statement which best describes the strength of the correlation, and the causation between the variables is that it's a strong positive correlation, and it is likely causal.
Read more on positive correlation here: brainly.com/question/10644261
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