Investigation on the effects of data manipulation Part 1: Research on Gun Deaths Part 2: Graphing and data manipulation Researchers: Matt Johns, Will Parkinson and James Pye for Mr. Lieff, Carleton Place High School

Part 1: Gun death Research Part 1: Gun death Research  Initially we attempted to obtain data on the relations between gun deaths and various trends in Canada.  We will show a portion of the data we collected.  The research will investigate trends and influences that certain government bills have had on gun deaths in Canada.

Bill C-150  The passage of Bill C-150 was May 14 th, 1969  The Bill deals with abortion rights.  The moral and political content was downplayed.  It legalized contraception and therapeutic abortion, but it assigned doctors control over women's reproductive decisions.

Bill C-51  Enforced since 1977  This Act requires acquisition certification for all firearms, restricts the availability of some types of firearms to certain types of individuals.  It establishes procedures for handling and storing firearms, requires permits for those selling firearms, and increases the sentences for firearm offences.

Bill C-17  The overarching goal of both the previous bill and this bill created in 1991 named “Firearms Control Initiative” was meant to enhance public safety from firearms accidents and suicides as well as reducing criminal firearms misuse thereby making Canada a safer society.

Bill C-68  The bill imposes a mandatory minimum sentence of four years in prison, in addition to a lifetime prohibition against possession of a firearm upon conviction of any of ten specific violent offences with a firearm.  Proclaimed into law on January 1,  The offences affected are: manslaughter, attempted murder, sexual assault with a weapon, robbery and criminal negligence causing death, causing bodily harm with intent, aggravated sexual assault, kidnapping, hostage taking, extortion.

Part 2: An Investigation on Data Manipulation. Part 2: An Investigation on Data Manipulation.  We will show how anybody can manipulate data to display any trend they wish.  We will use different graphing techniques and mathematical functions to display the data.

Changing the Scale

 By Changing the scale on a graph we are able to change the appearance of a relation.  As seen the previous graph the was a line with a R^2 of 1.0 and slope of  In the next graph observe how the slope and equation remain the same although the graph looks much different.  By changing the scale on the Y axis you can change the appearance of the slope of a graph.

Small Sample Size

Too Few Data Points  Here it shows that a large immigration population leads to a large amount of gun deaths but once the immigrant population reaches 1.4 million the gun deaths begin to decrease.  There is obvious problem with the data in the way in which it is presented because this relation is improbable.  As you can see there are only 4 data points on this graph that the curve is fitting to which makes the curve easily fit the data.  This explains why this curve only has as R^2 of 0.96

The Log Function

Y = 1/Log(x)  By using the logarithmic function on x we can change the appearance of the graph.  Although the equation changes the data that is used remains the same, it is just placed into the equation y = 1/log(x)  This is used to show how the U.S. Government debt does not appear to be increasing at a exponential rate like it actually is.

Logarithmic Scale

 As you have noticed the scale on the x-axis of the graph has a very weird structure.  This is called a logarithmic scale or a semi-log graph.  1 to 10 takes up the same amount of space as 10 to 100. Similarly 10 to 100 takes up the same space as 100 to  Doing this changes a linear equation into a equation with a curve.  In this case it makes the data look like it is decreasing at a faster rate than it really is.

Coincidences

Population vs. Bacon Prices

 You can use a coincidence to find a relation that shouldn’t exist.  Using this people can draw false conclusions on the data.

Conclusion:  We have shown that data can be manipulated to show any relation needed to be expressed.  All that needs to be done is footnote what has actually happened  By doing this most people wont pick up on the footnote and the people who made the graph cannot get in trouble for ‘falsified data’  You should always look for stars that lead to footnotes to truly understand the data.