In this template Rapporter will present you Multidimensional Scaling.

Multidimensional-scaling is a technique which gives us a visual representation about the distances between the observations.

Below you can see a plot, that presents you the distance between the observations, which was calculated based on *Age*, *Internet usage for educational purposes (hours per day)* and *Internet usage in leisure time (hours per day)*.

84 differs the most from the others, and 8 seems to be the most "common" observation, which lie nearest to all other observations.

*284* and *84* (8.02) are the "furthest", *280* and *1* (0) are the "nearest" to each other.

Now let's see which observations can be said statistically far/similar to each other in general. The *16* pairs with the biggest differences and the *10* pairs with the smallest differences will be presented. In the brackets you can see the amount of the distances between two observations.

There are *17* observations which are the most similar, and equal in the same time, that is a higher number than the wanted *16*, thus will not be reported one-by-one. Set *17* as parameter *max.dist.num* to check the pairs if you are interested.

There are *318* observations which are the most similar and equal in the same time, that is a higher number than the wanted *10*, thus will not be reported one-by-one. Set *318* as parameter *min.dist.num* to check the pairs if you are interested.

In this template Rapporter will present you Multidimensional Scaling.

Multidimensional-scaling is a technique which gives us a visual representation about the distances between the observations.

Below you can see a plot, that presents you the distance between the observations, which was calculated based on *Age*, *Internet usage for educational purposes (hours per day)* and *Internet usage in leisure time (hours per day)*.

84 differs the most from the others, and 8 seems to be the most "common" observation, which lie nearest to all other observations.

*284* and *84* (8.02) are the "furthest", *280* and *1* (0) are the "nearest" to each other.

Now let's see which observations can be said statistically far/similar to each other in general. The *17* pairs with the biggest differences and the *30* pairs with the smallest differences will be presented. In the brackets you can see the amount of the distances between two observations.

According to the used variables (*Age*, *Internet usage for educational purposes (hours per day)* and *Internet usage in leisure time (hours per day)*) the *17* furthest pair of observations are:

*284*and*84*(8.02)*224*and*84*(7.87)*230*and*84*(7.84)*84*and*68*(7.81)*463*and*84*(7.79)*583*and*84*(7.79)*582*and*84*(7.72)*122*and*84*(7.72)*460*and*84*(7.72)*606*and*84*(7.7)*607*and*84*(7.7)*128*and*84*(7.69)*253*and*84*(7.69)*84*and*41*(7.69)*269*and*84*(7.65)*376*and*84*(7.63)*506*and*84*(7.63)

There are *318* observations which are the most similar and equal in the same time, that is a higher number than the wanted *30*, thus will not be reported one-by-one. Set *318* as parameter *min.dist.num* to check the pairs if you are interested.

In this template Rapporter will present you Multidimensional Scaling.

Multidimensional-scaling is a technique which gives us a visual representation about the distances between the observations.

Below you can see a plot, that presents you the distance between the observations, which was calculated based on *drat*, *cyl* and *mpg*.

Honda Civic differs the most from the others, and Ferrari Dino seems to be the most "common" observation, which lie nearest to all other observations.

*Honda Civic* and *Cadillac Fleetwood* (5.48) are the "furthest", *Mazda RX4 Wag* and *Mazda RX4* (0) are the "nearest" to each other.

Now let's see which observations can be said statistically far/similar to each other in general. The *17* pairs with the biggest differences and the *30* pairs with the smallest differences will be presented. In the brackets you can see the amount of the distances between two observations.

According to the used variables (*drat*, *cyl* and *mpg*) the *17* furthest pair of observations are:

*Honda Civic*and*Cadillac Fleetwood*(5.48)*Honda Civic*and*Lincoln Continental*(5.39)*Dodge Challenger*and*Honda Civic*(5.25)*Toyota Corolla*and*Cadillac Fleetwood*(5.1)*Toyota Corolla*and*Lincoln Continental*(5.04)*Honda Civic*and*Merc 450SLC*(4.85)*Fiat 128*and*Cadillac Fleetwood*(4.79)*Honda Civic*and*Merc 450SE*(4.74)*Honda Civic*and*Duster 360*(4.74)*AMC Javelin*and*Honda Civic*(4.74)*Fiat 128*and*Lincoln Continental*(4.74)*Honda Civic*and*Chrysler Imperial*(4.68)*Honda Civic*and*Valiant*(4.68)*Honda Civic*and*Merc 450SL*(4.67)*Dodge Challenger*and*Toyota Corolla*(4.67)*Pontiac Firebird*and*Honda Civic*(4.52)*Honda Civic*and*Hornet Sportabout*(4.46)

According to the used variables (*drat*, *cyl* and *mpg*) the *30* nearest pair of observations are:

*Mazda RX4 Wag*and*Mazda RX4*(0)*Chrysler Imperial*and*Duster 360*(0.08)*Merc 230*and*Datsun 710*(0.13)*Lincoln Continental*and*Cadillac Fleetwood*(0.13)*Merc 450SL*and*Merc 450SE*(0.15)*AMC Javelin*and*Merc 450SLC*(0.15)*Pontiac Firebird*and*Hornet Sportabout*(0.15)*AMC Javelin*and*Chrysler Imperial*(0.17)*AMC Javelin*and*Duster 360*(0.19)*Merc 450SLC*and*Merc 450SE*(0.2)*Merc 280C*and*Merc 280*(0.23)*AMC Javelin*and*Merc 450SE*(0.25)*Merc 450SL*and*Hornet Sportabout*(0.28)*Merc 280*and*Mazda RX4*(0.3)*Merc 280*and*Mazda RX4 Wag*(0.3)*Merc 450SLC*and*Duster 360*(0.3)*Chrysler Imperial*and*Merc 450SLC*(0.31)*Pontiac Firebird*and*Merc 450SL*(0.32)*Merc 450SLC*and*Merc 450SL*(0.35)*Toyota Corona*and*Datsun 710*(0.35)*Toyota Corolla*and*Fiat 128*(0.36)*AMC Javelin*and*Merc 450SL*(0.38)*Merc 240D*and*Datsun 710*(0.4)*Merc 450SE*and*Hornet Sportabout*(0.41)*Chrysler Imperial*and*Merc 450SE*(0.41)*Volvo 142E*and*Merc 230*(0.42)*Merc 450SE*and*Duster 360*(0.44)*Maserati Bora*and*Camaro Z28*(0.45)*Toyota Corona*and*Merc 230*(0.46)*Pontiac Firebird*and*Merc 450SE*(0.46)

This report was generated with R (3.0.1) and rapport (0.51) in *3.338* sec on x86_64-unknown-linux-gnu platform.

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