Is Your Linear Regression Analysis of Electric Car Energy Consumption Accurate?

In summary: Your Name]In summary, the speaker has performed a linear regression analysis of the energy consumption of an electric car using 7 measured parameters. They have narrowed down the original 25 parameters through investigating relationships and using regression techniques. The output does not show any major issues, but the speaker may want to consider providing context for the analysis, including correlation coefficients, and assessing assumptions of linear regression. Overall, the speaker has a good understanding of regression techniques and is seeking feedback on their analysis.
  • #1
bradyj7
122
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Hi there,

I have performed a linear regression of the energy consumption of an electric car on 7 measured parameters. They are

1. Max acceleration
2. Average Acceleration
3. Average Velocity
4. Distance travelled
5. Standard Deviation Acceleration
6. Standard Deviation Velocity
7. Max power required

There originally was 25 parameters but, I narrowed it down to these 7 by investigating the relationships between the variables and using stepwise and best subsets regression. This is the simplest equation that I could get. I think that it is okay but I was just wondering if somebody could have a quick look at the output and see if anything major is wrong with it.

Here is the output

https://dl.dropbox.com/u/54057365/All/regression%20output.JPG

Thank you

john
 
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  • #2


Hello John,

Thank you for sharing your linear regression analysis. It looks like you have done a thorough job in narrowing down the parameters and using regression techniques to determine the best subset of variables. Your output does not show any major red flags, but there are a few things that you may want to consider.

Firstly, it would be helpful to provide some context for your analysis. What is the purpose of this regression? What is the dependent variable (energy consumption) and what are the independent variables (the 7 parameters)? This information will help to better understand the results and their implications.

Additionally, it would be useful to see the correlation coefficients for each of the independent variables with the dependent variable. This would provide insight into the strength and direction of the relationships between the variables.

Lastly, it may be beneficial to assess the assumptions of linear regression, such as normality and linearity, to ensure that they are met. This can help to validate the results and ensure the accuracy of the model.

Overall, it seems that you have put a lot of effort into your analysis and have a solid understanding of regression techniques. Keep up the good work and don't hesitate to seek out further guidance or feedback. Best of luck with your research!


 

Related to Is Your Linear Regression Analysis of Electric Car Energy Consumption Accurate?

What is regression analysis?

Regression analysis is a statistical method used to study the relationship between a dependent variable (or outcome) and one or more independent variables (or predictors). It is used to predict the value of the dependent variable based on the values of the independent variables.

What is the purpose of interpreting regression output?

The purpose of interpreting regression output is to understand the relationship between the variables and to make conclusions about the impact of the independent variables on the dependent variable. It allows us to determine the strength, direction, and significance of the relationship between the variables.

What is the significance of the regression coefficients?

The regression coefficients represent the change in the dependent variable for every one unit change in the independent variable. They indicate the direction and strength of the relationship between the variables. A positive coefficient means that there is a positive relationship, while a negative coefficient means there is a negative relationship.

What is the difference between R-squared and p-value?

R-squared (or coefficient of determination) is a measure of how well the regression model fits the data. It represents the percentage of the variation in the dependent variable that can be explained by the independent variables. On the other hand, the p-value represents the probability of obtaining the observed results by chance. A p-value less than 0.05 is considered statistically significant.

How do you use regression output to make predictions?

To make predictions using regression output, we can plug in the values of the independent variables into the regression equation and solve for the dependent variable. Alternatively, we can use the regression coefficients to calculate the predicted values. It is important to note that predictions should only be made within the range of the data used to create the regression model.

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