Tables of the cumulative probability distribution of the standard normal distribution (mean = 0, variance = 1) are readily available. Almost all textbooks on statistics will contain one and there are several sources on the net. For each value of z, the table gives Φ(z) = prob(Z < z). The tables usually gives value of z in steps of 0.01 for z ≥ 0. For a particular value of z, the height of the probability density function is approximately 100*[Φ(z+0.01) - Φ(z)].
As mentioned above, the tables give figures for z ≥ 0. For z < 0 you simply use the symmetry of the normal distribution.
It has no special name - other than a normal (or Gaussian) distribution graph.
No
When putting the scores in, you use the normal distribution graph, which is the best start.
The answer depends on what the graph is of: the distribution function or the cumulative distribution function.
Roughly speaking, yes. However, bells do not extend asymptotically to infinity.
It has no special name - other than a normal (or Gaussian) distribution graph.
A normal distribution is symmetrical; the mean, median and mode are all the same, on the line of symmetry (middle) of the graph.
It determines the location of the graph: left or right - but not its shape.
No
When putting the scores in, you use the normal distribution graph, which is the best start.
A normal distribution is symmetric and when looked at on a graph, the graph looks like a bell shaped curve. Approximately 95 percent of its values should lie within two standard deviations of the mean. Frequency of the data lies mostly in the middle of the curve.
The bell curve graph is another name for a normal (Gaussian) distribution graph. A Gaussian function is a certain kind of function whose graph results in a bell-shaped curve.
The answer depends on what the graph is of: the distribution function or the cumulative distribution function.
Roughly speaking, yes. However, bells do not extend asymptotically to infinity.
We prefer mostly normal distribution, because most of the data around us follows normal distribution example height, weight etc. will follow normal. We can check it by plotting the graph then we can see the bell curve on the histogram. The most importantly by CLT(central limit theorem) and law of large numbers, we can say that as n is large the data follows normal distribution.
frequency is the kinds of the line graph, bar graph, picture graph, pie graph. that's all
The standard normal distribution is a normal distribution with mean 0 and variance 1.