Parameter estimates: Parameter e Estimate e std. Err. + Alternative. DF • T-Stat. P-value *O 131 2.7735131 =0 131 7.507004 <0.0001 =0 131 3.5386467 Intercept 17.966577 6.4779134 0.0064 Father 0.50354896 O.067077219 Mother 0.27714316 0.078318967 0.0006 Analysis of variance table for multiple regression model: Source DF MS F-stat P-value 2 320.94662 160.47331 37.637221 <0.0001 Model Error 131 558.54293 4.2636865 Total 133 879.48955 Summary of fit: Root MSE: 2.0648696 R-squared: 0.3649 R-squared (adjusted): 0.3552
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.
Interpreting a Computer Display. In Exercises 5–8, we want to consider the
Height of Son A son will be bom to a father who is 70 in. tall and a mother who is 60 in. tall. Use the multiple regression equation to predict the height of the son. Is the result likely to be a good predicted value? Why or why not?
Step by step
Solved in 2 steps with 2 images
