Answer:
a) point estimate is 30%
b) null and alternative hypothesis would be
: p=27%
: p>27%
c) We reject the null hypothesis, percentage working people aged 65-69 had increased
Step-by-step explanation:
<em>a. </em>
Point estimate would be the proportion of the working people aged 65–69 to the sample size and equals
ie 30%
<em>b.</em>
Let p be the proportion of people aged 65–69 who is working. OECD claims that percentage working had increased. Then null and alternative hypothesis would be
: p=27%
: p>27%
<em>c.</em>
z-score of the sample proportion assuming null hypothesis is:
where
- p(s) is the sample proportion of working people aged 65–69 (0.3)
- p is the proportion assumed under null hypothesis. (0.27)
- N is the sample size (600)
then z=
= 1.655
Since one tailed p value of 1.655 = 0.048 < 0.05, sample proportion is significantly different than the proportion assumed in null hypothesis. Therefore we reject the null hypothesis.
It's 12.5; (4×x) -9 = 41; 4×x = 41 +9; x = 50 / 4; x = 25/2=12.5
Probe: 12.5 ×4-9 = 50-9=41.
The answer is x= - 11/ 16
You can check this by plugging in x for -11/16
Answer:
f(x) = -34x + 320
Step-by-step explanation:
In the figure attached, the graph of the data and the options are shown.
We can see from the graph that the best fit line for the data would have a negative slope (as x values increase, y values decrease) and a positive y-intercept (the y value at x = 0). The only option that satisfies these two criteria is f(x) = -34x + 320
Answer:
(56 )log3 x
Step-by-step explanation:
We know that log a^b = bloga
8log3 x^7 = (7*8 )log3 x = 56 log3 x