The table showing the conversion of angle measure in degrees to angles in gradients is attached below.
In order to find the slope we divide the difference of two y-coordinates (or dependent variable which in this case is gradient measure) by the difference of two respective x-coordinates (or independent variable which in this case is degree measure).
For finding the slope we will use the first and the last point given in the table. So, the slope m will be given by:
So rounding of to nearest hundredth, the slope of line representing the conversion of degrees to gradients is 1.11
Let
x----------------------- > number of <span>cans of fruit-----------------> 24 per minute
</span>y----------------------- > number of cans of vegetables-------> 64 per minute
<span>z----------------------- > number of cans of food per minute
t----------------------- > time in minutes
z=(x+y)*t
z=(24+64)*t--------------- > 88t
z=88t---------------------------------- > this is the equation required
we know that z=384
then
</span>384=88t------------- > t=4.36 minutes
Answer:
SAMPLE RESPONSE( The diagram should have five columns, one for each 25% and one for the total, because 25% is 1
4
of 100%. It should have 2 rows, 1 for percents and 1 for dollar amounts.
Step-by-step explanation:
Edg2020
Answer: Both students are correct.
Explanation:
In rotation a shape is rotated about a fixed point and it does not affect its measurements. it only changes its position.
Now, When Marcus rotated the
about point S then this process did not affect its shape and size this is why he got the
which is congruent to the
.
In reflection, when a shape reflected across a line then its all points get reflected across this line. But it also does not affect the measurement of the shape.
So, when Sam mapped
by the reflection of
then the measurement of the
did not change. And, he also got the congruent triangle.
Thus, we can say that, Both Marcus and Sam are right.
Answer:
Approximately normal for large sample sizes
Step-by-step explanation:
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean
and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
In this question:
The distribution is unknown, so the sampling distribution will only be approximately normal when n is at least 30.
So the correct answer should be:
Approximately normal for large sample sizes