Answer:
36
Step-by-step explanation:
split it into half, and you'll see that each rectangle when split into half has 2 triangles that are 12 ft, and now AECD is 3 lots of those 12 triangles that is 12 feet in area, therefore total would be 12 x 3 = 36
Answer: 
Step-by-step explanation:
We know that:
: The number of vibrations per second of the nylon guitar string.
: Tension.
: The length of the string.
Since varies directly with the square root of the and inversely with , the equation has the following form:
Where "k" is the constant of variation.
Knowing that when 
and 
, 
, we can find the value of "k":
Finally, in order to find the tension when the length is 0.3 meters and the number of vibrations is 12, you need to substitute these values and the value of "k" into and solve for :
The length of the enlarged copy in feet is 2.7 feet.
<u>Step-by-step explanation</u>:
- The painting is 4 inches wide and 2 feet long.
- convert inches into feet (1 inch=0.083 foot). Therefore, 4 inches=0.3 feet
- The enlarged copy is 1 foot wide and 'x' feet long.
The width is enlarged from 0.3 to 1 which is increased by 0.7 feet.
∴ The length should also be increased by 0.7 feet = 2+0.7 = 2.7 feet
Answer:
x= -3454
Or,
x= -3.58
Step-by-step explanation:
350x² - 2199x + 3454 = 0
350x² - 2199x = 0 - 3454
350x² - 2199x = -3454
x(350x - 2199) = -3454
Now,
Either ,
x=-3454
or,
350x-2199= -3454
350x= -3454+2199
350x= -1255
x= -1255/350
x= -3.58
Answer:
B. Sample mean used to estimate a population mean.
C. Sample variance used to estimate a population variance.
D. Sample proportion used to estimate a population proportion.
Step-by-step explanation:
This is because the mean of the sampling distribution of the mean tends to target the population mean. Also, the mean of the sampling distribution of the variance tends to target the population variance.
This means that the sample mean and variance tend to target the population mean and variance, respectively, instead of systematically tending to underestimate or overestimate that value. This is why sample means and variances are good estimators of population means and variances, respectively. This is also true for proportions but not true for medians, ranges and standard deviations.
