Discarding the influential outlying cases when detected is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset
Step-by-step explanation:
In a regression class ; If extremely influential outlying cases are detected in a Data set, discarding this influential outlying cases is the right way to go about it
Discarding the influential outlying cases when detected is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset
The possible product can be 94. You need the sum of two prime numbers that is divisible by seven and is a perfect square. A number that is a perfect square and divisible by 7 is number 49 (7^2=49). A prime number is a number that is divisible only by 1 and itself. 49 = 47 + 2 (47 and 2 are prime) 47 × 2 = 94.
