All categories

3 years ago

A ball is thrown from an initial height of

1 answer:

8 0

IF your equation is this: h(t)= -16t^2 + 27t + 7, then you set it equal to 17 and solve for t. What this does is tells you the times (t) when the ball is exactly 17 feet off the ground. Because this is a parabola, the ball will go up past the 17 foot mark, then pass it again on the way back down. This happens at .55 seconds (going up), and 1.13 seconds (coming down).

You might be interested in

Please help I will mark Brainly

Sedbober [7]

The answer for your question is 686

8 0

2 years ago

Read 2 more answers

(04.04 LC) Point P on the coordinate grid below shows the location of a stage light. A second stage light is 8 units above P. Th

Morgarella [4.7K]

The square 8 units is above the grid for stage. Coordinations was the location

7 0

3 years ago

Is 3 1/4 closer to 3 or 4<br><br><br>is 3 3/5 closer to 3 or 4

agasfer [191]

Answer:3 and 1/4 is closer to 3 and 3/5 is closer to 4

Step-by-step explanation: one is closer on the number scale then the other

3 0

2 years ago

A store owner puts in an order for product X and product Y for a total of 4,000 units. It costs $0.10 per item to ship of produc

lyudmila [28]

Answer:

0.1x+0.04y=4,000

x+y=352

Step-by-step explanation:

5 0

3 years ago

The variance can never bea.zerob.larger than the standard deviationc.negatived.smaller than the standard deviation

MrRa [10]

Answer:

c.negative

True, by definition since the variance take in count the differences around the mean squared, then we can't have a negative value for a sum of square values.

Step-by-step explanation:

We need to remember that the variance is a measure of dispersion for a dataset respect to a measure of central tendency called the mean. The mean is defined as:

$\bar x = \frac{\sum_{i=1}^n X_i}{n}$

And the population mean is given by:

$\mu= \frac{\sum_{i=1}^n X_i}{N}$

The population variance is defined by:

$\sigma^2 = \frac{\sum_{i=1}^n (X_i -\mu)^2}{N}$

And the sample variance is defined as:

$s^2= \frac{\sum_{i=1}^n (X_i -\bar x)}{n-1}$

Now if we analyze the options we have this:

a.zero

False, if all the values are the same then the mean would be the same to the values and the difference between each value and the mean would 0 and indeed the variance would be 0.

b.larger than the standard deviation

False, if the population standard deviation is $\sigma=2$ then the variance would be $\sigma^2 = 4$ and as we can see is higher than the standard deviation

c.negative

True, by definition since the variance take in count the differences around the mean squared, then we can't have a negative value for a sum of square values.

d.smaller than the standard deviation

False, if the population standard deviation is $\sigma=0.1$ then the variance would be $\sigma^2 = 0.01$ and as we can see is lower than the standard deviation

7 0

3 years ago