A residual plot is a graph that shows the residuals on the vertical axis and the independent variable on the horizontal axis. It helps to identify non-linearity, unequal error variances (heteroscedasticity), and outliers. If the points in a residual plot are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate.

The main difference between simple and multiple linear regression is the number of independent variables. While simple linear regression uses only one independent variable to predict the dependent variable, multiple linear regression uses two or more independent variables to predict the dependent variable.

Multicollinearity refers to a situation in which two or more independent variables in a regression model are highly linearly related. This can lead to unstable estimates of the regression coefficients and make it difficult to assess the effect of independent variables on the dependent variable.

A discrete random variable is one which may take on only a countable number of distinct values and thus can be quantified. For example, you can count the change in your pocket. You can count the money in your bank account. You can count the number of heads in 50 coin tosses. These are all examples of discrete random variables.

When a distribution has a long tail on the right, it is said to be positively skewed or right-skewed. In a positively skewed distribution, the mean is typically greater than the median, which is greater than the mode.

A histogram is a graphical representation of the distribution of a dataset, typically used to visualize the frequency of data items in successive numerical intervals. The data range is divided into a series of intervals or 'bins' and the number of data points falling within each bin is represented by the height of a bar.

The Wilcoxon Signed Rank Test is appropriate when comparing the medians of two related groups.

Larger sample sizes make the Mann-Whitney U test more reliable. As with most statistical tests, a larger sample size increases the power of the test, which is the probability that it will correctly reject a false null hypothesis.

Commvault's integration of backup scheduling and policies ensures data integrity by maintaining the consistency and compliance of backup policies across the backup environment. This integration guarantees efficient backup operations and adherence to data protection standards.

Commvault's automated failover and failback capabilities enable seamless disaster recovery operations, minimizing downtime and ensuring business continuity. This is essential for companies prioritizing robust disaster recovery plans in their virtualized environments.

To optimize performance during peak usage hours, the company should implement a scale-out architecture, which involves adding more nodes or resources to the Commvault infrastructure to handle increased workload efficiently. Backup scheduling can help distribute the workload, but it may not directly address performance degradation. Bandwidth throttling and resource allocation are important but are not proactive measures specific to optimizing performance during peak usage hours.

The primary function of Commvault is data protection, which includes backup, recovery, and archiving of data. It ensures the safety and accessibility of data in various scenarios, such as system failures, data corruption, or accidental deletion.