Answer:
First statement is correct.
Step-by-step explanation:
If we add or subtract a constant to each term in a set: Mean will increase or decrease by the same constant. Standard Deviation will not change.
If we increase or decrease each term in a set by the same percent (multiply all terms by the constant): Mean will increase or decrease by the same percent. Standard Deviation will increase or decrease by the same percent.
For example:
Standard Deviation of a set: {1,1,4} will be the same as that of {5,5,8} as second set is obtained by adding 4 to each term of the first set.
That's because Standard Deviation shows how much variation there is from the mean. And when adding or subtracting a constant to each term we are shifting the mean of the set by this constant (mean will increase or decrease by the same constant) but the variation from the mean remains the same as all terms are also shifted by the same constant.
So according to this rule, statement (1) is sufficient to get new Standard Deviation, it'll be 30% less than the old.. As for statement (2) it's clearly insufficient as knowing mean gives us no help in getting new Standard Deviation.
Answer:
Option B
Step-by-step explanation:
f(t) = 5000
g(t) = 250t
h(t) = f(t) + g(t) = 5000 + 250t
After 5 years, the amount of money in the account is:
h(t = 5) = 5000 + 250(5) = 5000 + 1250 = 6250$
Answer:
ten scores in order: (Hint: These are in order)
81
81
- 82
- 84
- 85
- 86
- 89
- 93
- 94
- 95
- sum = 870
- mean = 870/10 = 87
- median 85.5 (5 above, 5 under)
- mode = 81 (there are two of them)
Hope this helped you solve the problem :)
Remember to type this correctly!
Found this on a website: jiskha.com/questions/1060894/the-test-score-for-a-math-class-are-shown-below-81-84-82-93-81-85-95-89-86-94-what-are
P.S Bad at Math.
Step-by-step explanation:
Skills needed: Addition Multiplication Division Data Sets
When you get a big set of data there are all sorts of ways to mathematically describe the data. The term "average" is used a lot with data sets. Mean, median, and mode are all types of averages. Together with range, they help describe the data. Definitions: Mean - When people say "average" they usually are talking about the mean. You can figure out the mean by adding up all the numbers in the data and then dividing by the number of numbers. For example, if you have 12 numbers, you add them up and divide by 12. This would give you the mean of the data. Median - The median is the middle number of the data set. It is exactly like it sounds. To figure out the median you put all the numbers in order (highest to lowest or lowest to highest) and then pick the middle number. If there is an odd number of data points, then you will have just one middle number. If there is an even number of data points, then you need to pick the two middle numbers, add them together, and divide by two. That number will be your median. Mode - The mode is the number that appears the most. There are a few tricks to remember about mode: If there are two numbers that appear most often (and the same number of times) then the data has two modes. This is called bimodal. If there are more than 2 then the data would be called multi-modal. If all the numbers appear the same number of times, then the data set has no modes. They all start with the letter M, so it can be hard to remember which is which sometimes. Here are some tricks to help you remember: Mean - Mean is the average. It's also the meanest because it take the most math to figure it out. Median - Median is the middle. They both have a "d" in them. Mode - Mode is the most. They both start with "mo". Range - Range is the difference between the lowest number and the highest number. Take, for example, math test scores. Let's say your best score all year was a 100 and your worst was a 75. Then the rest of the scores don't matter for range. The range is 100-75=25. The range is 25.
Answer:
True
Step-by-step explanation:
The time between customer arrivals is called inter-arrival time. According to Queueing Notation, the inter-arrival time can be model based on difference probability distribution. The probability distribution by which the inter-arrival time can be modeled include:
- Exponential Distribution or Markov distribution
- Constant or Deterministic
- Hyper - exponential
- Arbitrary or General distribution
Given that the probability of mail being delivered is 0.90, to evaluate the probability that the mail will be delivered before 2 p.m for 2 consecutive days will be evaluated as follows:
Let the probability that the milk will be delivered before 2 p.m be P(x). Since the two days are independent events, the probability of the mail being delivered before 2 p.m in 2 consecutive days will be:
P(x)×P(x)
=0.9×0.9
=0.81
