Friesen and Shine (2019) wanted to determine whether male Australian cane toads have different testes sizes in different parts of the species' range (edge of the range vs. core of the range). As part of the study, they needed to quantify how big a toad's testes are relative to the toad's body size. They decided to perform a linear regression of total testes mass (in mg) against body mass (in g) and use the residual for each toad as a measure of the toad's relative testes size. The Coefficient Estimates table for their least-squares regression procedure is shown below. Term Coefficient Standard Error t-value Pr > t (Intercept) 19.192 43.082 0.44547 0.65643 body mass 3.0063 0.36387 8.262 1.5733e-14 (a) Suppose the researchers do a t-test for the slope of the linear model. Write the null hypothesis for the test and show that, under the null hypothesis, the observed value of the t-statistic is indeed 8.262. (That is, do the appropriate computations to prove the value is 8.262) (b) The regression output was based on a sample of 213 cane toads. How many degrees of freedom does the appropriate t-distribution used to get the p-value and the t* critical value for confidence intervals have? (c) One toad in the dataset had a body mass of 128g and a total testes mass of 163 mg. Compute the residual corresponding to this toad and write a sentence interpreting the residual
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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