he accompanying table shows a portion of data consisting of the selling price, the age, and the mileage for 20 used sedans.   Selling Price Age Miles 13,636 8 61,500 13,783 7 54,361 ⋮ ⋮ ⋮ 11,948 7 42,368        Click here for the Excel Data File a. Determine the sample regression equation that enables us to predict the price of a sedan on the basis of its age and mileage. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) [If you are using R to obtain the output, then first enter the following command at the prompt: options(scipen=10). This will ensure that the output is not in scientific notation.]     PriceˆPrice^  =  +  Age +  Miles. b. Interpret the slope coefficient of Age. multiple choice The slope coefficient of Age is −824.18, which suggests that for every additional year of age, the predicted price of car decreases by $824.18. The slope coefficient of Age is −0.08, which suggests that for every additional year of age, the predicted price of car decreases by $0.08. The slope coefficient of Age is −824.18, which suggests that for every additional year of age, the predicted price of car decreases by $824.18, holding number of miles constant. The slope coefficient of Age is −0.08, which suggests that for every additional year of age, the predicted price of car decreases by $0.08, holding number of miles constant. c. Predict the selling price of a seven-year-old sedan with 63,000 miles. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.)     PriceˆPrice^ = $

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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.