Both matrix and array data structures in R can only contain one data type across all their elements, ensuring consistency in the structure.

Data conditioning in the data preparation phase refers to the process of cleaning the data, normalizing datasets, and performing transformations to ensure the data is in a suitable format for analysis or modeling.

The Hadoop Distributed File System (HDFS) is a master/slave system designed to run on commodity hardware, meaning it can efficiently store large volumes of data across inexpensive, off-the-shelf hardware.

The GROUP BY clause in the SQL statement implies the use of an aggregate function. In this case, avg(prod_cost) is an aggregate function that calculates the average of the product costs for each group of products.

The Discovery phase involves understanding the business problem by conducting project sponsor interviews, gathering requirements, and drafting a problem statement to define the analytic objectives clearly.

A graph of the WSS (Within-Cluster Sum of Squares) versus the value of K helps determine the optimal number of clusters. The "elbow" point on the graph represents the value of K at which adding more clusters no longer significantly improves the WSS.

In a user-defined aggregate function (UDAF), FFUNC refers to the optional final calculation function. It is used to perform any final calculation or transformation on the aggregated result before it is returned to the user.

In ANOVA (Analysis of Variance), the null hypothesis is that all population means are equal to each other. The goal is to test whether there is evidence to reject this hypothesis, indicating that at least one population mean differs from the others.

The decision tree having 17 layers and a leaf node with only three customers suggests that the tree is overfitting the data. This is because a deep tree with many layers can lead to overfitting, where the model fits the training data too closely, capturing noise and small details that do not generalize well to new data. Reducing the tree depth or pruning the tree can help mitigate overfitting.

Spark's in-memory data processing avoids the need to write intermediate data to disk, which significantly speeds up processing compared to MapReduce, which writes intermediate data to disk after each operation. This makes Spark much faster for iterative tasks.

The project goals component of a final presentation provides a succinct overview of the business situation that led to the initiation of the project. It sets the context for why the project was undertaken and what it aimed to achieve.

P(Milk∩Eggs) is the probability of both Milk and Eggs being bought together, which is the count for Milk and Eggs (110).

P(Milk)) is the probability of Milk being bought, which is the total for Milk (233) divided by the grand total (374).

P(Eggs) is the probability of Eggs being bought, which is the total for Eggs (150) divided by the grand total (374).

The value of the lift for the association rule Milk -> Eggs is approximately 1.18.

Lift measures how many times more often X and Y occur together than would be expected if they were statistically independent. It is expressed as a ratio, and a lift greater than 1 indicates a positive association between X and Y.

A Type I error occurs when the null hypothesis is rejected even though it is actually true. This is also known as a "false positive" in hypothesis testing.