A random sample of 10 drivers insured with a company and having similar auto insurance policies was sclected. The following table lists their driving experiences (in years) and monthly auto insurance premiums. |Driving experience (X) 5 8 10 4 6 12 15 20 1 3 Auto Insurance Premium $ (Y) | 4 | 20 25 12 10 15 20 6 11 10 NB: Use two decimal places Considering a simple linear regression of the form Y = a + BX + e, where the dependent variable (Y) is insurance premium and independent variable (X) is driving experience; a) Use the least-squares method to compute the regression coefficients a and b. b) Write down the estimated equation and interpret the parameter estimates c) Determine the value of the extent of relationship between input (X) and output (Y), and interpret your result.
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.
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