Answer:
Step-by-step explanation:
We can rewrite the equation as
Notice that we have 
in both the numerator and the denominator, so it looks like we can divide it out. However, what if is 
? Then we would have 
, which is undefined. So although it looks like the numerator and denominator can be simplified, the resulting function we would get from simplification would not have the same behavior as this one (since such a function would be defined for 
, but this one is not).
A point of discontinuity refers to a particular point which is included in the simplified function, but which is not included in the original one. In this case, the point which is not included in the unsimplified function is at . In the simplified version of the function, if we plug in , we get
So the point is our only point of discontinuity.
It's also important to distinguish between specific points of discontinuity and vertical asymptotes. This function also has a vertical asymptote at 
(since it causes the denominator to be 0), but the difference in behavior is that in the case of the asymptote, only the denominator becomes 0 for a specific value of 
There are 20 saxophone players. For every 6 flutes there are 4 saxophones so if you do 6x5=30. Then you would do 4x5 and get 20. 30 flutes plus 20 saxophones = 50 total students
Answer:
Step-by-step explanation:
2(5-4g) + 3g - 11 = 5(g-3) - 12 - 3g (remove the parantheses)
10 - 8g + 3g - 11 = 5g - 15 - 12 -3g (Calculate and collect like terms)
-1 - 5g = 2g - 27 (move the terms)
-5g -2g = 27 + 1 (collect like terms and calculate)
-7g = -26 (divide both sides)
so G = 26/7
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.
