The linear regression method seeks to predict values of a(n) dependent variable based on values of a(n) independent variable.
According to the statement
we have to explain the linear regression method and explain the way by which this method is used to predict the values.
So, For this purpose we know that the
Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship.
And
Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.
from these definitions it is clear that the there is a presence of two types of variables which are dependent and independent variables.
So, The linear regression method seeks to predict values of a(n) dependent variable based on values of a(n) independent variable.
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Answer:
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Step-by-step explanation:
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5x + 55 = 2x + 100
3x = 45
x = 15
plug 15 into
5(15) + 55
75 + 55 = 130
130 + angle 2 = 180
angle 2 = 50
Normal or natural variations in the quality of production output that are due purely to chance are common causes.
According to the statement
we have to tell about the that cause which effect the quality and production output in the normal and natural variation.
So, For this purpose, we know that the
Common cause variation is present when the control chart of a process measure shows a random pattern of variation with all points within the control limits. When a control chart shows common cause variation, a process measure is said to be in statistical control or stable.
And due to this cause there is a lot of effect on the natural variations in the quality of production output.
So, Normal or natural variations in the quality of production output that are due purely to chance are common causes.
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