Arandom sample of two variables, x and y, produced the observations shown to the right 10 19 10 a. Develop a scatter plot for the two variables and describe what relationship, if any, exists b Compute the correlation coefficient for these sample data. c. Test to detemine whether the population corelation coefficient is positive. Use a significance level of 0.01 for the hypothesis test 13 7. 16 17 11 6 20 11 15 8 O Click the icon to view the t-distribution table.
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.


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