You can use the cdf function, which is a cumulative distribution function (CDF), from the SciPy Python package to calculate the probability (p value) from the normal distribution given the mean and standard deviation of the distribution. The CDF represents the probability that a random variable from the given distribution will be less than or equal to a specific value. The following examples explain how to calculate the probability given mean and standard deviation using the cdf function from the SciPy package.

A normal (Gaussian) distribution plot is a graphical representation of the probability density function (PDF) of a normal distribution. Sometimes, you need to shade the areas in a normal distribution plot or density curve to highlight the region of certain probabilities such as the 5% region on the left and right tail of the normal distribution plot. You can use the fill_between function from matplotlib to shade the areas of the normal distribution plots or density curves.

A normal (Gaussian) distribution plot is a graphical representation of the probability density function (PDF) of a normal distribution. The normal distribution plot is a bell-shaped curve and it is symmetric around the mean of the data. The normal distribution is based on two main parameters: mean and standard deviation. The mean is at the center of the distribution, whereas the standard deviation represents the spread of the distribution. The following examples explain how to create a normal distribution plot from a pandas DataFrame.

Multicollinearity occurs when two or more predictors (independent variables) in a regression analysis are highly correlated. Multicollinearity is problematic in machine learning (ML) as it leads to large standard errors for the regression coefficients and underestimates the statistical significance of predictors. Hence, the resulting ML model could not be reliable. Multicollinearity can be detected using various methods such as variance inflation factor (VIF), tolerance, correlation, and condition index. Even though there are several methods for multicollinearity detection, each technique has its threshold for assessing the multicollinearity issue.

bedtools merge is a useful tool in bioinformatics for merging the overlapping or book-ended genomic intervals from the BED file. Most of the time BED files contains the first three required columns (chrom, start, end). But, often there is a fourth name column for the feature annotation. When you merge the BED file with four or more columns, the information is not retained from the fourth column in a merged file.

bedtools merge is a useful tool in bioinformatics for merging the overlapping or book-ended genomic intervals from the BED file. By default, bedtools merge function merge overlapping (by at least 1 bp) interval, but you can control the maximum distance between the two intervals using the -d parameter for merging book-ended genomic intervals. The -d parameter can also be used to specify the minimum overlap between the genomic intervals by giving the negative value equal to the amount of overlap.