<u>Picture 1:</u>
To figure this out, notice the pattern happening on x. It's simply counting to four, so the first blank is 1 and the next is 2.
The y coordinates seem to be going up by 9. 27 plus 9 is 36, so the answer to the fourth box is 36.
Another way to see how this is correct is to notice that the x is multiplying by 9 to get y. It works out as you look at it and plug it in!
<u>Picture 2:</u>
Yes, this is a proportional relationship. Since Dennis is adding 3 logs every hour, it is keeping a consistent pattern.
<u>Picture 3:</u>
If Jane is driving 60 miles per hour, the first hour she would've gone 60 miles. After a second hour, she would've gone 120 miles. Multiply 60 to your x coordinates to figure this out. You should get 60, 120, 180, and 240 for each box.
Answer:
Confidence Interval for the mean
Step-by-step explanation:
Confidence interval is made using the observations of a <em>sample</em> of data obtained from a population, so it is constructed in such a way, that, with a certain <em>level of confidence </em>(this is the statement mentioned in the question), that is, one could have a percentage of probability that the interval, or range around the value obtained, frequently 95%, contains the true value of a population parameter (in this case, the population mean).
It is one way to extract information from a population using a sample of it. This kind of information is what inference statistic is always looking for.
An <u>approximation</u> about how to construct this interval or range:
- Select a random sample.
- For the specific case of a <em>mean</em>, you need to calculate the mean of the <em>sample </em>(sample mean), and, if standard deviation is unknown or not mentioned, also calculate the sample standard deviation.
- With this information, and acknowledged that these values follows a standard normal distribution (a normal distribution with mean 0 and a standard deviation of 1), represented by random variable Z, one can use all this information to calculate a <em>confidence interval for the mean</em>, with a certain confidence previously choosen (for example, 95%), that the population mean must be in this interval or <em>range around this sample mean.</em>
