We would like to estimate a relationship between the weight of a car (weight) and the miles-per-gallon rating (mpg). lmpg - the natural logarithm of mpg lweight - the natural logarithm of weight Three models are estimated. Based on the regression outputs, do you prefer MODEL 1 or MODEL 2? 1.Linear model is preferred because its R-Squared is higher compared to log-linear model. 2.Not enough information to answer the question. 3.Log-linear model is preferred because its R-Squared is higher compared to linear model. 4.Log-linear model is preferred as SER is lower compared to linear model.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
We would like to estimate a relationship between the weight of a car (weight) and the miles-per-gallon rating (mpg).
lmpg - the natural logarithm of mpg
lweight - the natural logarithm of weight
Three models are estimated.
Based on the regression outputs, do you prefer MODEL 1 or MODEL 2?
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