Use the given data set to complete parts​ (a) through​ (c) below.​ (Use α=​0.05.)   x 10 8 13 9 11 14 6 4 12 7 5   y 7.45 6.77 12.73 7.11 7.81 8.84 6.08 5.39 8.16 6.41 5.72           i) The linear correlation coefficient is r = ? ​(Round to three decimal places as​ needed.)   ii) Using the linear correlation coefficient found in the previous​ step, determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables. Choose the correct answer below.   A. There is sufficient evidence to support the claim of a nonlinear correlation between the two variables.   B. There is insufficient evidence to support the claim of a nonlinear correlation between the two variables.   C. There is sufficient evidence to support the claim of a linear correlation between the two variables.   D. There is insufficient evidence to support the claim of a linear correlation between the two variables.   iii) . Identify the feature of the data that would be missed if part​ (b) was completed without constructing the scatterplot. Choose the correct answer below.     A. The scatterplot reveals a perfect​ straight-line pattern and does not contain any outliers.   B. The scatterplot does not reveal a perfect​ straight-line pattern, and contains one outlier.   C. The scatterplot does not reveal a perfect​ straight-line pattern.   D. The scatterplot reveals a perfect​ straight-line pattern, except for the presence of one outlier.

Q: OB. 0.196 OC. 0.256 O D. 0

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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.