The only disadvantage of a global variable is that you cannot directly encapsulate them. Other than that, there is no real disadvantage of global variables.
Advantages: you can see an exact number. Disadvantages: you cannot see the changes between intervals.
ADVANTAGES Shows relationship between two variables best method to illustrate a non-linear pattern.
Every time the independent variables change, the dependent variables change.Dependent variables cannot change if the independent variables didn't change.
The substitution method for solving a system of equations is advantageous because it can be straightforward, especially when one equation is easily solvable for one variable, allowing for direct substitution. It can also provide clear insights into the relationships between variables. However, its disadvantages include the potential for increased complexity when dealing with more variables or complicated equations, and it may be less efficient than other methods, like elimination, for larger systems. Additionally, if the equations are not easily manipulated, it can lead to errors in calculation.
Regression analysis offers several advantages, including the ability to identify relationships between variables, make predictions, and quantify the strength of associations. However, it also has disadvantages, such as the assumption of linearity, which may not always hold true, and sensitivity to outliers, which can skew results. Additionally, regression models can become overly complex if too many variables are included, potentially leading to overfitting. Lastly, correlation does not imply causation, meaning that regression results must be interpreted cautiously.
Advantages: you can see an exact number. Disadvantages: you cannot see the changes between intervals.
advantages: its easier to figure out or look at exact numbers disadvantages: its harder to see the change between intervals
The answer: Advantages: you will be able to see the exact numbers. Disadvantages: you wont be able to see were they would go on a graph
disadvantage - less control over variables
Advantages: You can collect the correct data in words rather than in numbers. Disadvantages: Sometimes you can't find all the data when it is written.
ADVANTAGES Shows relationship between two variables best method to illustrate a non-linear pattern.
Not very good for a large number of categories. Not good for comparing two (or more) variables, especially if one is not consistently bigger than the other. Very poor for comparing a large number of variables.
ADVANTAGES Shows relationship between two variables best method to illustrate a non-linear pattern.
Multiple correlation assesses the strength of the relationship between one dependent variable and several independent variables. Advantages include its ability to provide a comprehensive understanding of how multiple factors collectively influence an outcome, and it can help identify key predictors. However, disadvantages include the potential for multicollinearity, where independent variables are highly correlated with each other, which can distort results, and the complexity of interpretation increases with the number of variables involved. Additionally, it may overfit the model if too many variables are included without proper validation.
The advantage is knowing what you are writing about, and why. The disadvantage is not doing the research,and not knowing what you are supposed to be writing about.
Advantages: • relationships amongst quantifiable variables of decision-making strategies Enhance accuracy level while analyzing the data ** Disadvantages: • Broad the study, can be increased the number of samples from population.
Causal comparative research, while useful for exploring relationships between variables, has several disadvantages. One major limitation is its inability to establish causation definitively, as it does not involve random assignment and can be influenced by confounding variables. Additionally, the reliance on pre-existing groups can lead to selection bias, affecting the validity of the findings. Lastly, the retrospective nature of many causal comparative studies may limit the control over variables, making it challenging to draw clear conclusions.