I need help solving this problem. From the information given below, please provide the following:

1. Discuss any limitations of the study such as small sample size, presence of outliers, assumptions not fully met, and other pertinent information of the study. Please respond in paragraph format and APA guidelines. Thank you.

2. Provide a conclusion paragraph bringing together the findings and writing up the results in APA format. Include:

• The regression equation
• The significance of the model and its R value
• Key results from the correlationand regression analyses
• Implications of the relationship between stress and doctor visits

Correlation Analysis

A Pearson correlation was conducted to explore the relationship between the number of visits to health professionals (timedrs) and stressful life events (stress) among participants. The results indicated that there was a statistically significant positive correlation between stress and the number of visits to health professionals, r(451) = .294, p < .001. This suggests that higher stress levels are associated with a higher number of visits to health professionals. A total of 453 participants were included in the analysis.

The correlation coefficient of 0.294 suggests that the association falls in the "weak" correlation range. However, it is very close to the moderate threshold, so it may be interpreted as a borderline weak-to-moderate association. The p-value is less than .001, which means the correlation is statistically significant. This implies that the observed relationship is unlikely due to chance.

The direction of the correlation is positive, meaning that as the score for stressful life events increases, the number of visits to health professionals tends to increase as well. This relationship implies that individuals experiencing more stressful life events may be more likely to seek healthcare services.

Regression Analysis

A simple linear regression was conducted to determine whether stress levels predicted the number of doctor visits. The model was statistically significant, F(1, 451) = 42.60, p < .001, and explained 8.64% of the variance in doctor visits (R = .086).

The regression equation was: timedrs = 3.30 + 0.02 stress

Both the intercept (b = 3.30, SE = 0.61, t = 5.42, p < .001) and the slope for stress (b = 0.02, SE = 0.003, t = 6.53, p < .001) were statistically significant. The intercept suggests that individuals with zero stress are predicted to visit the doctor approximately 3.3 times. The slope indicates that each one-unit increase in stress level is associated with a 0.02 increase in the predicted number of doctor visits.

Assumption Checks

The scatterplot shows the relationship between stress (IV) and timedrs (DV). There appears to be a slight positive trend, indicating a potential linear relationship. However, the points are quite dispersed, suggesting a weak correlation.

Although the relationship between stress and doctor visits is statistically significant, the effect size is small Additionally, the Q-Q plot for the residuals indicates mild deviation from normality. The points mostly follow the line in the middle but deviate significantly at the tails. This suggests some skewness and potential outliers affecting the normality assumption.

Comparing Correlation and Regression

The correlation coefficient (r = 0.294) indicates a weak positive relationship between stress and timedrs. It is independent of the units or scale of variables, ranging from -1 to 1.

The regression slope (b) indicates that for each unit increase in stress, the timedrs increases by 0.0166 units. This value maintains the units of timedrs and depends on the scales of the variables.

Explanation of the Difference

The correlation coefficient provides a standardized measure of how well stress predicts timedrs without regarding the scale. The value of 0.294 suggests a weak positive correlation.

The regression slope shows the actual change in timedrs for a one-unit increase in stress. For this study, it means that as stress increases, the number of doctor visits tends to slightly increase.