Step-by-step explanation:
120/20=6g
50/20=2.5g
170/20=8.5g
for one shortbread ^^
6*50=300g
2.5*50=125g
8.5*50=425g
for 50 shortbread ^^
Answer:
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
Answer:
-1/4 or -0.25
Step-by-step explanation:
Answer:
The answer is J
Step-by-step explanation: