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C) Company B: Using linear regression models from both data sets, she determines that it pays about $10 more.
What is linear regression?
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.
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Amelia should work for (C) company B: using linear regression models from both data sets, she determines that it pays about $10 more.
What is a linear model?
- The word linear model is used differently in statistics depending on the context.
- The most common usage is in relation to regression models, and the phrase is frequently used interchangeably with linear regression models.
- However, the phrase is also used with a different connotation in time series analysis.
- Linear regression analysis predicts the value of one variable depending on the value of another.
- The variable you wish to forecast is referred to as the dependent variable.
- The variable you are using to forecast the value of the other variable is known as the independent variable.
Therefore, Amelia should work for (C) company B: using linear regression models from both data sets, she determines that it pays about $10 more.
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The complete question is given below:
If Amelia wants to make the maximum amount of money working only 22 hours per week, which company should she work for? explain your answer.
A) Company A: Using linear regression models from both data sets, she determines that it pays about $14 more.
B) Company A: Using quadratic regression models from both data sets, she determines that it pays about $5 more.
C) Company B: Using linear regression models from both data sets, she determines that it pays about $10 more.
D) Company B: Using exponential regression models from both data sets, she determines that it pays about $8 more.
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