7 Types of Statistical Analysis Techniques

Standard deviation
Standard deviation (SD) is used to determine the dispersion of data points. It is a statistical analysis method that helps determine how the data spreads around the mean. A high standard deviation means the data disperses widely from the mean. A low standard deviation shows that most of the data are closer to the mean.
An application of SD is to test whether participants in a survey gave similar questions. If a large percentage of respondents' answers are similar, it means you have a low standard deviation and you can apply their responses to a larger population. To calculate standard deviation, use this formula:
σ2 = Σ(x − μ)2/n

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  σ represents standard deviation
  
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  Σ represents the sum of the data
  
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  x represents the value of the dataset
  
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  μ represents the mean of the data
  
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  n represents the number of data points in the population
  

Example: You can calculate the standard deviation of the data set used in the mean calculation. The first step is to find the variance of the data set. To find variance, subtract each value in the data set from the mean, square the answer, add everything together and divide by the number of data points.
Variance = ((3.5-1)² + (3.5-2) ² + (3.5-3) ² + (3.5-4) ² + (3.5-5) ² + (3.5-6) ²) / 6
Variance = (6.25 + 2.25 + 0.25 + 0.25 + 2.25 + 6.25) / 6
Variance = 17.25/6 = 2.875
Next, you can calculate the square root of the variance to find the standard deviation of the data.
Standard deviation = √2.875 = 1.695
Regression
Regression is a statistical technique used to find a relationship between a dependent variable and an independent variable. It helps track how changes in one variable affect changes in another or the effect of one on the other. Regression can show whether the relationship between two variables is weak, strong or varies over a time interval. The regression formula is:
Y = a + b(x)

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  Y represents the independent variable, or the data used to predict the dependent variable
  
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  x represents the dependent variable which is the variable you want to measure
  
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  a represents the y-intercept or the value of y when x equals zero
  
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  b represents the slope of the regression graph
  

Example: Find the dollar cost of maintaining a car driven for 40,000 miles if the cost of maintenance when there is no mileage on the car is $100. Take b as 0.02, so the cost of maintenance increases by $0.02 for every unit increase in miles driven.

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  Y = cost of maintaining the car
  
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  X = 40,000 miles
  
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  a = $100
  
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  b = $0.02
  

Y = $100 + 0.02(40,000)
Y = $900
This shows that mileage affects the maintenance costs of a car.