Excellent Custom «Market Research: Statistical Analysis and Minitab» Free Essay

«Market Research: Statistical Analysis and Minitab»


Market research offers businesses valuable information and analyses that can serve as a foundation, on which the companies can build their expansion strategies and pursue opportunities for growth. One example of a business based in the United Kingdom that would like to expand is Entertainment Company Film 2011. The company is considering opening chain of cinemas and has launched thorough market research in order to evaluate expansion opportunities. In order to obtain reliable and credible market evaluation, Film 2011 hired group of business consultants from Nelson Market Research Company. Nelson Market Research performed analysis of the company’s internal and external environments (SWOT and PESTEL) as well as the industry’s competitive environment (Five Forces analysis). In order to enhance the appraisal of competitive environment, Nelson Market Research obtained the feedback from 128 middle managers of three competing cinema chains (A, B, and C) that operate across the UK in England, Scotland, and Wales.

Following are the market research objectives:

  1. To determine whether spending on advertising is justified and whether advertising costs impact sales.
  2. To identify and appraise the disparities between sales during working days and weekends.
  3. To subject gathered quantitative data to statistical analysis to answer questions of aforementioned objectives.
  4. To suggest the optimal profile of a cinema facility for the new cinema chain.

Methodology of Statistical Analysis via Minitab

Consultants of Nelson Market Research analyzed the quantitative data from competitors’ management feedback via statistical software Minitab 16. Beyman and Cramer (2003) argue that Minitab 16 is tool that can be used to facilitate and greatly enhance market research. For this purpose, descriptive and statistical analyses have been conducted. Therefore, the outcome of the market research will be multivariate statistical analysis that can be applied in market studies (Hardle & Simar, 2012). The data analysis was followed by the final report. While performing the analysis of three competing chains the following variables were taken into consideration: regional locations of cinema theaters and their location in towns, size of cinemas, gender of a manager, sales on different days, total sales, advertizing expenditures, rent (See Appendix A, Table 1). Corresponding sales and costs were appraised on a monthly basis.

Descriptive statistical analysis allowed determining such parameters of the collected data as standard deviation, variance, mean, etc. Additionally, skewness was calculated to find out the distribution’s degree of symmetry. Furthermore, box-plots that reflect the distribution of quantitative variables and histogram were plotted, as well. Lastly, correlation analysis and regression were used to determine association between advertisement costs expenditure and total sales per month. Presented equation was employed to calculate the relationship between dependent and independent variables: Y = f X.

In the performed analysis, Y is total sales per month where variable Y is dependent on variable X (cost of advertisement). The scatter diagram can be employed to depict the relationship of variables Y and X (Hardle & Simar, 2012). When the scatter line goes upward from left to right, it demonstrates positive association between the variables Y and X. When the line goes downward in the same direction it indicates that there is no relationship between the dependent variable Y and independent variable X (Hardle & Simar, 2012).

Next task is to determine R, the correlation coefficient that shows the intensity of relationship between two variables (Y and X). The value of R is between -1 and +1. Strong positive relationship shows direct association and vice versa while negative relationship indicates that there is no direct association between variables. Lastly, R2, regression coefficient, was calculated to determine how intensive the association between variables Y and X was.

Statistical Analysis

The Table 2 shows that the three competing cinema chains are fairly evenly distributed. However, Scotland has the highest number of cinemas; Ireland follows the Scotland closely, while Wales has the lowest number of cinemas (See Appendix A, Table 2 and Appendix B, Figure 1).

Moreover, 53.91% of cinemas are located in town, while the remaining percentage is out of the town. Lastly, the table shows that most of the cinemas are medium in size with manager’s position occupied mostly by males.

Table 3 shows that the advertisement cost is £2285.5, and rent of cinema facility is £2032 per month. Furthermore, on the monthly basis, the working day sale is £90372 while the weekend sale is £157020 (See Appendix A, Table 3). Thus, it is evident that on a monthly basis, sales during working days are significantly lower than sales during weekends.

 Benefit from Our Service: Save 25% Along with the first order offer - 15% discount, you save extra 10% since we provide 300 words/page instead of 275 words/page

Cross tabulation shows that out of 128 cinemas 69 are in the town, while the rest are out of the tow meaning that there are more viewers in the town (See Appendix A, Table 4).

The comparison and analysis of tables shows that although the large cinema facilities account for the highest sales per month, medium size cinema facilities remain more profitable due to maintaining lower operation expenditures.

Figure 2 (See Appendix B) shows the existence of positive relationship between the variables Y and X (monthly sales and advertising expenditures) as the scatter line is going up from left side to the right side. R (correlation coefficient) between total sales and advertisement cost per month equals 0.854. This quantitative indicator shows a positive association between the two variables. Subsequently, the increase in advertisement costs drives the sales up.

Regression Equation:

160047 + 178 Advertisement Costs per month = Total Sales per month

R2 (regression coefficient) equals 72.9% and shows strong relationship between two variables.

Figure 3 (See Appendix B) shows that at 95% confidence interval, one cannot expect to achieve sales of £285000 per month. It cannot be accepted since the confidence interval lies between £222823 and £271960.



Our Customers' Testimonials

Current status


Preparing Orders


Active Writers


Support Agents

Order your 1st paper and get discount Use code first15
We are online - chat with us!