How to use Regression In real life

In summary, the speaker is asking for clarification on how to use the a and b values in a regression to determine how something is growing. They also ask for suggestions on where to find rules for interpreting the y-axis based on the x-axis. The response explains that this type of question is more related to basic function graphs, and mentions linear regression and using a formula like m=kL^3 to determine mass growth in relation to length growth. The summary also includes a note on the relationship between length and mass in a cube.
  • #1
Soley101
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Hi, I had made an inquiry about regression last week and am still working on understanding the whole concept.This week my question is different. How would I use the a and b values of the regression to indicate how something is growing. Like I'm having problems interpreting data because I do not yet know the rules I suppose. Like if I am doing a regression expressing the mass in terms of length so m=al^b then how can i describe how a certain something is growing based on the regression. Like is there a way to say if b is less then one then mass is growing slower then length etc. Where can I find these rules on how the y-axis will act based on the xaixs. Any suggestions?
 
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  • #2
Soley101 said:
Hi, I had made an inquiry about regression last week and am still working on understanding the whole concept.This week my question is different. How would I use the a and b values of the regression to indicate how something is growing. Like I'm having problems interpreting data because I do not yet know the rules I suppose. Like if I am doing a regression expressing the mass in terms of length so m=al^b then how can i describe how a certain something is growing based on the regression. Like is there a way to say if b is less then one then mass is growing slower then length etc. Where can I find these rules on how the y-axis will act based on the xaixs. Any suggestions?
Your question has less to do with regression, and is more related to the graphs of basic functions such as ##y = x, y = x^2,## and ##y = x^{1/2} = \sqrt x##.
A common type of regression is linear regression, which you would use if the variable quantities you're looking at grow at about the same rate. On the other hand, if you are trying to come up with a formula for the masses of a number of cubes that are L units on a side, you would try to fit your measured data to a formula like this: ##m = kL^3##, where k represents the density of the material.

For such a cube, if you double the length L, the mass will then be 8 times greater.

Here are a few graphs of some basic functions.
graphs.jpg
 

1. How is regression used in real life?

Regression is a statistical technique used to analyze the relationship between a dependent variable and one or more independent variables. In real life, regression is used to make predictions and understand the impact of various factors on a particular outcome.

2. What are some common applications of regression?

Regression is used in various fields such as economics, finance, healthcare, marketing, and social sciences. Some common applications include sales forecasting, risk analysis, market research, and medical research.

3. How do you interpret the results of a regression analysis?

The results of a regression analysis are typically presented in the form of a regression equation, which shows the relationship between the dependent variable and the independent variables. The coefficients in the equation indicate the magnitude and direction of the impact of each independent variable on the dependent variable.

4. What are the assumptions of regression?

There are several assumptions that must be met for a regression analysis to be valid. These include linearity, normality, homoscedasticity, and independence of errors. Violation of these assumptions can lead to biased results and inaccurate predictions.

5. How can I improve the accuracy of a regression model?

To improve the accuracy of a regression model, it is important to carefully select the variables to include in the analysis, ensure that the assumptions are met, and use appropriate techniques for handling outliers and missing data. Additionally, regularly evaluating and updating the model can help improve its predictive power.

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