I have a question regarding my homework from chapter 7 I need to fit the linear model in y = β0 + β1χ + ε for the following test Residuals: Min 1Q Median 3Q Max -18.7823 -0.4865 0.8395 3.0765 7.1123 Coefficients: Estimate Std. Error t value (Intercept) -6.4793 5.5481 -1.168 LAT 0.7515 0.1438 5.228 Pr(>|t|) (Intercept) 0.249 LAT 4.79e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 5.498 on 43 degrees of freedom Multiple R-squared: 0.3886, Adjusted R-squared: 0.3744 F-statistic: 27.33 on 1 and 43 DF, p-value: 4.786e-06 Also how to explain the Parameter Estimates and interpret the parameters with hypothesis test.
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.
I have a question regarding my homework from chapter 7
I need to fit the linear model in y = β0 + β1χ + ε for the following test
Residuals:
Min 1Q
-18.7823 -0.4865 0.8395 3.0765 7.1123
Coefficients:
Estimate Std. Error t value
(Intercept) -6.4793 5.5481 -1.168
LAT 0.7515 0.1438 5.228
Pr(>|t|)
(Intercept) 0.249
LAT 4.79e-06 ***
---
Signif. codes:
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.498 on 43 degrees of freedom
Multiple R-squared: 0.3886, Adjusted R-squared: 0.3744
F-statistic: 27.33 on 1 and 43 DF, p-value: 4.786e-06
Also how to explain the
Parameter Estimates and interpret the parameters with hypothesis test.
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