Consider the output shown in Table 4.1. What is the value of the regression df? 1 3
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.
![Table 4.1
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
0.99794806
Missing
0.99513164
Standard Error
1.64839211
Observations
20
ANOVA
Significance F
2.66E-19
af
MS
F
Regression
10561.07486
Missing
16
43.47514498
19
1295.585
Missing
2.717197
Residual
Total
10604.55
Coeficients
Standard Error
t Stat
P-Value
Intercept
0.562
1.327
0.424
0.677
X1
0.959
0.038
25.245
0.000
X2
1.117
0.125
8.916
0.000
X3
1.460
0.066
22.185
0.000
Consider the output shown in Table 4.1. What is the value of the regression df?
1
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