Anagha is interested in buying a new Model X car and wants to gather information about how the selling price of the car is related to the year of the model. She randomly selects 24 used Model X cars for sale. For each used car, she records the car’s selling price (in dollars) and age (in years). She computes a 96 percent confidence interval to estimate the slope of the regression line relating the age of a used Model X car to its selling price. The resulting confidence interval is given by (−5,556,−3,157). Assume that the conditions for inference on the slope of the regression equation are met. Which of the following is the correct interpretation of the confidence interval? We are 96 percent confident that a Model X car will have a predicted decrease in selling price of between $3,157 and $5,556. A Ninety-six percent of the time, a one-year increase in the age of a Model X car will result in a predicted decrease in selling price of between $3,157 and $5,556. B Ninety-six percent of samples of 24 used Model X cars will have an average selling price that is between $3,157 and $5,556 less than the selling price of a new Model X. C We are 96 percent confident that any sample of 24 Model X cars will produce a slope of the regression line of between −5,556 and −3,157. D We are 96 percent confident that a one-year increase in the age of a Model X car will result in a predicted decrease in the selling price of between $3,157 and $5,556. E
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.
Anagha is interested in buying a new Model X car and wants to gather information about how the selling price of the car is related to the year of the model. She randomly selects 24 used Model X cars for sale. For each used car, she records the car’s selling price (in dollars) and age (in years). She computes a 96 percent confidence
Assume that the conditions for inference on the slope of the regression equation are met. Which of the following is the correct interpretation of the confidence interval?
-
We are 96 percent confident that a Model X car will have a predicted decrease in selling price of between $3,157 and $5,556.
A -
Ninety-six percent of the time, a one-year increase in the age of a Model X car will result in a predicted decrease in selling price of between $3,157 and $5,556.
B -
Ninety-six percent of samples of 24 used Model X cars will have an average selling price that is between $3,157 and $5,556 less than the selling price of a new Model X.
C -
We are 96 percent confident that any sample of 24 Model X cars will produce a slope of the regression line of between −5,556 and −3,157.
D -
We are 96 percent confident that a one-year increase in the age of a Model X car will result in a predicted decrease in the selling price of between $3,157 and $5,556.
E
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