In the picture, there is a summary of regression analysis. Please interpret the P values for each coefficient and discuss the characteristics of your residuals. call:1m(formula = lifeexpectancy - gdp + thinness1019years + hivaids +incomecompositionofresources + percentageexpenditure + schooling +adultmortality + bmi, data = data)Residuals:Median1Q-20.9507 -2.1990 -0.0606Min3Q2.369722.3862MaxCoefficients:Estimate Std. Error t value Pr(>|t|)5.481e+01 5.110e-01 107.254 < 2e-16 ***(Intercept)gdpthinness1019yearshivaids0.010 *-9.929e-02 2.293e-02 -4.330 1.55e-05 ***< 2e-16 ***3.587e-05 1.393e-052.576-4.933e-01 1.834e-02 -26.905incomecompositionofresources 8.745e+00 6.902e-01 12.671 < 2e-16 ***percentageexpenditureschoolingadultmortalitybmi1.245e-04 9.005e-051.3830.167< 2e-16 ***< 2e-16 ***7.316 3.44e-13 ***9.418e-01 4.532e-02 20.781-1.777e-02 8.769e-04 -20.2684.099e-02 5.602e-03Signif. codes: 0 ****0.001 **' 0.01 *' 0.05 .' 0.11Residual 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: 0.8131336 on 8 and 2449 DF, p-value: < 2.2e-16

The p-values corresponding to each coefficient implies, if there is a significance of the particular…

Answered: In the picture, there is a summary of…

In the picture, there is a summary of regression analysis. Please interpret the P values for each coefficient and discuss the characteristics of your residuals.

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter4: Eigenvalues And Eigenvectors

Section4.6: Applications And The Perron-frobenius Theorem

Problem 25EQ

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Concept explainers

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.

Question

100%

In the picture, there is a summary of regression analysis. Please interpret the P values for each coefficient and discuss the characteristics of your residuals.

call:
1m(formula = lifeexpectancy - gdp + thinness1019years + hivaids +
incomecompositionofresources + percentageexpenditure + schooling +
adultmortality + bmi, data = data)
Residuals:
Median
1Q
-20.9507 -2.1990 -0.0606
Min
3Q
2.3697
22.3862
Max
Coefficients:
Estimate Std. Error t value Pr(>|t|)
5.481e+01 5.110e-01 107.254 < 2e-16 ***
(Intercept)
gdp
thinness1019years
hivaids
0.010 *
-9.929e-02 2.293e-02 -4.330 1.55e-05 ***
< 2e-16 ***
3.587e-05 1.393e-05
2.576
-4.933e-01 1.834e-02 -26.905
incomecompositionofresources 8.745e+00 6.902e-01 12.671 < 2e-16 ***
percentageexpenditure
schooling
adultmortality
bmi
1.245e-04 9.005e-05
1.383
0.167
< 2e-16 ***
< 2e-16 ***
7.316 3.44e-13 ***
9.418e-01 4.532e-02 20.781
-1.777e-02 8.769e-04 -20.268
4.099e-02 5.602e-03
Signif. codes: 0 ****
0.001 **' 0.01 *' 0.05 .' 0.1
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
1336 on 8 and 2449 DF, p-value: < 2.2e-16

Definition Definition Statistical method that estimates the relationship between a dependent variable and one or more independent variables. In regression analysis, dependent variables are called outcome variables and independent variables are called predictors.

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