Study, Citing Student Injuries, Calls for Safety Belts on Buses

National data show that accidents involving school buses send 17,000 children to emergency rooms each year.

About one-fourth of the accidents occur when children are boarding or leaving school buses. Crashes account for 42 percent, according to a study in the November issue of Pediatrics, to be released Monday.

Slips and falls, being jostled in sudden stops or turns and roughhousing are among other ways children get hurt on buses, the study found.

Injuries range from cuts and sprains to broken bones, but most are not life-threatening and do not require hospitalization. And the number of students injured is a small fraction of the 23.5 million children who travel on school buses nationwide each year, the study said.

The researchers said the results provided a strong argument for requiring safety belts on school buses, something industry groups say is unnecessary.

Safety belts, particularly lap-shoulder belts, ''could not only prevent injuries related to crashes,'' said the lead author, Jennifer McGeehan, a researcher at the Center for Injury Research and Policy at Columbus Children's Hospital in Ohio, but also keep children seated ''so they're not falling out of their seats when buses make normal turns or brake.''

The research, involving nonfatal injuries treated in emergency rooms, is based on data from 2001 to 2003 from a surveillance system operated by the Consumer Product Safety Commission.

Data from the National Highway Traffic Safety Administration through 2005 show that about 8,000 children are injured each year in school bus crashes, but on average fewer than 9 are killed. Those numbers have remained stable for the last decade or so, the agency said. The tally is based on police reports, and not all injuries resulted in emergency room treatment.

Source: InfoTrack

Nov 6, 2006

Conviction fiasco wrecks crime drive

ALMOST half of the offenders caught by police are getting away without being punished by a court, according to an independent study which exposes as a sham Tony Blair's pledge to be tough on crime.

In the past year 624,000 of the 1.327m offences "brought to justice" were "non-convictions" -dealt with by cautions, summary fines or official warnings, to boost the clear-up rate.

The 75-page report, by the Centre for Crime and Justice Studies at King's College London, concludes there have been no "significant improvements" in law and order under Labour, and Britain remains "a high crime society".

The report, published in association with The Sunday Times, assesses the boosting of spending on criminal justice by over a third in real terms over the past decade, to Pounds 22.7 billion this year.

It claims Britain spends proportionately more on criminal justice than any other western country but that "there has not been a significant step change" in outcome. It dismisses Labour's manifesto claims that it has made Britain a safer place as "overstated and at times misleading".

From 1998-99 to 2004-05 the police received a 21% real terms increase in funding, with even larger rises for the probation service (160%), the crown courts (116%) and the Crown Prosecution Service (44%).

Yet the report shows: oCourt convictions as a proportion of offences "brought to justice" have fallen. The absolute number of convictions has also fallen. oNearly half (47%) of the 1.327m offences "brought to justice" in 2006 were "non-convictions". * Robberies have risen and murders are up by more than a third, from 600 in 1997 to 820 last year.

Source: InfoTrack

Jan 14, 2007

individual

Observation or case

variable

Which of these variables are categorical? Which are numerical continuous variables? Which are numerical discrete?

Education Count(millions) Percent

Less than high-school 4.6 11.8

High-school grad 11.6 30.6

Some college 7.4 19.5

Associate degree 3.3 8.8

Bachelor’s degree 8.6 22.7

Advanced degree 2.5 6.6

Create a Bar Chart

0

2

4

6

8

10

12

Count(millions)

Less than high-school

High-school grad

Some college

Associate degree

Bachelor’s degree

Advanced degree

Create a Pie Chart

Less than high-school

High-school grad

Some college

Associate degree

Bachelor’s degree

Advanced degree

0.1

.2.3

.4.5

Den

sity

2 4 6 8 10Percent

What is the overall pattern of the distribution? Are there any outliers?

Where is the center of the distribution? How would you characterize its spread?

Go to

http://amu-chemlab.avemaria.edu/~martinez/ECON303/index.htm

Use Text file: MPG– hist mpg

Use arch_firms.txt

•Go to•http://amu-chemlab.avemaria.edu/~martinez/ECON303/index.htm •Use Text file: MPG

•hist mpg

02

04

06

08

01

00F

req

uenc

y

-20 -10 0 10 20Percent return

P:\Ave_Maria\Spring_2007\STATS\Packet\Histogram\Length_of_calls_IPS.do

05

001

000

150

02

000

250

0F

req

uenc

y

0 200 400 600length

Length of phone calls to a customer service center

What is the likelihood that we will have at least 4 hurricanes this year?

50

100

150

1989m7 1990m7 1991m7 1992m7 1993m7 1994m7m

Housing Starts Prediction

See file Yield_Tbill.txt

Describing Distributions with Numbers

Mean age of GM’s kids

Joaquin 4.81

Miguel 3.036

Marisabel 1.074

Mean age

Go tohttp://amu-chemlab.avemaria.edu/~martinez/

ECON303/index.htm Use Text file: MPG

– summarize mpg Use SAT_AVG.dta

– summarize svavg smavg grad

bysort worker: tabstat earnings, stat(mean)

Bank_worker_earnings.dta

histogram

tabstat amountspent if amountspent<70, stat(mean)amount_spent.dta

growth.dta

hist g, width(1)

tabstat g, stat(mean median)

tabstat g if g<8, stat(mean median)

tabstat svavg smavg grad, stat(mean min q max n)

SAT_AVG.dta

graph box svavg smavg

use http://amu-chemlab.avemaria.edu/~martinez/ECON303/growth_EE.dta, clear

bysort region: tabstat g, stat(min q max)

gr box g, over(r)

10 15 20 25 30 35

x= 12 mean = 19.7520 x=30

deviation = 10.25deviation = -7.75

tabstat svavg smavg grad, stat(mean min q max sd var n)

SAT_AVG.dta

Using Excel, calculate

Value Deviations Squared Deviations

1600

1157

937

800

707

700

mean = sum= sum =

Show results on the board

Standard Deviation

s measures spread around the mean and should only be used when the mean is chosen as the measure of center.

s=0 only when there is no spread. s has the same units as the observations Like the mean, s is not resistant: strong

skewness or outliers greatly increase s.

