One standard deviation
0.088508
It is 0.877
The Z value is negative, but area is always positive.
The area under the normal distribution curve represents the probability of an event occurring that is normally distributed. So, the area under the entire normal distribution curve must be 1 (equal to 100%). For example, if the mean (average) male height is 5'10" then there is a 50% chance that a randomly selected male will have a height that is below or exactly 5'10". This is because the area under the normal curve from the left hand side up to the mean consists of half of the entire area of the normal curve. This leads us to the definitions of z-scores and standard deviations to represent how far along the normal curve a particular value is. We can calculate the likelihood of the value by finding the area under the normal curve to that point, usually by using a z-score cdf (cumulative density function) utility of a calculator or statistics software.
Area to the left of z = -1.72 = area to the right of z = 1.72 That is ALL the "working" that you will be able to show - unless you are into some serious high level mathematics. Most school teachers and many university lecturers will not be able to integrate the standard normal distribution: they will look it up in tables. (I have an MSc in Mathematical Statistics and I could do it but not without difficulty). Pr(z < -1.72) = 0.042716
-1.43 (approx)
It is 0.839
0.088508
It is 0.877
The Z value is negative, but area is always positive.
In a normal distribution half (50%) of the distribution falls below (to the left of) the mean.
The area under the normal distribution curve represents the probability of an event occurring that is normally distributed. So, the area under the entire normal distribution curve must be 1 (equal to 100%). For example, if the mean (average) male height is 5'10" then there is a 50% chance that a randomly selected male will have a height that is below or exactly 5'10". This is because the area under the normal curve from the left hand side up to the mean consists of half of the entire area of the normal curve. This leads us to the definitions of z-scores and standard deviations to represent how far along the normal curve a particular value is. We can calculate the likelihood of the value by finding the area under the normal curve to that point, usually by using a z-score cdf (cumulative density function) utility of a calculator or statistics software.
At Z = 0.25, the area (larger) to the left of the line is 0.5987. The smaller area to the right of the line is 1 - 0.5987 = 0.4013. Therefore, the smaller section corresponds to 40.13% of the area.
Symmetric
Area to the left of z = -1.72 = area to the right of z = 1.72 That is ALL the "working" that you will be able to show - unless you are into some serious high level mathematics. Most school teachers and many university lecturers will not be able to integrate the standard normal distribution: they will look it up in tables. (I have an MSc in Mathematical Statistics and I could do it but not without difficulty). Pr(z < -1.72) = 0.042716
Approx 78.88 % Normal distribution tables give the area under the normal curve between the mean where z = 0 and the given number of standard deviations (z value) to its right; negative z values are to the left of the mean. Looking up z = 1.25 gives 0.3944 (using 4 figure tables). → area between -1.25 and 1.25 is 0.3944 + 0.3944 = 0.7888 → the proportion of the normal distribution between z = -1.25 and z = 1.25 is (approx) 78.88 %
It determines the location of the graph: left or right - but not its shape.