The regression output from a model for predicting the heart weight (in g) of cats from their body weight (in kg) is provided below. The coefficients are estimated using a dataset of 144 domestic cats. Estimate Std. Error t value Pr(>|t|) (Intercept) -0.357 0.692 -0.515 0,607 body wt 4.034 0.250 16.119 0.000 S = 1.452 R2 = 64.66% R2 adj = 64.41% (a) What are the hypotheses for evaluating whether body weight is positively associated with heart weight in cats? O Hoi B120 HA: B1 < 0 O Ho: B1 = 0 HA: B1 > 0 O Ho: B1 # 0 HAi B1 = 0 O Hoi B1 = 0 HA: B1 * 0 O Hoi B1 = 0 HA: B1 < 0 (b) State the conclusion of the hypothesis test from part (a) in context of the data. (Use a significance level of 0.05. Enter your test statistic to three decimal places.) The test statistic is T = 0 x with degrees of freedom df = .05 X . The p-value is less than 0.05. Therefore, we reject vv Ho. The data provide strong evidence that body weight is positively associated with heart weight in cats.
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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