Normal distribution is the default probability for many real world scenarios. It represents a symmetric distribution where most of the observations cluster around the central peak called as mean of the distribution. A normal distribution can be thought of as a bell curve or Gaussian Distribution which typically has two parameters: mean and standard deviation (SD). The parameter used to measure the variability of observations around the mean is called as standard deviation. The probabilities for values occurring near mean are higher than the values far away from the mean. The parameters of the normal distribution plot defining the shape and the probabilities are mean and standard deviation. The area of the plot between two different points in the normal distribution plot represents the probability of the value occurring between those two points.

Please generate the nromal distribution for the following cases suing Python:

(1) IQ scores (the mean is typically 100, SD = 15)

(2) Marks of students in a class (mean = 60, SD = 20)

(3) Measure of weight (mean = 80 kg, SD = 10)

(4) Measure of blood pressure (mean = 120/80, SD = 20)

(5) Measure of wealth (measurement in money; mean = 50,000,SD=100)

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