1400 1200 1000 800 600 400 200 20 30 40 50 60 70 80 Income (thousands of dollars) The correlation between Total Yearly Purchases and Income is 0.722. Summary statistics for the two variables are: Mean SD $50,343.40 $16,952.50 $572.52 Income Total Yearly Purchase $253.62 Total Yearly Purchases (dollars)
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.
cussed in the previous problem, the
is 0.722. Summary statistics for the two variables are:
Total Yearly Purchase from Income?
pear to be met?
for someone with a yearly Income of $20,000? For
someone with an annual Income of $80,000?
d) What percent of the variability in Total Yearly
Purchases is accounted for by this model?
e) Do you think the regression might be a useful one for
the company? Comment.
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