However, note that as x approaches 2, the values of y decrease in order to get to -1. In other words, will always be greater or equal to -1 (you can also see this from the graph). This means that as x approaches 2, f(x) will approach -.99 then -.999 then -.9999 until it reaches -1 and then go back up. What is important is that because of this, we can determine that:
This is because for the denominator, the +1 will always be greater than the f(x). This makes this increase towards positive infinity. Note that limits want the values of the function as it approaches it, not at it.
This data suggest that there is more variability in low-dose weight gains than in control weight gains.
Step-by-step explanation:
Let be the variance for the population of weight gains for rats given a low dose, and the variance for the population of weight gains for control rats whose diet did not include the insecticide.
We want to test vs . We have that the sample standard deviation for female control rats was g and for female low-dose rats was g. So, we have observed the value
which comes from a F distribution with degrees of freedom (numerator) and degrees of freedom (denominator).
As we want carry out a test of hypothesis at the significance level of 0.05, we should find the 95th quantile of the F distribution with 17 and 21 degrees of freedom, this value is 2.1389. The rejection region is given by {F > 2.1389}, because the observed value is 3.3176 > 2.1389, we reject the null hypothesis. So, this data suggest that there is more variability in low-dose weight gains than in control weight gains.