exam 2.0

PR
What is the importance of presenting the data in graphical or tabular representation?
	The importance of presenting the data in graphical or tabular representation is to get the attention of the readers or audience. Also, if you're looking for an easy-to-understand presentation of enormous amounts of complex data that also keeps the attention of its readers, then data visualization is an excellent choice. Additionally, it is also possible to quickly analyze a vast quantity of data using graphical or tabular representation, which can aid in formulating predictions and knowledgeable decisions. Data visualizations can dramatically improve collaboration by utilizing familiar visual metaphors to demonstrate relationships and highlight meaning, minimizing complex, drawn-out explanations of an otherwise chaotic-looking set of data. After all, everything that’s pleasing to our eyes never fails to grab our attention.

True or False: Contingency tables are not necessary for bivariate analysis. Explain your answer
	False, contingency tables are necessary for bivariate analysis. Contingency tables are used in statistics to show the relationship between the variety of different categorical variables. This type of frequency distribution table is known as a contingency table because it shows the frequency distribution of two variables simultaneously. Also, two variables can be analyzed simultaneously using bivariate statistics. Furthermore, contingency tables demonstrate how many observations are recorded when two categorical variables have different values. This allows us to see how the values of an outcome variable are influenced by the categories of a variable. And the bivariate analysis is an essential step in describing the relationships between observed variables.

What does the statement “variables are independent of each other” imply?
	The statement “variables are independent of each other” implies that the variables can stand alone and it does not depend on other variables. There are no other variables that might influence the measurement. Also, when there is something independent, there will sometimes be a dependent. Furthermore, it is a random variable that has no influence on the other random variables. Nevertheless, it does not alter the probability of another occurrence happening. Sometimes changing the independent variable might affect the dependent variables. It's possible for researchers to find that changes in the independent variables have no impact on the variables that are being measured.