Suppose a researcher is interested in examining the relationship between a person 's age and whether he or she likes the taste of cilantro. She collects a sample of n- 10 people and asks them whether they like the taste of cilantro. The following table summarizes the results. Does not like Cilantro 2 Does like Cilantro « 18 years old 18+ years old 3 The researcher wants to calculate the correlation between a person 's age and whether he or she likes the taste of cilantro. To do so, you (the researcher) must first create a table of the data by converting each variable to a numerical value. Assign a O to "< 18 years old" and 1 to "18+ years old. Then assign O to "does not like the taste of cilantro" and a 1 to " likes the taste of cilantro." Complete the top two rows of the your (the researcher's) data. Age (0 is <18; 1 is 18+) a) Ob) 1 Likes the taste of Cilantro (0 is no; 1 is yes) a) Ob) 1 a) ob) 1 a) ob) 1 1 The phi-coefficient is a) -0.09 b) -0.16 c) -0.20 d) 0.80
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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