ake a look at the following sample regressions (type of regression in top row), choose the type of model that best fits the data. Linear Logarithmic Intercept 6.7904 −5.6712 x 1.0607 NA In(x) NA 10.5447 se 2.4935 1.5231 R2 0.8233 0.9341 Adjusted R2 0.8013 0.9259 Linear Logrithmic Both equally Neither model explains a majority of the variation in y
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.
Take a look at the following sample regressions (type of regression in top row), choose the type of model that best fits the data.
|
|
Linear |
Logarithmic |
|
Intercept |
6.7904 |
−5.6712 |
|
x |
1.0607 |
NA |
|
In(x) |
NA |
10.5447 |
|
|
|
|
|
se |
2.4935 |
1.5231 |
|
R2 |
0.8233 |
0.9341 |
|
Adjusted R2 |
0.8013 |
0.9259 |
|
Linear |
||
|
Logrithmic |
||
|
Both equally |
||
|
Neither model explains a majority of the variation in y |
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