The two main parameters of the normal distribution are  and .  is a location parameter which determines the location of the peak of the normal distribution on the real number line.  is a scale parameter which determines the concentration of the density around the mean. Larger 's lead the normal to spread out more than smaller 's. 

The mean determines where the normal distribution lies on the real number line, while the variance determines the spread of the distribution. 

A normal distribution is one in which the values are evenly distributed both above and below the mean.  A population has a precisely normal distribution if the mean, mode, and median are all equal.  For the population of 3,4,5,5,5,6,7, the mean, mode, and median are all 5.

In a normal distribution, the number of values within one positive standard deviation of the mean is equal to the number of values within one negative standard deviation of the mean.  The reason for this is that the values below the population mean exactly parallel the values above the mean.

2) and 3) are true by definition of the Empirical Rule - also known as the 68-95-99.7 Rule. Using our information with mean of 100 and a standard deviation of 5 we can create a bell curve with 100 in the middle. One standard deviation out from the mean would give us a range from 95 to 105 and would be in our 68% section. If we go two standard deviations out from 100 we would get the range 90 to 110 thus lying in the 95% section. Lastly, when we go out 3 standard deviations we get a range of 85 to 115 thus falling within the 99.7% section.

1) can be deduced as true because it means that 68% of observations are between 95 and 105, and removing one of those bounds (namely, the upper one) adds the 16%, since , of observations larger than 105 leading to 68 + 16 = 84% of observations greater than 95.

A normal distrubtion is completely symmetrical and has not outliers.  This means that the mean is not pulled to either side by outliers and should lie directly in the middle along with the median (the middle number of the distribution).  

The empirical rule tells us that the probability that a random data point is within one standard deviation of the mean is approximately 68%, not 78%.

The standard normal distribution is just like any other normal distribution that you might have looked at except that it has a standard deviation of 1 and a mean of 0. 

The data is not normal by virtue of being skewed left, which also supports Cheyenne's theory... there is no way of knowing wether this data was a representative sample, but also no option with ii, iii and iv was provided to avoid frustration/confusion

1) Percentile for Z=1 is .8413 - or - .1587 in one tail - or - .3174 in both tails -

1 - .3174=.6826

2) Percentile for Z=2 is .9772 - or - .0228 in one tail - or - .0456 in both tails -

1 - .0456=.9544

3) Percentile for Z=3 is .9987 - or - .0013 in one tail - or - .0026 in both tails -

1 - .0026=.9974