Both Matplotlib and SciPy come included when you install Anaconda. We will also use the norm() function from SciPy's stats library. To build the plot, we will use Python and a plotting package called Matplotlib. Show the probability that a resistor picked off the production line is within spec on a plot. FIND:Īssuming a normal distribution, determine the probability that a resistor coming off the production line will be within spec (in the range of 900 Ω to 1100 Ω). This tolerance range means the acceptable range of resistance is 900 Ω to 1100 Ω. The desired resistance tolerance for the 1-kΩ resistors is ± 10%.
#Normal distribution scatter plot matplotlib how to#
A mean resistance of 979.8 kΩ and a standard deviation of 73.10 kΩ represents the sample of resistors. How to plot a normal distribution with matplotlib in python import matplotlib.pyplot as pltimport scipy.statsimport numpy as npxmin 0.0xmax 16.0mean 8.0 std 3.0x np. The resistor's resistances are measured and recorded.
In this post, we will calculate the probability under the normal curve to answer a question like the one below: GIVEN:Īt a facility that manufactures electrical resistors, a statistical sample of 1-kΩ resistors is pulled from the production line. This type of calculation can be helpful to predict the likely hood of a part coming off an assembly line being within a given specification when the statistical properties of all the parts that have come of the assembly line previously are known. To build the Gaussian normal curve, we are going to use Python, Matplotlib, and a module called SciPy.Ĭalculating the probability under a normal curve is useful for engineers. In this post, we will construct a plot that illustrates the standard normal curve and the area we calculated. In the previous post, we calculated the area under the standard normal curve using Python and the erf() function from the math module in Python's Standard Library.
0 kommentar(er)
