Statistics in Analytics: Essential Statistical Techniques

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In today's data-driven world, businesses are increasingly relying on analytics to gain insights and make informed decisions. One of the fundamental pillars of analytics is statistics, which involves using mathematical methods to collect, analyse, and interpret data. From predictive modelling to hypothesis testing, statistics play a crucial role in uncovering meaningful patterns and trends in data.

In addition to mathematical techniques, data visualisation is also a key component of statistical analysis, as it allows us to present complex data in a way that is easy to understand and visually appealing.

Let us explore essential statistical techniques that are commonly used in analytics and how they can enhance our understanding and interpretation of statistical results.

Essential Statistical Techniques Used in Analysis

There are a plethora of statistical techniques you can employ in order to get valuable insights from the data you possess. Down below are listed a few of them with real-world examples for each:

Descriptive Statistics

Descriptive statistics as defined by the word itself ‘describes’ the essential features that one can gather from a dataset. Let’s take a small example to understand it better.

Imagine a hypothetical scenario where a business is tasked with analysing the sales data for a product over the past year. The dataset they have at their disposal includes a wide range of variables such as the number of units sold each month, the average price per unit, and the total revenue generated.

To gain a more granular understanding of the data, the business could employ descriptive statistics techniques. This would allow them to summarise and describe key features of the dataset in a more intuitive manner.

The main techniques employed in descriptive statistics:

  • Mean
  • Median 
  • Mode
  • Variance
  • Standard Deviation

Inferential Statistics

Inferential statistical techniques are employed to ‘infer’ the differences amongst groups of data and then make an assumption about the vast population pertaining to the insights gained from the inference.

Let's say that you own a healthcare company and want to determine whether a new medication is effective in reducing blood pressure. You conduct a randomised controlled trial where you randomly assign patients to receive either the new medication or a placebo. After the trial, you collect data on the blood pressure readings for both groups. 

To draw insightful inferences about the effectiveness of the new medication, you could leverage inferential statistics techniques to analyse the data. By calculating the difference in the mean blood pressure readings between the two groups, you can gain a deeper understanding of the impact of the medication on blood pressure levels using data visualisation.

Following the inference, you could employ a hypothesis test to determine if the difference in blood pressure readings between the two groups is statistically significant. This would allow you to draw strong conclusions about the effectiveness of the new medication in reducing blood pressure.

The main techniques employed in inferential statistics:

  • Hypothesis Testing
  • Confidence Intervals

Correlation Analysis

Correlation Analysis is a statistical technique used to determine whether or not there is a link between two variables/datasets and the strength of that relationship.

Let's say a company wants to investigate the relationship between advertising spend and sales revenue. They have collected an extensive dataset that contains information on the amount of money spent on advertising and the corresponding sales revenue for each month over the past year.

To unravel the intricacies of the relationship between advertising spend and sales revenue, the company could use correlation analysis techniques. This would involve calculating the correlation coefficient, which is a numerical measure that reveals the strength and direction of the linear relationship between two variables.

In the case mentioned above, the variables would be advertising spend and sales revenue. The most widely used correlation coefficients are ‘Spearman’s Rank Correlation Coefficient’ and the ‘Pearson Product-Moment Coefficient’.

If the correlation coefficient is positively skewed, it would indicate a strong positive relationship between advertising spend and sales revenue, signifying that as advertising spending increases, sales revenue also tends to increase. Conversely, if the correlation coefficient is negatively skewed, it would indicate a negative relationship, implying that as advertising spending increases, sales revenue tends to decrease.

Regression Analysis

Picture a world where you're looking to understand the relationship between two or more variables. In walks regression analysis, a statistical method that helps you do just that. This technique is heavily utilised in an array of fields, including economics, finance, marketing, and social sciences.

It aims to pinpoint a mathematical equation that can predict the value of one variable based on the values of other variables. The variable being predicted is known as the dependent variable, while the variables that are used to predict it are known as independent variables or predictors.

Let's say a car manufacturer wants to predict the fuel efficiency of its vehicles based on various factors such as engine size, weight, and transmission type. To achieve this, they conduct a regression analysis to identify the most significant predictors of fuel efficiency.

The manufacturer compiles data on fuel efficiency, engine size, weight, and transmission type for each of their car models. They then utilise regression analysis to construct a mathematical equation that optimally foresees fuel efficiency grounded on these variables.

Upon scrutinising the data, the regression model reveals that engine size and weight wield significant influence on fuel efficiency, whereas transmission type has no substantial impact on fuel efficiency. The car manufacturer can exploit this knowledge to reconfigure its production methods and make adjustments to the design of their cars to fine-tune fuel efficiency.

The dependent variable in the scenario above is the fuel efficiency of the vehicles. This is the variable that the manufacturer is trying to predict based on the values of the independent variables.

The independent variables are the engine size, weight, and transmission type of the vehicles. These are the variables that are used to predict the fuel efficiency of cars.

The most widely used techniques in regression analysis are:

  • Linear Regression
  • Logistic Regression
    NumPy, a popular Python library, is used for regression because it provides a fast and efficient array operation, mathematical functions, linear algebra operations, and interoperability with other libraries. These features of NumPy make it an ideal tool for handling the computations involved in different types of machine learning including regression modelling.


As you can understand from the aforementioned, statistical techniques play a crucial role in insights for businesses to function optimally. Though we discussed a few of the plethora of techniques, it is important to remember that these techniques are used in most types of machine learning.

If you’re interested to know more about techniques such as cluster analysis, time series analysis, and many more, then you should check out the Postgraduate Programme in Data Science and Analytics offered by Imarticus Learning. With expert instructors, hands-on projects, and a industry-relevant curriculum, this programme can help you launch your career in the dynamic field of data science. Don't wait, click now to learn more and enrol today!

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