Application of Statistical Multivariate Techniques to Business Problems

Application of Statistical Multivariate Techniques to Business Problems

Current business environments have become complicated with numerous variables and voluminous data that require analysis. In such a situation, multivariate techniques have come in handy to offer the necessary solution for analyzing various business variables. Multivariate analysis techniques are statistical, mathematical techniques that help in studying a problem with more than two variables (Weedmark, 2019). The technique is used in various fields such as business, geology, medicine, agriculture, and meteorology. Multivariable analysis techniques apply the prediction model and the descriptive model in analyzing a phenomenon. Many companies are currently using this technique to solve multiple and complex data business circumstances.

In many business instances, data scientists use advanced statistical software to conduct and analyze business data. These experts develop strategies and analysis techniques in their minds and use them to inform business stakeholders. As much as it may not be necessary for managers, marketers, and business owners to have the nitty details of multivariate analysis, they must understand the metrics to make better and informed decisions in their business practices (Weedmark, 2019). Results from the multivariate analysis are also significant as they help business owners and the government to know the best strategies to take in sustaining the economy.

Business requires an analysis of numerous variables that determine revenues. Some of the variables that need analysis include market prices, market conditions, advertising strategies, techniques that the competitors use, and weather conditions (Zikmund, 2013). multivariate analysis is also important in categorizing customers, product packaging, and market trends are other variables that multivariate analysis helps to analyze in businesses. The multivariate analysis classifies all these variables into either dependent or independent variables. Variables or factors under examination are dependent variables, while those that influence the variables under investigation are independent variables.

Multivariate analysis has various techniques such as multiple linear regression, logistic regression, discriminant function analysis, analysis of variance techniques, and factor analysis, among others (Marcoulides, 2013). One of the most common techniques used in forecasting is the multiple linear regression analysis. The technique helps in predicting what will happen when there is a change in the independent variables. The technique shows the changes and the relationship that exists between dependent and independent variables on a regression line. For example, when testing the influence of advertising on sales, the regression line will indicate that sales increase with an increase in advertising. Multiple regression analysis shows more that one variable. It can help to show the relationship with other variables such as the relationship between sales, advertising, market prices, changes in weekly market conditions, and GDP levels.  Multiple regression analysis helps to show the specific variables or a combination of variables that influences sales.

Logistic regression analysis is another multivariate technique that similarly analyses business variables as multiple regression analysis. However, the method is beneficial when an analyst wants to find out the relationship between binary variables, either variable one or variable two. For example, when a business analyst wants to know the relationship existing between buying decisions and other variables, the analyst would use logistic regression. The results of this regression will give either choice A or B, to sell or not to sell.

Discriminant function analysis, another example of multivariate techniques, helps in categorizing variables into specific groups. For example, it is used in profiling customers based on their purchase characteristics and spending abilities (Kočišová & Mišanková, 2014). When a business analysis wants to know the person that is likely to make the next purchase, the analyst will group his customers and classify them by age, gender, education levels, professional occupations or the level of income. The analysis will then compare the spending levels of each category to identify how much they spent on a product in the previous year. Information obtained will help to predict the people who likely to make purchases. Such information will help in identifying individuals who spend more on a product and those that are likely to become loyal customers.

Multivariate analysis of variance technique, also known as MANOVA is another analysis technique that helps in analyzing how one or more independent variables influence two or more dependent variables (Correia et al., 2016). ANOVA is another analysis technique that has a close relationship with MANOVA. However, ANOVA helps in the analysis of the differences that exist between group-variables, while MANOVA shows how many dependent variables relate to other variables in more than two groups.  For example,  if a business person wishes to change the strategy for delivering services, multivariate analysis of variance will help the business person to compare and identify the differences between the former strategy and the current strategy. The business person will also determine the extent of customer satisfaction and the influence on employee motivation.

Many multivariate statistical techniques help in the analysis of business variables. All these techniques play a significant role in analyzing business variables and making vital decisions. Nevertheless, one needs to assess the benefits of using a particular method over the other. It is also crucial for one to evaluate the variables that and individual wishes to analyze to identify the best technique that is suitable for analyzing such variables. To get reliable analysis results, analysts need to use reliable data and analysis techniques to make informed decisions about business trends.