Police Use Different Standards of Case Study

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The color of the car was also considered as 1= brown, 2 = black, 3 = blue, 5 = red, 6 = yellow and 7 = white. Other variables included the time of the day and month at which the person was caught driving to control for the possible rush hour affect and month influence, the exact time of the stop was eliminated to avoid double counting in the model. The two dummy variables included the fact whether the person speeding was the resident or none resident. The none residents were assigned 1 to hypothize that none residents no matter of their gender and car type and time of the day, day of the month and month, are more tempted to speed over the limit. The second variable is the gender to assume that at all other things being equal, male are more prone to speed over the limit than men.

Thus, the hypothesis was that there is the following relationship to be estimated:

a + ?1*Car_Color1 + ?2*Time_Of_Day + ?3-Month+ ?2* Day + ?3-Month + + ?2*Resident_Dummy + ?3 *Gender_Dummy...+ ?

The hypothesis is tested with the confidence level of 95%, thus the allowed chance of rejecting no relationship between the variables when there is actually this relationship, is 5%. Decreasing the confidence level to 90% will give more errors in the model and the model did not result in better relationship. Having carried out this multifactor regression, the result revealed that there is no statistically significant relationship between the over speeding and the fact that the person is a resident or non-resident and the gender of the person.

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The first problem with the model could be the very data set as out of the 536 observations in the population, only 136 were the cases when people were none residents. Thus, the results could be distorted. The R2 in the model is extremely low and reveals that very little variation in the severity of this crime could be explain by the factors in the model. P-values are low only for the intercept and none-residence factor.

Regression Statistics

Multiple R

Square

Adjusted R. Square

Standard Error

Observations

For all other variables, P-values are reflective of the fact that either the population was not selected property, there are some statistical problems with it or the model is faulty.

Coefficients

Standard Error

Stat

P-value

Intercept

7,21031E-25

PLATE

1,8907E-09

COLOR

GENDER

TIME2

DAY

MONTH

In case when T. Stat is very large and positive, the hypothized value of the intercept is not large enough and at all variables being equal to zero, the chance for over speeding is high. Where the T. Stat value is too high and negative, the hypothized value then is too large. The F. ratio is greater than F. static and thus the coefficients for the independent variables should not equal in sum to zero. The implication is that they have influence on the dependant variable, or the over speeding, but the relationship was not modeled correctly.

Thus, the relationship exists, while it is difficult and cannot be estimated precisely due to problems with the population......

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