adjusted R-squared shows that this regression is preferable to the previous one (0.74 versus 0.65). Figure 4: Residual plot of the regression of company revenue on Openness to Experience, Conscientiousness, Extraversion, Agreeableness and Neuroticism The residual plot shows a similar pattern to the residual plot of the previous regression. Therefore, the interpretation stays the same: since there are two sets which appear to be following a clear pattern, means that the sample does not suffice the condition of normality. A possible way to improve the normality is to take job tenure into account, which will be done in the next model: 4.3.6 Model 6 Regression Statistics Multiple R - correlation coefficient 0,911 R-square 0,830 Adjusted R-square 0,717 Standard 83765,919 Observations 16 Coefficients Standard Error t Stat Intercept 11120538,04 936427,159 11,875 0.000 Job Tenure 1034,799 3471,202 0,298 0,772 Openness to Experience -5265,119 66072,654 -0,080 0,938 Conscientiousness 118288,100 310786,357 0,380 0,712 Extraversion -40233,210 57451,315 -0,700 0,501 Agreeableness 7623,277 312854,488 0,024 0,981 Neuroticism 435663,151 224621,994 1,940 0,084 Table 11: Model 6, regression of company revenue on the Big Five and job tenure This regression model has a lower adjusted square, which means that the previous model is better at explaining the different revenues. Therefore, I will not elaborate further on this specific model. Another way for Openness to Experience to influence company revenue could be through interactions with other variables. Because of the large amount of variables, I will only present the variables that generated an adjusted R-square that was higher than the fifth model:
adjusted R-squared shows that this regression is preferable to the previous one (0.74 versus 0.65). Figure 4: Residual plot of the regression of company revenue on Openness to Experience, Conscientiousness, Extraversion, Agreeableness and Neuroticism The residual plot shows a similar pattern to the residual plot of the previous regression. Therefore, the interpretation stays the same: since there are two sets which appear to be following a clear pattern, means that the sample does not suffice the condition of normality. A possible way to improve the normality is to take job tenure into account, which will be done in the next model: 4.3.6 Model 6 Regression Statistics Multiple R - correlation coefficient 0,911 R-square 0,830 Adjusted R-square 0,717 Standard 83765,919 Observations 16 Coefficients Standard Error t Stat Intercept 11120538,04 936427,159 11,875 0.000 Job Tenure 1034,799 3471,202 0,298 0,772 Openness to Experience -5265,119 66072,654 -0,080 0,938 Conscientiousness 118288,100 310786,357 0,380 0,712 Extraversion -40233,210 57451,315 -0,700 0,501 Agreeableness 7623,277 312854,488 0,024 0,981 Neuroticism 435663,151 224621,994 1,940 0,084 Table 11: Model 6, regression of company revenue on the Big Five and job tenure This regression model has a lower adjusted square, which means that the previous model is better at explaining the different revenues. Therefore, I will not elaborate further on this specific model. Another way for Openness to Experience to influence company revenue could be through interactions with other variables. Because of the large amount of variables, I will only present the variables that generated an adjusted R-square that was higher than the fifth model: