Call: 1m(formula = lifeexpectancy - gdp + thinness1019years + hivaids + incomecompositionofresources + percentageexpenditure + schooling + adultmortality + bmi, data = data) Residuals: Min Median 1Q -20.9507 -2.1990 -0.0606 3Q 2.3697 22.3862 Маx Coefficients: (Intercept) gdp thinness1019years hivaids Estimate Std. Error t value Pr(>|t|) 5.481e+01 5.110e-01 107.254 < 2e-16 *** 3.587e-05 1.393e-05 -9.929e-02 2.293e-02 -4.330 1.55e-05 *** -4.933e-01 1.834e-02 -26.905 2.576 0.010 * < 2e-16 *** incomecompositionofresources 8.745e+00 6.902e-01 12.671 < 2e-16 *** percentageexpenditure schooling adultmortality bmi 1.245e-04 9.005e-05 9.418e-01 4.532e-02 20.781 -1.777e-02 8.769e-04 -20.268 4.099e-02 5.602e-03 0.167 < 2e-16 *** < 2e-16 *** 7.316 3.44e-13 *** 1.383 Signif. codes: **' 0.001 **' 0.01 *' 0.05 .' 0.1 Residual standard error: 4.165 on 2449 degrees of freedom (480 observations deleted due to missingness) Multiple R-squared: 0.8136, F-statistic: Adjusted R-squared: 0.813 p-value: < 2.2e-16 1336 on 8 and 2449 DF,
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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Please write the regression equation and interpret the p-value which is indicated in output as p-value: < 2.2e-16.
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