The test yielded a t-value of 1.592 with a corresponding p-value of 0.059. Which of the following is the correct interpretation of the p-value?
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.
A popular musician believes an increase in the number of times songs are listened to via a streaming service leads to an increase in recording sales. The musician’s recording company selected 50 songs at random and used the data to test the claim that there is a positive linear relationship between the number of times a song is listened to and recording sales. The following hypotheses were used to test the claim.
H0:β1=0
Ha:β1>0
The test yielded a t-value of 1.592 with a corresponding p-value of 0.059. Which of the following is the correct interpretation of the p-value?
A. If the alternative hypothesis is true, the
B. If the alternative hypothesis is true, the probability of observing a test statistic of 1.592 or greater is 0.059.
C. If the null hypothesis is true, the probability of observing a test statistic of 1.592 or greater is 0.059.
D. If the null hypothesis is true, the probability of observing a test statistic of 1.592 is 0.059.
E. If the null hypothesis is true, the probability of observing a test statistic of 1.592 or smaller is 0.059.
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