The answer is 2 first you add then multiple
Answer:6x+1-3x-1
negative and negative= positive
3x+2
answer: 3x+2
Step-by-step explanation:
Answer:
Second option: On a coordinate plane, rectangle A'B'C'D' prime has points
(See the graph attached)
Step-by-step explanation:
For this exercise it is importnat to know that a Dilation is defined as a transformation in which the Image (The figure obtained after the transformation) has the same shape as the Pre-Image (which is the original figure before the transformation), but they have different sizes.
In this case, you know that the vertices of the rectangle ABCD ( The Pre-Image) are the following:

Therefore, to find the vertices of the rectangle A'B'C'D' (The Image) that results of dilating the rectangle ABCD by a factor of 4 about the origin, you need to multiply the coordinates of each original vertex by 4. Then, you get:

Finally, knowing those points, you can identify that the graph that shows the result of that Dilation, is the one attached.
Answer:
D. No, because the sample size is large enough.
Step-by-step explanation:
The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
If the sample size is higher than 30, on this case the answer would be:
D. No, because the sample size is large enough.
And the reason is given by The Central Limit Theorem since states if the individual distribution is normal then the sampling distribution for the sample mean is also normal.
From the central limit theorem we know that the distribution for the sample mean
is given by:
If the sample size it's not large enough n<30, on that case the distribution would be not normal.