Solve simple regression problem

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In summary, the relationship between NBA players' salaries and average points per game can be represented by the equation Salary = 1 500 000 + 0.9log(points), with a sample size of 150 and an r^2 value of 0.14. Based on this equation, a bad player's salary would be approximately 1 500 000, as the lower their average points, the closer their salary is to this value. The coefficient in front of the aggressor indicates that an increase in points correlates with an increase in salary, but the coefficient is relatively small. Running the equation in log-log form would provide the advantage of calculating the elasticity, with a value of 0.9.
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Homework Statement


Salaries of NBA players related to average points per game:

Salary = 1 500 000 + 0.9log(points), n = 150, r^2 = 0.14

1) What is a bad players salary? 2) Interpret the coefficient in front of the aggressor 3) What would be the advantages of running it in log-log form?

Homework Equations



Salary = 1 500 000 + 0.9log(points), n = 150, r^2 = 0.14

The Attempt at a Solution



1) Approx 1 500 000. The lower the average points, the closer the salary is to 1 500 000
2) It is the right sign, as an increase in points means an increase in salary. But it is too small;
3) The main advantage is that the coefficient would give the elasticity; the elasticity would be 0.9.
 
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Thanks for the post! Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?
 

What is simple regression?

Simple regression is a statistical technique used to identify and quantify the relationship between two variables. It is commonly used to predict the value of one variable (dependent variable) based on the value of another variable (independent variable).

What is the purpose of simple regression?

The purpose of simple regression is to examine the relationship between two variables and determine if there is a significant association between them. It can also be used to make predictions about the value of the dependent variable based on the value of the independent variable.

How do you perform a simple regression analysis?

To perform a simple regression analysis, you will need to collect data on two variables and plot them on a scatter plot. Then, you can use statistical software or a calculator to calculate the regression line, which represents the best-fit line for the data. The slope and intercept of this line can then be used to make predictions about the dependent variable.

What is the difference between simple regression and multiple regression?

Simple regression involves examining the relationship between two variables, while multiple regression involves examining the relationship between a single dependent variable and multiple independent variables. In simple regression, there is only one independent variable, but in multiple regression, there can be several independent variables that may influence the dependent variable.

What is the importance of interpreting the regression line?

Interpreting the regression line is important because it allows us to understand the relationship between the two variables and make predictions about the value of the dependent variable. The slope of the regression line indicates the strength and direction of the relationship, while the intercept represents the predicted value of the dependent variable when the independent variable is equal to zero.

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