Below is the regression output from a model for predicting the heart weight (in g) of cats from their body weight (in kg). The coefficients are estimated using a dataset of 144 domestic cat. The model output is also provided below. Estimate Std. Error t value Pr(>t|) (Intercept) body wt -0.357 0.692 -0.515 0.607 4.034 0.250 16.119 0.000 R2 = 64.41% Radi s = 1.452 64.66% %3D (a) We see that the point estimate for the slope is positive. What are Ho and HA for evaluating whether body weight is associated with heart weight in cats? (b) State the conclusion of the hypothesis test from part (a) in context of the data. (c) Calculate a 95% confidence interval for the slope of body weight, and interpret it in context of the data. (d) Do your results from the hypothesis test and the confidence interval agree? Explain.
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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