stimate the linear model and use an analysis-of-variance approach to test the hypothesis that β1 = 0 against the alternative hypothesis β1 is not equals to 0. What is the value of the computed F and conclusion? Y 126 135 124 128 130 128 128 129 X 0.17 0.13 0.11 0.15 0.19 0.12 0.15 0.21 F0 = 0.112, fail to reject H0 F0 = 0.112, reject H0 F0 = 0.167, fail to reject H0 F0 = 0.167, reject H0 None from the choices.
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.
Estimate the linear model and use an analysis-of-variance approach to test the hypothesis that β1 = 0 against the alternative hypothesis β1 is not equals to 0.
What is the value of the computed F and conclusion?
|
Y |
126 |
135 |
124 |
128 |
130 |
128 |
128 |
129 |
|
X |
0.17 |
0.13 |
0.11 |
0.15 |
0.19 |
0.12 |
0.15 |
0.21 |
|
F0 = 0.112, fail to reject H0 |
||
|
F0 = 0.112, reject H0 |
||
|
F0 = 0.167, fail to reject H0 |
||
|
F0 = 0.167, reject H0 |
||
|
None from the choices. |
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