Answer:
Step-by-step explanation:
We will use following steps to measure exactly 4 gallons of water with the help of 5 gallons and 3 gallons empty jugs.
1). Fill 3 gallons jug completely.
2). Pour this 3 gallons of water into 5 gallons jug. Now we have 3 gallons of water in 5 gallons jug and 3 gallons jug empty.
We can add 2 gallons of water in the empty space of 5 gallons jug more.
3). Fill the 3 gallons jug with the water again.
4). Pour this water into 5 gallons jug which can hold 2 gallons of water more.
Now we have 5 gallons of jug filled fully and 1 gallon water remaining in the 3 gallons jug.
5). Empty the 5 gallons jug completely.
6). Pour the remaining 1 gallon of remaining water in 3 gallons jug into 5 gallons jug.
7). Fill the 3 gallons jug completely and pour it into 5 gallon jug.
8). Finally we have 4 gallons of water in the 5 gallons jug.
Answer:
Step-by-step explanation:
Hope this helps :D
Answer:
I tried solving it and didn't get same exact numbers but I got 8.67 million people so it might be answer choice B.
Answer:
inches
Step-by-step explanation:
So to go from 300 to 3700 you have to multiply by
Answer:
Answer = d. Chi-Square Goodness of Fit
Step-by-step explanation:
A decision maker may need to understand whether an actual sample distribution matches with a known theoretical probability distribution such as Normal distribution and so on. The Goodness-of-fit Test is a type of Chi-Square test that can be used to determine if a data set follows a Normal distribution and how well it fits the distribution. The Chi-Square test for Goodness-of-fit enables us to determine the extent to which theoretical probability distributions coincide with empirical sample distribution. To apply the test, a particular theoretical distribution is first hypothesized for a given population and then the test is carried out to determine whether or not the sample data could have come from the population of interest with hypothesized theoretical distribution. The observed frequencies or values come from the sample and the expected frequencies or values come from the theoretical hypothesized probability distribution. The Goodness-of-fit now focuses on the differences between the observed values and the expected values. Large differences between the two distributions throw doubt on the assumption that the hypothesized theoretical distribution is correct and small differences between the two distributions may be assumed to be resulting from sampling error.
