NUMBER OF TOURISTS (IN MILLIONS) YEAR RIDERSHIP (SUMMER MONTHS) (IN MILLIONS) 1 7 1.5 2 2 1.0 3 6 1.3 4 4 1.5 5 14 2.5 6 15 2.7 7 16 2.4 8 12 2.0 14 2.7 10 20 4.4 11 15 3.4 12 1.7
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.
Bus and subway ridership for the summer months in
London, England, is believed to be tied heavily to the number of
tourists visiting the city. During the past 12 years, the data on the
next page have been obtained:
a) Plot these data and decide if a linear model is reasonable.
b) Develop a regression relationship.
c) What is expected ridership if 10 million tourists visit London
in a year?
d) Explain the predicted ridership if there are no tourists at all.
e) What is the standard error of the estimate?
f) What is the model’s
determination?
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