Computer output for fitting a simple linear model is given below. State the value of the sample slope for the given model. In testing if the slope in the population is different from zero, identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model. The regression equation is Y=92.5-7.87X. Predictor Coef SE Coef T P Constant 92.499 4.693 19.71 0.000 X -7.8692 0.9182 -8.57 0.000
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.
Computer output for fitting a simple linear model is given below. State the value of the sample slope for the given model. In testing if the slope in the population is different from zero, identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model.
The regression equation is Y=92.5-7.87X.
| Predictor | Coef | SE Coef | T | P |
| Constant | 92.499 | 4.693 | 19.71 | 0.000 |
| X | -7.8692 | 0.9182 | -8.57 | 0.000 |
Consider that β is the slope coefficient of the population regression equation.
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