Answer:6
Step-by-step explanation:
In a circle, a radius perpendicular to a chord bisects the chord. So BC = CA BC = 6 (given) x (or AC) = 6 Answer = 6. Tell me if I was right. Thanks.
Answer:
Distance xy is 6
Distance yz is 11
Step-by-step explanation:
Given that the full length between x and z, xz = 17
Point y is located along xz where :
yz = 3x-10
xy = 2x-8
A simple sketch to show the distance and points can be;
x----2x-8---------y------3x-10-----------z
-----------------------17-----------------------
So you can see from this sketch that;
(2x-8) +(3x-10) = 17
2x-8+3x-10=17
5x-18=17
5x=17+18
5x=35
x=35/5 = 7
x=7
Distance xy=2x-8 = 2(7)-8 = 14-8 = 6
Distance yz=3x-10 = 3(7)-10 = 21-10 =11
Answer:
a) Poisson distribution
use a Poisson distribution model when events happen at a constant rate over time or space.
Step-by-step explanation:
<u> Poisson distribution</u>
- Counts based on events in disjoint intervals of time or space produce a Poisson random variable.
- A Poisson random variable has one parameter, its mean λ
- The Poisson model uses a Poisson random variable to describe counts in data.
use a Poisson distribution model when events happen at a constant rate over time or space.
<u>Hyper geometric probability distribution</u>:-
The Hyper geometric probability distribution is a discrete probability distribution that describes the probability of successes (random draws for which the object drawn has a specified feature) in draws without replacement, from a finite population of size that contains exactly objects with that feature where in each draw is either a success or failure.
This is more than geometric function so it is called the <u>Hyper geometric probability distribution </u>
<u></u>
<u>Binomial distribution</u>
- The number of successes in 'n' Bernoulli trials produces a <u>Binomial distribution </u>. The parameters are size 'n' success 'p' and failure 'q'
- The binomial model uses a binomial random variable to describe counts of success observed for a real phenomenon.
Finally use a Binomial distribution when you recognize distinct Bernoulli trials.
<u>Normal distribution</u>:-
- <u>normal distribution is a continuous distribution in which the variate can take all values within a range.</u>
- Examples of continuous distribution are the heights of persons ,the speed of a vehicle., and so on
- Associate normal models with bell shaped distribution of data and the empirical rule.
- connect <u>Normal distribution</u> to sums of like sized effects with central limit theorem
- use histograms and normal quantile plots to judge whether the data match the assumptions of a normal model.
<u>Conclusion</u>:-
Given data use a Poisson distribution model when events happen at a constant rate over time or space.
Answer:
The missing value is 128
Step-by-step explanation:
we know that
The equation of the exponential function is of the form
where
a is the initial value (value of the function when the value of x is equal to zero)
b is the base
r is the rate
b=(1+r)
In this problem , looking at the table, we have that the number of bacteria is doubled every second
so
substitute in the formula
where
f(x) is the number of bacteria
x is the time in seconds
For x=4 sec
substitute
Answer:
Growth.
Step-by-step explanation:
The growth factor is 1.18 ( > 1) and the exponent is > 0.
