Exercise 3.2 Lifetime of Drill bits Drill Y = Lifetime (in thousands of holes drilled), X = Drill speed Replacing parts in manufacturing has two costs: The price of the part and lost production time while the part is installed. Here, the manufacturer wanted to know if the rotation speed had an impact on the lifetime of drill bits. (b) Is a linear model appropriate ? Discuss. (c) Is there an optimal Drill speed ? Explain. (d) How would you explain the results to the manufacturer. (e) Would fitting a line be helpful ? Explain.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Exercise 3.2 Lifetime of Drill bits
Drill Y = Lifetime (in thousands of holes drilled), X = Drill speed
Replacing parts in manufacturing has two costs:
The price of the part and lost production time while the part is installed.
Here, the manufacturer wanted to know if the rotation speed had an impact
on the lifetime of drill bits.
(b) Is a linear model appropriate ? Discuss.
(c) Is there an optimal Drill speed ? Explain.
(d) How would you explain the results to the manufacturer.
(e) Would fitting a line be helpful ? Explain.

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