Emma’s parents recorded her height at various ages up to 66 months. They entered the data into StatCrunch and found the following statistics: Simple linear regression results: Dependent Variable: Height Independent Variable: Age Height = 22.324324 + 0.34234234 Age Sample size: 5 R (correlation coefficient) = 0.99399497 Check all statements that are correct: The correlation is positive At birth, Emma was approximately 22.3 inches tall. In a month, Emma grew approximately 0.34 inches. The correlation is negative. When Emma is 12 years (144 months) old, the parents can expect her to be about 71.6 inches tall. There is no correlation.
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.
Emma’s parents recorded her height at various ages up to 66 months. They entered the data into StatCrunch and found the following statistics: Simple linear regression results: Dependent Variable: Height Independent Variable: Age Height = 22.324324 + 0.34234234 Age
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