Objective We aimed to estimate prevalence and identify determinants of hypertension in adults aged 15–49 years in Tanzania. Design We analysed cross-sectional survey data from the 2022 Tanzania ...

VIF measures the strength of the correlation between the independent variables in regression analysis. This correlation is known as multicollinearity, which can cause problems for regression models.

Understanding the linear relationship between two numerical variables is essential for effective data analysis. Pearson’s correlation coefficient (r) measures the strength and direction of an ...

A closely related method is Pearson’s correlation coefficient, which also uses a regression line through the data points on a scatter plot to summarize the strength of an association between two ...

The results of first analysis shows that it is good to classify the data according to UCS values. In the second stage, simple and multiple linear regression analyses were performed for estimating the ...

The variance explained by two factors constitutes 88.80% of the total variance. The regression equation was statistically significant (p<0.01). In the regression equation, two factors obtained by ...

Using the FORECAST Function In the past, it was necessary to create an equation to make forecasts from regression analysis. Seldom were these equations easy to calculate or as simple as x=y2.

Regression analysis: This can help determine the relative importance of multiple factors simultaneously. Decision trees: These can capture non-linear relationships and interactions between factors.

4. Conclusion This article analyzes their per capita GDP data and urbanization rate in mainland of China and finds that there is a strong correlation between the two. It can be seen that the growth of ...

Correlation vs Regression: Both correlation and regression are two powerful tools of statistics and data analysis used to understand the relationships between variables. However, they serve ...