Answer:
This is simple linear regression analysis. We can determine the line of best fit using technology
Step-by-step explanation:
Answer:
Option C
Step-by-step explanation:
From the number line given in the picture,
Blue arrow has the starting point at a = 6 (Dark point) and moving towards negative side of the number line.
Therefore, number line represents a ≤ 6
3a ≤ 3(6)
3a ≤ 18
3a - 11 ≤ 18 - 11
3a - 11 ≤ 7
Therefore, Option C is the correct option.
Answer:
x = 15/8
Step-by-step explanation:
x/3 = 5/8
Using cross products
8x = 3*5
8x = 15
Divide each side by 8
8x/8 = 15/8
x = 15/8
Answer:
c
Step-by-step explanation:
Here's how this works:
Get everything together into one fraction by finding the LCD and doing the math. The LCD is sin(x) cos(x). Multiplying that in to each term looks like this:
![[sin(x)cos(x)]\frac{sin(x)}{cos(x)}+[sin(x)cos(x)]\frac{cos(x)}{sin(x)} =?](https://tex.z-dn.net/?f=%5Bsin%28x%29cos%28x%29%5D%5Cfrac%7Bsin%28x%29%7D%7Bcos%28x%29%7D%2B%5Bsin%28x%29cos%28x%29%5D%5Cfrac%7Bcos%28x%29%7D%7Bsin%28x%29%7D%20%3D%3F)
In the first term, the cos(x)'s cancel out, and in the second term the sin(x)'s cancel out, leaving:
Put everything over the common denominator now:
Since 
, we will make that substitution:
We could separate that fraction into 2:
×
and 
Therefore, the simplification is
sec(x)csc(x)
This question is incomplete, the complete question is;
Let X denote the time in minutes (rounded to the nearest half minute) for a blood sample to be taken. The probability mass function for X is:
x 0 0.5 1 1.5 2 2.5
f(x) 0.1 0.2 0.3 0.2 0.1 0.1
determine;
a) P( X < 2.5 )
B) P( 0.75 < X ≤ 1.5 )
Answer:
a) P( X < 2.5 ) = 0.9
b) P( 0.75 < X ≤ 1.5 ) = 0.5
Step-by-step explanation:
Given the data in the question;
The probability mass function for X is:
x 0 0.5 1 1.5 2 2.5
f(x) 0.1 0.2 0.3 0.2 0.1 0.1
a) P( X < 2.5 )
P( X < 2.5 ) = p[ x = 0 ] + p[ x = 0.5 ] + p[ x = 1 ] + p[ x = 1.5 ] + p[ x = 2 ]
so
P( X < 2.5 ) = 0.1 + 0.2 + 0.3 + 0.2 + 0.1
P( X < 2.5 ) = 0.9
b) P( 0.75 < X ≤ 1.5 )
P( 0.75 < X ≤ 1.5 ) = p[ x = 1 ] + p[ x = 1.5 ]
so
P( 0.75 < X ≤ 1.5 ) = 0.3 + 0.2
P( 0.75 < X ≤ 1.5 ) = 0.5
