| Tag | Value |
|---|---|
| file | Inferential_Statistics_eur-inferential_statistics-137-en_eur-inferential_statistics-137-en |
| name | eur-inferential_statistics-137-en |
| section | Inferential Statistics/Regression/Multiple linear regression/R squared change |
| type | num |
| solution | 0.006 |
| tolerance | 0 |
| Type | Performing analysis |
| Program | SPSS |
| Language | English |
| Level | Statistical Literacy |
A researcher is interested in the influence of the drop in price for gasoline (bezine) on students who own a car. He wants to predict how many kilometers they will drive in in Januari 2015 [KM_JAN] by using a few predictors. For 202 students, the researcher has gathered the following data: 1) actual driven kilometers in Jan 2015 [KM_JAN], 2) actual driven kilometers in October 2014 [KM_OCT], 3) income for January 2015 [INCOME_JAN], 4) total kilometers traveled using public transport for February 2015 [PUB_TRANS], and 5) type of car [car] (1 = hybrid, 2 = sedan, 3 = suv).
Open the data file.
Expand the model using the variable for type of car [car]. Create dummy variables and make “hybrid” your reference group. How much did the proportion of explained variance in driven kilometers in January 2015 increase after the variable car [car] was added as a predictor to the model?
The proportion explained variance in driven kilometers in January 2015 increased with 0.006 by adding the variable car to the model.