A student (just like you) is researching car loans issued at a local bank. She gathered a sample of 200 to determine if there is a relationship between the loan amount, length of the loan, and interest rate. The regression results are in the table below. Which model is more suitable for prediction and what is the best fit reason? Variable Model 1 Model 2 Constant 114.325 110.54 0.000 0.000 Interest Rate 106.505 108.650 (0.000) (0.000) Loan Length 0.2074 0.3290 (0.000) (0.006) Interest × Loan NA −0.1430 (0.0005) Adjusted R2 0.2178 0.2089 Model 1 is the most suitable because of the higher adjusted R2 value. Model 2 is the most suitable because of the lower adjusted R2 value. Neither provide enough results data to predict the model or reasoning. Model 2 is the most suitable because of the p-value variance.
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.
QUESTION 5
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A student (just like you) is researching car loans issued at a local bank. She gathered a sample of 200 to determine if there is a relationship between the loan amount, length of the loan, and interest rate. The regression results are in the table below. Which model is more suitable for prediction and what is the best fit reason?
Variable Model 1 Model 2 Constant 114.325 110.54 0.000 0.000 Interest Rate 106.505 108.650 (0.000) (0.000) Loan Length 0.2074 0.3290 (0.000) (0.006) Interest × Loan NA −0.1430 (0.0005) Adjusted R2 0.2178 0.2089 Model 1 is the most suitable because of the higher adjusted R2 value.
Model 2 is the most suitable because of the lower adjusted R2 value.
Neither provide enough results data to predict the model or reasoning.
Model 2 is the most suitable because of the p-value variance.
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