C is for cabbage ~ A researcher wants to know if the head weight of the cabbage is a good predictor for ascorbic acid (vitamin C) content. From a random sample of 60 cabbages, the researcher recorded the ascorbic acid content in milligrams and the weight of the cabbage head in pounds. Head weight is the X variable and Ascorbic acid content is the Y variable in this scenario. R output Coefficients: t value Pr(>ltl) < 2e-16 *** 9.75e-09 *** Estimate Std. Error (Intercept) 77.574 HeadWeight 3.096 25.052 -7.567 1.131 -6.689 --- Residual standard error: 7.668 on 58 degrees of freedom Multiple R-squared: 0.4355, Adjusted R-squared: 0.4257 F-statistic: 44.74 on 1 and 58 DF, p-value: 9.753e-09 The equation of the estimated regression line is written as Blank #1 = Blank #2 + Blank #3 X. Match what goes in the Blank #1, Blank #2 and Blank #3 in the equation above. 1. 77.574 2. 3.096 3. 25.052
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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