Number three is D and number 4 is also D.
Explanation for number three:
When you divide 12 by 3, you get 4. This gives us the number we need to multiply 10 by to get our answer. 10 x 4 = 40
Explanation for number 4:
In the last question, the first step to getting to the answer was to divide the first two numbers. This is no different. When you divide 32.5 by 5, you get 6.5. Then you multiply 10 and 6.5, which equals 65.
Using exponential function concepts, it is found that the second function has a greater rate, as 0.8 > 0.2.
<h3>What is an exponential function?</h3>
It is modeled by:
In which:
- a is the initial value, that is, y when x = 0.
- b is the rate of change, as a decimal.
Function 1 is given by:
Hence the rate is b = 0.2.
Considering the values on the table, function 2 is given by:
Hence the rate is b = 0.8.
Hence, the second function has a greater rate, as 0.8 > 0.2.
More can be learned about exponential function concepts at brainly.com/question/14398287
<span>4 medium strawberries→</span><span> 48% of your daily recommend amount of vitamin c
x </span><span>medium strawberries→100% </span><span>of your daily recommend amount of vitamin c
4:48=x:100⇒48x=100*4
48x=400
x=8.33
8.33-4=4.33
4 and 1/3 of </span>medium strawberries you still need for your <span>your daily recommend amount of vitamin c.</span>
This is an equality. There is no answer, by only using the given information, that can be found for "x".
Of the last 100 customers entering a computer shop, 25 have purchased a computer. If the classical probability assessment for computing probability is used, the probability that the next customer will purchase a computer is:
Answer: In classical probability, all the outcomes are equally likely. In this situation, the next customer can either buy the computer or not. Therefore, the probability that the next customer will purchase a computer is:
Here the previous outcomes will not have any impact on the new outcomes. That is the reason the probability that the next customer will purchase a computer is 0.5
Classical probability measures the likelihood of something happening. It also means that every statistical experiment will contain elements that are equally likely to happen.
The example of classical probability is fair dice roll because it is equally probable that it will land on any of the 6 numbers on the die: 1, 2, 3, 4, 5, or 6.