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Data Analysis Techniques

Statistical Analysis​

Statistical analysis could involve running a t-test to compare the means of two groups, or running a regression to predict a continuous variable. Here's a simple example of a t-test:

const group1 = [1, 2, 3, 4, 5]
const group2 = [6, 7, 8, 9, 10]

const mean1 = group1.reduce((sum, value) => sum + value, 0) / group1.length
const mean2 = group2.reduce((sum, value) => sum + value, 0) / group2.length

const var1 =
  group1.reduce((sum, value) => sum + Math.pow(value - mean1, 2), 0) /
  (group1.length - 1)
const var2 =
  group2.reduce((sum, value) => sum + Math.pow(value - mean2, 2), 0) /
  (group2.length - 1)

const t =
  (mean1 - mean2) / Math.sqrt(var1 / group1.length + var2 / group2.length)

Statistical measures​

  • Mean (Average): The sum of all the data divided by the number of data points. It gives the central tendency of the data.

  • Median: The middle value in the dataset when it's sorted in ascending or descending order. If there's an even number of observations, the median is the average of the two middle numbers.

  • Mode: The most frequently occurring value in the dataset.

  • Range: The difference between the maximum and minimum values in the dataset. It gives an idea of the spread of the data.

  • Variance: The average of the squared differences from the mean. It measures how far each number in the set is from the mean and thus from every other number in the set.

  • Standard Deviation: The square root of the variance. It measures the amount of variation or dispersion in the dataset.

  • Percentiles: Percentiles divide the dataset into 100 equal parts. They can give a better understanding of the distribution of the data.

  • Quartiles: Quartiles divide the dataset into four equal parts. The first quartile (Q1) is the 25th percentile, the second quartile (Q2) is the median or 50th percentile, and the third quartile (Q3) is the 75th percentile.

  • Correlation Coefficient: A measure that determines the degree to which two variables' movements are associated.

  • Regression Analysis: A set of statistical processes for estimating the relationships among variables.

These measures provide a comprehensive understanding of the dataset and are fundamental to many statistical analyses.

Machine Learning​

Machine learning could involve training a machine learning model to make predictions. This is more complex and typically requires a machine learning library, but here's a simple example of a linear regression:

const data = [
  { x: 1, y: 2 },
  { x: 2, y: 4 },
  { x: 3, y: 6 },
]

const meanX = data.reduce((sum, value) => sum + value.x, 0) / data.length
const meanY = data.reduce((sum, value) => sum + value.y, 0) / data.length

const slope =
  data.reduce((sum, value) => sum + (value.x - meanX) * (value.y - meanY), 0) /
  data.reduce((sum, value) => sum + Math.pow(value.x - meanX, 2), 0)

const intercept = meanY - slope * meanX

// Now you can make predictions
const prediction = slope * 4 + intercept // x = 4