Answer:
C. No, the distribution is not symmetric and the frequencies do not start off low.
Step-by-step explanation:
Hello!
You have the data of daily rainfall for one month (inches)
To arrange a data set in a frequency table you have to determine the number of class intervals you want to make, then you calculate their widths as: "total observations"/"desired number of intervals". Once you have the class width, you can determine the limits of the intervals. For the first interval, you select the minimum value of the sample and add the width to obtain the upper limit. Then you have to use that limit as the lower limit of the next interval and add the width to obtain the next limit. And so on until the last one.
In this example you have a given class width of 0.20 and the lower limit for the first interval is 0.00.
Once you have all intervals determined, you have to arrange the data set from least to greatest and count how many observations correspond to each interval.
The symbol [;) in the intervals indicates that the interval is "closed" on the lower limit and "open" on the upper limit. Meaning, if you have for example the observation "0.20" and the intervals [0.00; 0.20) and [0.20; 0.40), the first interval has a limit equal to 0.20 but is open, meaning that the observation does not belong to it, it belongs to the next interval.
(See table in attachment)
Does the frequency distribution appear to be roughly a normal distribution?
A. No, although the distribution is approximately symmetric, the frequencies do not start low, increase to some maximum frequency, then decrease.
B. No, although the frequencies start low, increase to some maximum, then decrease, the distribution is not symmetric.
C. No, the distribution is not symmetric and the frequencies do not start off low.
D. Yes, all the requirements are met.
As you can see not all defined intervals have at least one observed frequency, most of the observations belong to the first and second one. And there is value, 1.28 inches, that shifts the distribution to the right making it strongly skewed. This distribution is far away from being normally distributed.
Hope it helps!