-1-.

50

.51

han

dedn

ess

1 2 3 4Frequency

Handedness08.dtadotplot handedness

. tabstat h, stat(min q max)

variable min p25 p50 p75 max

handedness -0.3 0.75 0.837662 1 1

Handedness07.dta

Recognizing outliers

A nice rule is the 1.5 x IQR rule. The distance between the 25% quartile and the

75% quartile is the Inter-Quartile Range.

In this case, IQR=145.5, and 1.5x145.5=218.25.

1 54.5 103.5 200 2631

min q1 median q3 max

IQR1.5 x IQR 1.5 x IQR

Handedness scores

-1 -0.5 0 0.5 1

mean – 2*sd mean – sd mean mean + sd

-0.6205347 0.029635 0.679812 1.329988

Handedness08.dta

When confronted with data

First open the file and look at the data Then make a graph, e.g., a histogram Look for an overall pattern and for outliers Calculate a numerical summary

… and if there are enormous amounts of data, we can describe them with smooth curves.

The Normal Distributions

State_Unemp.dta hist p, bin(4)

0.0

5.1

.15

.2.2

5D

ensi

ty

2 4 6 8Percent

0.1

.2.3

.4D

ensi

ty

2 4 6 8Percent

State_Unemp.dta hist p, bin(7)

State_Unemp.dta hist p, bin(7)

0.1

.2.3

.4.5

Den

sity

2 4 6 8 10Percent

State_Unemp.dta hist p, bin(15)

0.2

.4.6

.8D

ensi

ty

2 4 6 8 10Percent

State_Unemp.dta hist p, bin(40)

0.2

.4.6

.8D

ensi

ty

2 4 6 8 10Percent

State_Unemp.dta hist p, bin(50) kdensity

0.2

.4.6

.8D

ensi

ty

1 2 3 4 5 6Percent

State_Unemp.dta hist p if p<8, bin(50) kdensity

Center in weightCenter in distance

Center in weight

Center in weight

Center in distance

Center in weight

Why is the total area under the curve = 1?

What percent of the observations lie above 0.8?

What percent of obs lie below 0.6?

What percent lie between 0.25 and 0.75?

What is the mean of the distribution?

Standardizing potato chip bag weights

15.0

12.97.815.0

12.93.915.0

12.9

15.0;12.9

z

z

weightz

What percentage of years have least 4 hurricanes?

Normal distribution calculations

An area under a density curve is a proportion of the observations in a distribution.

Any question about what proportion of observations lies in some range of values can be answered by finding an area under the curve.– Because all Normal distributions are the same

when we standardize the data, we can find areas under any Normal curve from a single table.

Indicates the proportion of the data that is less than z.

Look up the value in Table A

Look up the value in Table A

= 1 – Table entry

Look up the value in Table A

= 1 – Table entry

x= 0; = 12; = 16.5

What is the proportion of years since 1945 in which the S&P500 fell?

Assessing the Normality of distributions

Examples of Normally distributed data– MPG ratings– Average payrolls in MLB– State unemployment rates

Examples of non-Normally distributed data– Personal income– Gross sales of firms– Service lifetime of equipment.

use http://amu-chemlab.avemaria.edu/~martinez/ECON303/state_unemp.dta, clear

hist percent if percent<8, width(1) kdensity

. qnorm p if p<8

. qnorm p

qnorm plots the quantiles of varname against the quantiles of the

normal distribution.

Is skewed to the right: the high salaries are farther from the center (2*s) than the low salaries (1.5*s)

The overall pattern is Normal, even

though there are deviations. That’s

ok: our statistical methods can handle

small deviations from Normality.

Is almost symmetric but for one very low return. Also, the tails are “fatter” than normal.

-20

-10

01

02

0P

erc

ent r

etur

n

-10 -5 0 5 10 15Inverse Normal

If this were normal, there would be much fewer very low or very high values.

0.0

5.1

Den

sity

-20 -10 0 10 20Percent return

0.0

5.1

.15

Den

sity

-20 -10 0 10 20normal

percent_return.dta summarize pgen normal = invnorm(uniform())*4.192153+1.068817sum normalhist p, kden nodrawgraph rename percentreturnhist n, kden nodrawgraph rename normalgraph combine percentreturn normal, rows(2) xcommon

Beyond the Basics: Density Estimation

0.0

5.1

.15

.2.2

5D

ensi

ty

2 4 6 8 10Growth

hist g, width(1) kden

Beyond the Basics: Density Estimation

hist g, width(1) kden kdenopts(w(.5))

0.0

5.1

.15

.2.2

5D

ensi

ty

2 4 6 8 10Growth

End