Introduction
Data management is one of the issues that can be challenging in any organization. This becomes more challenging especially when the organization is growing. Business growth means more data being managed. The challenge however comes in when trying to compile business data. This can be quite challenging since when making business reports, data from different departments has to be compiled. Failure to do so would lead to no logical comparison of business data. Data collected by the marketing department has to be compiled with that in the finance department; the human resource has also to forward its data. All this data has to be compiled in making decisions.
The management has one key task of analyzing business data and making any changes on the business depending on the analyzed data. Business growth can only be determined by how a business handles its information and data in making amicable decisions. Unless there e is proper data management, an organization cannot achieve its business growth. Because of these, it is important for the management to identify proper multivariate techniques for analyzing business data. An efficient multivariate technique is also crucial in making decisions relating to market competitions. The management will be able to cope with competition from emerging and existing firms and maintain business growth.
Multivariate data analysis involves a group of statistical procedures that need to be carried out in managing data in an organization. These methods of analysis can be used to analyses data, which has three or more variables. It is essential to use multivariate techniques in data analysis since most challenges encountered in a business are multi-dimensional. An example is handling the marketing department. There are a number of factors that the business needs to consider before making marketing decisions such as consumer taste and preference. The company will have to continuously make frequent decisions especially in its managing department will have to consider some of the customer needs. Unless the customer is satisfied, the business can certainly not meet its predicted growth, the only way to make this possible is through having a proper multivariate technique.
Multivariate Techniques
There are a number of considerations that have to be made when selecting the best method of multivariable technique. One of the factors that need to be considered is data type. There are three different types of data that an organization handles and especially this company. This data is classified based on consumer response. First category of response is independent response, this include data such as individual gender or age which is not affected by other business variables. The second category is dependent response, this kind of response, which is affected by other independent variables. An example is how a customer’s taste for cakes is affected by their age. The third category is the interdependent response, such kind of a response has no variable that can be classified as dependent or independent. These factors are the key determiners on the multivariate technique to use.
Since these techniques are mostly used for determining the dependent and interdependent variable, the type of technique selected has to be properly selected. Dependence methods are used to identify the dependent variable that is explained using other independent variables. The dependence techniques used in multivariate analysis include discriminant analysis, multiple regression analysis and conjoint analysis. This method is most suitable for analyzing data in a business whose information is multi-dimensional. The techniques are however different in application with some being better than the others are. There are other methods that can be used for interdependent analysis; they include factor analysis, cluster analysis and multidimensional scaling (Berry, W. D., & Feldman, S,199).
Multiple Regression
Multiple regression is a technique that is used to predict a single variable from one independent variable or a group of them. It is the most suitable method for analyzing business data in different models. Once an independent variable has been identified.it is possible to make predictions that are more accurate on some of the factors affecting the business. The formula used for calculation in multiple regression is similar to that of linear regression though it has some additional variables. While the formula for making predictions in linear regression is Y’ = a + bX, the formula for multiple regression is Y’ = a + b1 + X + b X.
Y’ represents a value that is predicted. This value also represents the dependent variable selected.
a represents the “Y’ intercept.
b1 =This is a changing variable. It represents the in Y for every one increment that occurs in X
b2 =this represents the change in Y in every one increment that occurs in X2.
X=It represents an independent variable which is used to predict the value of Y.
The different between multiple regression and linear regression in the number of variables used to make a prediction. While linear regression uses one variable, multiple regressions uses more than one variable.
Some of the variables that can be predicted through this method include the effect that age has on the purchase of cakes in the business. This can have some effects on the business sales. One of effect is the increase in the sales of cakes for children between the ages of 5-18 years. Another factor that can be predicted and used in making business decision is data related to pricing. The sales can be analyzed in relation to cost of the different cakes.
There are a number of factors that the organization can use to make some of the most challenging managerial decisions. It is possible to make decision related to market competition since through multiple regression techniques; the company can be able to predict how the entry of other firms affects the market. This method has different advantages when used in a company. By using linear regression, which is the key formula, optimal estimates for parameters that are not known can be obtained. This shows the level of accuracy of this method over the other two methods, which are discriminative and conjoint analysis. The estimates used can be based on a broad class of parameters .The assumptions are used in developing the necessary models.
Management does not necessarily require to have huge amount of data to make its predictions and decisions. Small data sets can be used for making decisions unlike other methods of data analysis. This method also makes some major assumptions. One of the assumptions made is on the distribution of errors. It is assumed that errors in this method are normally distributed. The variance of error is zero and the errors are independent variables. This assumption means that mean error is therefore zero.
The flexibility of this method makes it suitable than the other two methods, the independent variable used can either be numeric or categorical. Most people are familiar with this statistical technique and employees will not face a difficulty adapting to it.The approaches that can be used include predicting one variable from a number of other known variables. It is also suitable when management wants to determine the variable that gives the best predictor when there are many variables involved. The prediction is also tested when another predictor variable is added in the approach (Cooley, W. W., & Lohnes, P. R, 2001)
Real Life Company Using Multiple Regression
Magic food is one leading company in that uses multiple regression in its business and managerial operations. This company is in the same line of business as our company and has achieved immense growth that is attributed to multiple regression technique in management. The company started using this method after facing some challenges in its operations. One of the main challenges that it faced before implementing this method was identifying the factors that affect sales and how to predict future sales. Through application of multiple regression in data analysis, it was able to tackle the challenge effectively. Most challenges that affected future sales predictions were able to be solved effectively by the management. It proved to be a simple method to implement although the company had hired a private firm to do the analysis.
Application in the Organisation
This organization can effectively apply the technique and achieve business growth. The marketing, sales and production departments need to apply this technique in ensuring that the business grows and cope with market competition. One of the key areas that it can be applied is in the sales department. It is possible to identify and predict factors that affect sales. Once they have been identified, it will be possible to increase the sales and counter market competition. Some of the factors maybe change in customer taste or even the price of commodity. The production department through making its predictions on the factors that affect production in this organization. Once they have been identified, the production process can be streamlined to ensure that the production process is in line with the business operations. Just like in Magic Food Company, it will be possible to predict future sales in the organisations.Once this is done, the management will be able to predict business growth and achieving its business goals.
There is need for a proper analysis technique. The best option for this organization would be multiple regressions. It offers the best solution in making best analysis decisions in all the departments of the organization.
Work Cited
Berry, W. D., & Feldman, S. (1999). Multiple regression in practice. Beverly Hills: Sage Publications.
Cooley, W. W., & Lohnes, P. R. (2001). Multivariate data analysis. New York: Wiley.
