Volume of cube=side³
ok, so you need to know the difference or sum of cubes
a³+b³=(a+b)(x²-xy+y²)
so
(4p)³+(2q²)³=
(4p+2q²)((4p)²-(4p)(2q²)+(2q²)²)=
(4p+2q²)(16p²-8pq²+4q⁴)
3rd option
Answer:
P(6) = 0.6217
Step-by-step explanation:
To find P(6), which is the probability of getting a 6 or less, we will need to first calculate two things: the mean of the sample (also known as the "expected value") and the standard deviation of the sample.
Mean = np
Here, "n" is the sample size and "p" is the probability of the outcome of interest, which could be getting a heads when a tossing a coin, for instanc
So, Mean = n × p = (18) ×(0.30) = 5.4
Next we we will find the standard deviation:
Standard Deviation = 
n = 18 and p = 0.3 "q" is simply the probability of the other possible outcome (maybe getting a tails when flipping a coin), so q = 1 - p
Standard Deviation =
= 1.944
Now calculate the Z score for 6 successes.
Z = ( of successes we're interested in - Mean) ÷ (Standard Deviation)
=(6-5.4) ÷ (1.944) = 0.309
we have our Z-score, we look on the normal distribution and find the area of the curve to the left of a Z value of 0.309. This is basically adding up all of the possibilities for getting less than or equal to 6 successes. So, we get 0.6217.
Answer:x=4
Step-by-step explanation:
This triangle is an equilateral triangle with all angles equal.
Sum of angles in a triangle=180
17x-8+17x-8+17x-8=180
Collect like terms
17x+17x+17x-8-8-8=180
51x-24=180
51x=180+24
51x=204
Divide both sides by 51
51x/51=204/51
x=4
Answer:
Step-by-step explanation:
The question is incomplete. Here is the complete question.
the batting wang xiu ying uses to fill quilts has a thermal conductivity rate of 0.03 watts (W) per meter(m) per degree celsius. what is the batting thermal conductivity when w/cm•c
Given
K = 0.03W/m°C
Required
Convert K to W/cm°C
Since 1m = 100cm,
K = 0.03w/1×100cm°C
K = 3×10^-2w/10² cm°C
K = 3 × 10^-2w/10² cm°C
K = 3w× 10^{-2-2}cm°C
K = 3w×10^-4cm°C
K = 0.0003w/cm°C
Answer:
Solution-
We know that,
Residual value = Given value - Predicted value
The table for residual values is shown below,
Plotting a graph, by taking the residual values on ordinate and values of given x on abscissa, a random pattern is obtained where the points are evenly distributed about x-axis.
We know that,
If the points in a residual plot are randomly dispersed around the horizontal or x-axis, a linear regression model is appropriate for the data. Otherwise, a non-linear model is more appropriate.
As, in this case the points are distributed randomly around x-axis, so the residual plot show that the line of regression is best fit for the data set.
Hope this helps!
Step-by-step explanation:
