7) Season Tickets Sold 8) 100 80 60 + 40 20 2000 2004 2006 2008 1998 2002 2010 Vehicle Weight Year a) Pos./ Neg./Cluster/N.A b) Linear/Non-Linear )Strong/Weak a) Pos./ Neg./Cluster/N.A b)Linear/Non-Linear c)Strong/Weak Percentage of Season Tickets Sold Miles Per Gallon
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.
![7)
Season Tickets Sold
8)
100
80
60
40
20 -
++
2000
2004
2008
1998
2002
2006
2010
Vehicle Weight
Year
a) Pos./ Neg./Cluster/N.A
b) Linear/Non-Linear
c)Strong/Weak
a) Pos./ Neg./Cluster/N.A
b)Linear/Non-Linear
c)Strong/Weak
Percentage of
Season Tickets Sold
Miles Per Gallon](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F14dae06c-6401-40ae-a779-566c10894681%2F6c8fe187-c05b-4f8c-b05f-f58795edfb99%2F80ipo6d_processed.jpeg&w=3840&q=75)
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