![]() ![]() None of the salaries repeat here, so there is no mode.Īges of 10 employees: 22, 23, 27, 25, 27, 30, 29, 22, 32, 26 yearsĢ2 and 27 both appear twice in the dataset. Let’s look at the same salary dataset again. Mode is the only measure of centre that can be used with nominal values. No mode: No values are repeated in the dataset.Multimodal: More than 2 values occur with the same greatest frequency.Bimodal: Two values occur with the same greatest frequency.Here are some common types of modes in a dataset: Mode of a dataset is the most frequently occurring value. Hence the median is a Resistant measure of centre. The median did not change by a large amount with the addition of an outlier. Median without the outlier is $40,714 and median with the outlier is $53,423. Now let’s add the CEO’s salary to the list. If the number of values in the dataset is odd, median is the number located at the exact middle of the sorted data. If the number of values in the dataset is even, median is the mean of the middle two numbers in the sorted data. Median is the middle value when the values in the dataset are arranged in ascending or descending order. Here is a simple explanation to understand the effect of outliers: Hence the mean is called a non-resistant measure of centre. An outlier (high or low) can dramatically alter the mean. In our example, Mean without the outlier is $53,715 and mean with the outlier is $1,833,249. The CEO’s salary is an outlier, meaning it is markedly higher than the other salaries in the dataset. Let’s add another value to the salary dataset – a CEO’s salary. ![]() Mean = Sum of all data values/number of data values The mean, also known as average is the measure of centre found by adding the data values and dividing the total by the number of items. ![]() There are 3 measures of centre commonly used in exploratory data analysis: Mean, Median and Mode. Measure of centre is a value at the centre or middle of a dataset. Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively Statistical Features. Basic statistics for exploring data : Measures of Centre We will find that interesting stories may arise from both the norm and the exceptions in data. One way to do that is using measures of centre. In exploratory data analysis we get familiar with data, ask questions, visualize data in a number of forms, look for relationships between the variables, look for outliers, patterns and trends in data. Tukey created a branch of data analysis called Exploratory Data Analysis. ![]()
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