A confidence interval provides an estimated range of values which is likely to include the unknown parameter (such as mean) of a population when you draw samples many times from the population. For example, if we take 100 random samples, and calculate the 95% confidence interval on each of these samples, then 95 of the 100 samples are likely to contain the population mean. The 95% confidence interval indicates that we are 95% confident that the true population parameter will fall within the given confidence interval.

1. Welch’s t-test Welch’s t-test is a statistical method used in comparing the means of two independent groups when the assumption of equal variance between the two groups is violated. Welch’s t-test extends the traditional two-sample t-test and is specifically designed for situations where we cannot assume equal variances between the two groups being compared. In R, you can perform Welch’s t-test using the t.test function from base R. The basic syntax of this function is as follows:

What is Multiple Hypothesis Testing? Multiple hypothesis testing problem refers to an increase in type I error when you perform multiple statistical tests simultaneously. The type I error (also known as false positive) occurs when the null hypothesis (H0) is actually true but is rejected. Hypothesis test error table A multiple hypothesis testing problem occurs when we have to conduct many hypothesis tests at the same time. In genomics experiments, we often perform thousands of hypothesis tests simultaneously to study differences in gene expression between samples.

Sometimes you need to create a Pandas DataFrame with random data for data analysis and exploration. This article describes three methods of how to create Pandas DataFrame with random data Method 1 The following example demonstrate how to create a Pandas DataFrame with customized values for each column. # load packages import pandas as pd import numpy as np # set random seed for reproducibility np.random.seed(42) # crate random pandas dataframe df = pd.

1 kNN background k-Nearest Neighbors (kNN) is a supervised machine learning algorithm widely used for classification and regression analysis. The kNN algorithm uses the rule of majority for classification and the average of nearest neighbors’ values for regression. The variable parameter (k), also known as nearest neighbours is a crucial parameter in kNN. An appropriate value of k is crucial; a small value may be sensitive to noise, whereas a large value may smooth out patterns.

A scatterplot is useful for plotting the relationship between the two continuous variables as data points on a two-dimensional graph. Scatterplots are useful for identifying patterns, clusters, and trends among variables. In Pandas, you can create a scatterplot from a DataFrame using the DataFrame.plot.scatter function. The basic syntax for DataFrame.plot.scatter for creating a scatterplot: # load package import pandas as pd # create scatterplot df.plot.scatter(x='col1', y='col2', c='red', s=2) Where, parameter description x The column name for x-axis y The column name for y-axis c Color s data point size The following examples illustrate how to create beautiful scatterplots using Pandas.