#### Words of Wisdom:

"( o Y o ) means nice rack" - Graeffsgirl

# Statistics Report

• Date Submitted: 05/02/2012 10:56 PM
• Flesch-Kincaid Score: 58.6
• Words: 4114
• Report this Essay
Data was collected from http://archive.ics.uci.edu/ml/datasets/Automobile on the curb weight (kg) of cars, their engine sizes (cc) and the wheel base size (cm). The curb weights of each car were recorded in kilograms and the engine size cubic centimetres of 206 selected cars. The data chosen to investigate is continuous and hence appropriate for regression analysis.

The purpose of this investigation to find if there is a relationship between the engine sizes of a car (predictor variable) and the curb-weight of a car (response variable) and to compare this relationship with one of the wheel base size of a car (predictor variable) and curb weight (response variable) .

This graph is a scatter plot of Engine Size (cubic centimetres) vs. Curb Weight (kg). The scatter appears to particularly constant and to verify the validity of its linearity; it will be investigated under the Engine Size vs. Curb Weight Residuals section. There seems to be 3 possible outliers in the 5000-6000 cc range and also seems to be possible grouping in the 1500-2500 cc range. The straight line is the line of best fit between the x and y variables (engine size and the curb weight respectively) and has an equation of y=0.294x+546.9. The correlation coefficient is r= 0.85 and it measures the strength of the linear association and indicates that   as the curb weight tends to increase as the engine size increases and that there is a strong, positive, linear relationship between the two variables.
The model is quite good and it is saying that for every 1 cubic centimetre increase in the engine size, we would estimate a 0.3 kg increase in curb weight.

This graph is a scatter plot graph of Wheel Base Size (cm) vs. Curb Weight (kg). There is constant scatter. There seems to be some   y-outliers in the 250-260 cm range   and possible groupings about the 230-260 cm range. The line shown above is the line of best fit relationship between the two variables and has an equation of y=11.98x-1848. The...