Correlation and Regression Analysis
PART 1: Correlation analysis (Module 5 – TD1)
1a. Run a Pearson’s correlation matrix on the three continuous variables in your data set (titled “Multiple Stores.sav”)
1b. Upload as an attachment or copy and paste your SPSS results into the discussion.
1c. Describe results from your analyses based upon your statistical analysis.
1d. Explain how you would use correlation in your research project (Intellectual Property Rights (IPR) Laws are in a continuous state of evolution in the Emerging Markets)
PART 2: Regression analysis (Module 5 – TD2)
2a. Run a multiple regression analysis using Building Age and Return on Investment as predictor (Independent) variables and customer service score as the outcome (Dependent) variable. (data set titled “Multiple Stores.sav”)
2b. Upload as an attachment or copy and paste your relevant SPSS results into the discussion.
2c. Describe results from your analyses based upon your statistical analysis.
2d. Explain how you might use regression in your doctoral research project (Intellectual Property Rights (IPR) Laws are in a continuous state of evolution in the Emerging Markets)
Correlation and Regression Analysis
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Correlation and Regression Analysis Part 1: Correlation Analysis A correlation analysis was undertaken using SPSS using the three continuous variables in the data set that included building age, return on investment, and customer satisfaction. The aim of Pearson’s correlation test was to determine the direction and strength of relationships between the variables (Mathematics and Statistics, 2020). The results are shown in table 1 below.Table 1: Person’s Correlation Bldg. Age Return on Investment Customer SatisfactionBldg. Age Pearson Correlation 1 -.193** .274** Sig. (2-tailed) .000 .000 N 869 869 869Return on Investment Pearson Correlation -.193** 1 .637** Sig. (2-tailed) .000 .000 N 869 869 869Customer Satisfaction Pearson Correlation .274** .637** 1 Sig. (2-tailed) .000 .000 N 869 869 869**. Correlation is significant at the 0.01 level (2-tailed).
The results show that there was a small and negative correlation between building age and return on investment that was statistically significant (r = -.193, n = 869, p = 0.05) (Field). Additionally, there was a small positive correlation between building age and customer satisfaction which was statistically significant (r = .274, n = 869, p = 0.05). Return on investment and customer satisfaction showed a large and positive correlation which was also statistically significant (r = .637, n = 869, p = 0.05). In my research project, correlation analysis could be undertaken to determine whether the presence and strengths of any relationships between the different factors that could influence the evolution of IPR laws.Part 2: Regression Analysis A regression analysis was undertaken in SPSS using customer satisfaction as the dependent variable and building age and return on investment as the independent variables. The model summary, as shown in table 2 below, indicates the R-value (R = .755), which indicates a good level of prediction of the dependent variable (Rochi, 2020). The R Square value (...
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