Answer:
a) Null and alternative hypothesis:

b) A Type I error is made when a true null hypothesis is rejected. In this case, it would mean a conclusion that the proportion is significantly bigger than 10%, when in fact it is not.
c) The consequences would be that they would be more optimistic than they should about the result of the investment, expecting a proportion of students that is bigger than the true population proportion.
d) A Type II error is made when a false null hypothesis is failed to be rejected. This would mean that, although the proportion is significantly bigger than 10%, there is no enough evidence and it is concluded erroneously that the proportion is not significantly bigger than 10%
e) The consequences would be that the investment may not be made, even when the results would have been more positive than expected from the conclusion of the hypothesis test.
Step-by-step explanation:
a) The hypothesis should be carried to test if the proportion of students that would eat there at least once a week is significantly higher than 10%.
Then, the alternative or spectulative hypothesis will state this claim: that the population proportion is significantly bigger than 10%.
On the contrary, the null hypothesis will state that this proportion is not significantly higher than 10%.
This can be written as:

Answer:
D.
Step-by-step explanation:
y-intercept is (0, 3)
x-intercept is (4.5, 0)
formula to find the slope from two points is

then the slope of the line that crosses (0, 3) & (4.5, 0) is

Answer:
<em><u>let </u></em><em><u>the </u></em><em><u>ratio</u></em><em><u> be</u></em><em><u> in</u></em><em><u> </u></em><em><u>x </u></em>
<em><u>a/</u></em><em><u>q</u></em><em><u>. </u></em><em><u>3</u></em><em><u>x</u></em><em><u> </u></em><em><u>+</u></em><em><u> </u></em><em><u>2</u></em><em><u>x</u></em><em><u> </u></em><em><u>=</u></em><em><u> </u></em><em><u>3</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em>
<em><u> </u></em><em><u> </u></em><em><u>5</u></em><em><u>x</u></em><em><u> </u></em><em><u>=</u></em><em><u> </u></em><em><u>3</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em>
<em><u>x </u></em><em><u>=</u></em><em><u> </u></em><em><u>3</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>/</u></em><em><u>5</u></em>
<em><u>x=</u></em><em><u> </u></em><em><u>6</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em>
<em><u>Leona </u></em><em><u>=</u></em><em><u> </u></em><em><u>6</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>*</u></em><em><u>3</u></em><em><u> </u></em><em><u>=</u></em><em><u> </u></em><em><u>1</u></em><em><u>8</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em>
<em><u>Janet </u></em><em><u>=</u></em><em><u> </u></em><em><u>6</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>*</u></em><em><u>2</u></em><em><u> </u></em><em><u>=</u></em><em><u> </u></em><em><u>1</u></em><em><u>2</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em><em><u>0</u></em>
<em><u>hope</u></em><em><u> it</u></em><em><u> helps</u></em>
(a)
Q1, the first quartile, 25th percentile, is greater than or equal to 1/4 of the points. It's in the first bar so we can estimate Q1=5. In reality the bar includes values from 0 to 9 or 10 (not clear which) and has around 37% of the points so we might estimate Q1 a bit higher as it's 2/3 of the points, say Q1=7.
The median is bigger than half the points. First bar is 37%, next is 22%, so its about halfway in the second bar, median=15
Third bar is 11%, so 70% so far. Four bar is 5%, so we're at the right end of the fourth bar for Q3, the third quartile, 75th percentile, say Q3=40
b
When the data is heavily skewed left like it is here, the median tends to be lower than the mean. The 5% of the data from 80 to 120 averages around 100 so adds 5 to the mean, and 8% of the data from the 60 to 80 adds another 5.6, 15% of the data from 40 to 60 adds about 7.5, plus the rest, so the mean is gonna be way bigger than the median of around 15.