What's WRONG with my multiple regression model I am working on a regression model, more precisely, multiple regression model for predicting one single value. I have a dataset of cars and some technical data.
For example, I have the following prediction for the following car data:
Make: Volvo 
Model: XC90 T8 Recharge AWD 
Doors: 4 
Seats: 7 
SeatsMin: 7 
SeatsMax: 7 
Dimensions: 4950x1931x1776 
Length: 4950 
Width: 1931 
Height: 1776 
WheelBase: 2984 
Weight: 2320 
MaxWeight: 2980 
TrunkVolMin: 262 
TrunkVolMax: 1816 
TankVolume: 70 
Electric: Electric or hybrid 
TurboCharged: 1: Turbo charged engine 
Kompressor: 1: Kompressor charged engine 
Displacement: 1969 
EnginePower: 288(390)/6000 
EngineTorque: 640/2200 
MaxPower: 288.00 
MaxTorque: 640.00 
Druve: All wheel drive 
FrontWheelDrive: Front wheel drive 
RearWheelDrive: Rear wheel drive 
Gears: 8 
Automatic: Automatic gearbox 
Acceleration: 5.8 
MaxSpeed: 180 
FuelConsumption: 2.1 S 
CO2: 57 
FuelType: S 
Predicted_Acceleration: 5.66731 
Residuals: .13269

Acceleration by default is 5.8, the model gives 5.66731, meaning it is off by 0.1 or something. According to the model summary, $R^2$ is roughly 75.9% percent accurate.

EDIT: I do not know for sure, what other bits of data here can be used to form a more accurate model. I haven't made any dummy variables yet. Not decoded into binary.

It is not clear what your question is, especially as the prediction looks amazingly accurate to me: 5.67 versus 5.8 is just an error of about 2%. Maybe you are misinterpreting the reported value of $R^2$ as a measure for accuracy, which it is not.
A nice thing about linear models is that they also estimate the accuracy of the prediction by means of a "prediction interval"*). It estimates, under the assumption that the model is correct, an interval in which the response value will be observed 95% of the times for the given predictor values. In R, e.g., you can obtain it with
predict(model, data.frame(x1=val1, x2=val2, 
          ...), interval="predict")

This way, you can estimate the accuracy of the prediction even when the true value is unknown.
*) Edit: in the original version of the answer, I called the "prediction interval" a "confidence interval", but as pointed out by @whuber, this term is reserved for parameter estimates.
