Part1 - 描述正态分布 描述正态分布从这四点做起: 举栗子: Part2 - 正态分布的数据总结 Min - 最小值1st quartile - 25%的数据在这个点以...
# Import all libraries for this portion of the blog post from scipy.integrate import quad import numpy as np import matplotlib.pyplot as plt %matplotlib inline x = np.linspace(-4, 4, num = 100) constant = 1.0 / np.sqrt(2*np.pi) pdf_normal_distribution = constant * np.exp((-x**...
distribution plots histogram eda statistical-analysis iris-dataset boxplots qqplot seaborn-plots violin-plots contour-plots Updated Jun 7, 2021 Jupyter Notebook UBC-MDS / Movie_Selection Star 0 Code Issues Pull requests An app to select movies for streaming services select movies voting drop...
The significance level is based on a normal distribution assumption, but comparisons of medians are reasonably robust for other distributions. Comparing box plot medians is like a visual hypothesis test, analogous to the t test used for means. In some cases, notches can extend outside the ...
# Make PDF for the normal distribution a functiondefnormalProbabilityDensity(x):constant=1.0/np.sqrt(2*np.pi)return(constant*np.exp((-x**2)/2.0))# Integrate PDF from -.6745 to .6745result_50p,_=quad(normalProbabilityDensity,-.6745,.6745,limit=1000)print(result_50p) ...
Boxplotis probably the most commonly used chart type to compare distribution of several groups. However, you should keep in mind that datadistribution is hiddenbehind each box. For instance, a normal distribution could look exactly the same as a bimodal distribution. Please readmore explanationon ...
How outliers are (for a normal distribution) .7% of the data. What a “minimum” and a “maximum” are Probability Density Function This part of the post is very similar to the68–95–99.7 rule article, but adapted for a boxplot. To be able to understand where the percentages come fro...
this distribution is therefore flattened (platykurtic) relative to a normal distribution. To some extent, this also holds for the other 2 age groups; means as well as standard deviations seem to increase with increasing age.Our histograms make these points much clearer than our boxplot: in box...
override.shape <- c(16,17)#q-q plotqq <- expression_data %>%ggplot(aes(sample=log_count)) + geom_qq(aes(color=specimen, shape=specimen)) +xlab('theoretical normal distribution') +ylab('logged normalized expression') + ggtitle('b) q-q plot') +labs(color="specimen") +guides(color...
For example, running the code bellow will plot a boxplot of a hundred observation sampled from a normal distribution, and will then enable you to pick the outlier point and have it’s label (in this case, that number id) plotted beside the point: set.seed(482) y <- rnorm(100) box...