a. Choose the oorrect graph below OA. OB. Oc. 100 There appears to be linear relationship between x and y. b. First compute the c |(Round to is needed.) r= no Now test to determine on is significant at the signifi tested. a positive O A. Ho: p=0, Ha: a negative b. First compute the correlation coefficient r. (Round to three decimal places as needed.) Now test to determine whether the correlation significant at the significance level of 0.10. First determine the null and alternative hypotheses that are to be tested. OA. Ho p0, H p0 OB. Ho: p0, Hipro OC. Hoi pa0, Hap<0 OD. Ho: ps0, Haip>0 Now identify the t-test statistic for the correlation. t = (Round to two decimal places as needed.) Now find the p-value. p-value = (Round to three decimal places as needed.) Determine the appropriate conclusion at the significance level of 0.10. Choose the correct answer below. O A. Do not reject the null hypothesis. There i sufficient evidence to conclude that the correlation is significant. O B. Do not reject the null hypothesis. There not sufficient evidence to conclude that the correlation is significant. OC. Reject the null hypothesis. There is sufficient evidence to conclude that the correlation is significant. O D. Reject the null hypothesis. There is not sufficient evidence to conclude that the correlation is significant.
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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