Answer:
2.5
Step-by-step explanation:
250 cm equals 2.5 m
Answer:
25 is what I got
Answer:
2≤x≤4
Step-by-step explanation:
2 is less than/equal to x; x is less than/equal to 4
The answer is the first option: I, II and III.
The explanation is shown below:
1. By definition, there is a joint variation when a variable depends on two or more different variables. Therefore, you can express it as following:
Where and are the variables and is the constant of proportionality.
As you can see, is directly proportional to and .
2. Keeping the information above, you have:
I) ( varies jointly with , and .
II) (If is the constant of proportionality, varies jointly with and ).
III) ( varies jointly with and .
Answer : I, II and III
To find volume formula that shows joint variation we analyze each option
Joint variation always depends on atleast two dependent variables
for example y = kxy
The variable y depends on x and y and k is constant of proportionality
V= lwh
It means volume depends on length , width and height. 1 is the constant of proportionality .
V depends on radius r and height h. Here 1/3 pi is the constant of proportionality
V= BH
V depend on base b and height h . 1 is the constant of proportionality .
So answer is I, II , III
Answer:
0.7
Step-by-step explanation:
Answer:
0.7
Step-by-step explanation:
7/10 can also be written as 7÷10
Answer:
d. Decrease
Step-by-step explanation:
A Type II error is when we fail to reject a false null hypothesis. Higher values of α make it easier to reject the null hypothesis, so choosing higher values for α can reduce the probability of a Type II error.
The consequence here is that if the null hypothesis is true, increasing α makes it more likely that we commit a Type I error (rejecting a true null hypothesis).
So using lower values of α can increase the probability of a Type II error.
Raising the level of significance in a hypothesis test from .01 to .05 would decrease the probability of making a Type II error. This is because as we become more accepting of risk in making a Type I error, we simultaneously reduce the risk of making a Type II error.
The level of significance in a hypothesis test is the probability that we are willing to accept for incorrectly rejecting the null hypothesis or making a Type I error. If the level of significance is raised, there is a higher chance we incorrectly reject the null hypothesis, increasing the chances of a Type I error. However, this also has an effect on the probability of committing a Type II error, which is to incorrectly accept the null hypothesis.
Specifically, when the level of significance of a hypothesis test is raised from .01 to .05, the probability of a Type II error (option b) will decrease. The reason for this is that increasing the level of significance or alpha means we are more likely to reject the null hypothesis. As we are more accepting of risk in terms of making a Type I error, we are less likely to make a Type II error, as the two error types often move in opposite directions. Thus, the answer to your question is d. The probability of a Type II error will decrease if the significance level is raised from .01 to .05.
#SPJ3
CHECK THE ATTACHMENT FOR COMPLETE QUESTION
Answer:
We can show that ΔABC is congruent to ΔA'B'C' by a translation of 2 unit(s) Left and a Reflection across the x axis.
Step-by-step explanation:
We were given triangles ABC and A'B'C' of which were told are congruents,
Now we can provide the coordinates of A and A' from the given triangles ΔABC and ΔA'B'C' ,if we choose a point of A from ΔABC and A' from ΔA'B'C' we have these coordinates;
A as (8,8) and A' (6,-8) from the two triangles.
If we shift A to A' , we have (8_6) = 2 unit for that of x- axis
If we try the shift on the y-coordinates we will see that there is no translation.
Hence, the only translation that take place is of 2 units left.
It can also be deducted that there is a reflection
by x-axis to form A'B'C' by the ΔABC.
BEST OF LUCK
Answer:
(Choice A) A graph of an increasing linear function in quadrant 1 with a positive y-intercept.
Step-by-step explanation:
The weight of the sumo wrestler starts at a positive value of 79.5 kilograms, and we are given that the sumo wrestler gains a linear amount of weight per month at 5.5 kilograms per month.
If we were to graph this relationship, the sumo wrestler's weight would be represented on the y-axis, and the amount of time on the x-axis.
So the initial weight would occur at (0, 79.5) which is the positive y-intercept.
And since his weight is increasing at 5.5 kilograms per month, the slope of the linear function is positive.
Hence, the graph of the linear increasing function in quadrant 1 with a positive y-intercept.
Cheers.