Listed below are amounts of court income and salaries paid to the town justices. All amounts are in thousands of dollars. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between court incomes and justice salaries? Based on the results, does it appear that justices might profit by levying larger fines? Court Income Justice Salary 65.0 403.0 1568.01132.0 271.0 252.0 112.0 150.0 34.0 e 46 31 42 91 57 60 24 25 |17 What are the null and alternative hypotheses? O A. Ho: p=0 'B. Ho: p=0 H:p>0 OC. Ho: p=0 O D. Ho: p#0 H:p=0 H,ip<0 Construct a scatterplot. Choose the correct graph below. OA. OB. Oc. D. AJustice Salary 100- AJustice Salary 100- AJustice Salary 100- Q AJustice Salary 100- 50- 50- 50 . 50- -- > 800 800 1600 Court Income 800 1600 1600 800 1600 Court Income Court Income Court Income The linear correlation coefficient is r= (Round to three decimal places as needed.)
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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