Kids' School Projects, etc. Interfere with Fishing

[longfisher]

My Son's physics teacher seems to have a thing for medieval artillery...that hurl nothing much but tennis balls and pencils.

Last year I helped my Son build a catapult. We built one from just garage crap wood and hardware left over from previous projects and it would throw a tennis ball with great force almost 40 meters. We actually had to detune it to get it to hit a target meter-diameter circle on the ground only 10 meters away.

The year before we built a massive crossbow device that could throw a pencil like a high-speed projectile over 20 yards.

This year it was a trevochet (spelling). It hurls a, what else, tennis ball, about 15 meters in a parabolic trajectory and has suprisingly good accuracy and repeatability. Some intersting physics at work there by the way.

Each of these projects eats into my fishing time like crazy. I spend about 2 - 3 days on everyone of them. And, I've been doing this sort of stuff for three kids now. It's amazing I've ever caught a fish or even started the boat motor but for charging the batteries and keeping the carbs free of resin.

On top of that, the wife wants her kitchen remodeled every 10 years whether it needs it or not. And, I own a 23 year old home that requires much maintenance.

So, I'm feeling a little put upon by all these non-fishing chores. I hardly have any time to keep the boat up anymore, given all the land-based chores I have.

Anyone else have the same problem and, if so, what have you done to try to achieve more balance in your life (other than divorce and drowning the kids at birth)?

LF

[Robja]

Had to do the same with two of my children. We were limited in size for the catapult and could only use wood glue ice cream sticks one dowel rod and rubber bands. When my son had the same teacher one of his was a Rube Goldberg machine using all the simple machines. I had to carry it into school under a sheet since one of the end parts was a gullitine that cut a carrot in half. He made the highest grade the teacher had given in that class for a project.

[Captain Michael Buffington]

use that creativity towards teaching them about the woods and waters. Less war and more wildlife. Steve is uploading an example. Stay tuned.

Mike

[longfisher]

use that creativity towards teaching them about the woods and waters. Less war and more wildlife. Steve is uploading an example. Stay tuned.

Mike

As you know, I and my Son spend many, many hours and days afield either hunting, camping, hiking, fishing, boating, skiing or snokeling.

I'd like nothing better than for the physics instructor to assign field or nautical navigation projects, instead. But that's not how it happens.

Or, have I missed your point?

LF

[bigfish4me]

I like doing the projects with my kids.........85% of my fishing is done at night......night time is the right time

[Captain Michael Buffington]

As you know, I and my Son spend many, many hours and days afield either hunting, camping, hiking, fishing, boating, skiing or snokeling.

I'd like nothing better than for the physics instructor to assign field or nautical navigation projects, instead. But that's not how it happens.

Or, have I missed your point?

LF

no, you did not miss my point. Take some video of the war machinery in action. I love inventing and inventions.

Mike

[Free_Jumper]

10 year old science project = i learned something too

Which Fishing Line is the strongest?

January 31, 2010


Report Abstract


There is no place to get reliable, independent information about buying monofilament line. I tested four brands of twelve pound test fishing line for the rated breaking strength in pounds. Using a spring scale and a wooden board with an anchor point in it gave useful results. There were differences between both dry line and wet line samples that could be important when making a buying decision. My hypothesis was partly correct. The data showed the most expensive line was not the best choice for fishing for a world record.


Table of Contents:

• Purpose
• Background
• Problem Statement
• Hypothesis
• Materials
• Methods
• Data analysis methods
• Dry line results
• Wet line results
• Discussion
• Conclusion
• Bibliography
• Acknowledgements

Table of Photgraphs Tables, and Graphics

1. Figure 1 photo of manufacturer line samples.
2. Figure 2 photo of line samples, and various materials.
3. Figure 3 of Method of knot “ Spyder hitch”
4. Figure 4 of measurement with result.
5. Figure 5 close-up of spring scale result.
6. Table 1 of statistics
7. Line graphs 1 (dry) and 2 (wet) of samples and standard deviations.
8. Bar graph 1 of Dry and Wet average line breaking strength.


Table of Measured Samples

• Ziploc 1.) containing samples tested performed Jan 2, 2010, Trilene Big Game 12 lb., 20 samples, tested dry.
• Ziploc 2.) containing samples tested performed Jan 18, 2010, Trilene Big Game 12 lb., 20 samples, tested wet.
• Ziploc 3.) containing samples tested performed Jan 11, 2010, Zebco 12 lb., 20 samples, tested dry.
• Ziploc 4.) containing samples tested performed Jan 18, 2010, Zebco 12 lb., 20 samples, tested wet.
• Ziploc 5.) containing samples tested performed Jan 17, 2010, Stren High Impact 12 lb., samples not tested.
• Ziploc 6.) containing samples tested performed Jan 17, 2010, Stren High Impact 12 lb., 20 samples, tested dry.
• Ziploc 7.) containing samples tested performed Jan 18, 2010, Stren High Impact 12 lb., 20 samples, tested wet.
• Ziploc 8.) containing samples tested performed Jan 17, 2010, Cajun Red Cast Line 12 lb., 20 samples, tested dry.
• Ziploc 9.) containing samples tested performed Jan 18, 2010, Cajun Red Cast Line 12 lb., 20 samples, tested wet.


Purpose:

My purpose of doing this project is one day I would like to catch an International Game Fishing Association (IGFA) world record on light tackle. Not only that but I wanted to learn which brand of line to have my Dad buy. There was only one way to find out and that one way was to test each of the four lines that we picked out.


Background Section:

Many fishing lines claim to be stronger than others. But, the true strength is a measure of the breaking point of a line. Look at the advertisements for fishing line on any line manufacturer's Web site. They all claim: Cast father! Better knot strength! Smaller diameter! Strongest monofilament we've ever made! Superior abrasion resistance!

Where can I go to figure it all out? Who has the data to back the manufacturer’s claims? No place that I could find on the web.

Problem statement:

It turns out, no industry standards exist to help fisherman sort the qualities of fishing lines. I couldn’t find any laws that require manufacturers to reveal and particular products information. So line manufacturers can pretty much make whatever claims they want. Then hope that you are lucky! Buy their and line go fishing for the big one!

So without reliable line-testing data, it is hard for the average fisherman to decide what is best for their needs.

Pick the right line!

The IGFA will test a sample of the line you used to catch your line class record fish. They need to make sure the line breaks at the specified weight. If you caught your potential line class world record fish on 12lb test line and when they test it, it breaks at 15lbs of weight in the IGFA test your record would not count.

Hypothesis

My hypothesis is that the more expensive line is higher quality and will break closest to its rated breaking strength.

Materials

1. Purchased four different spools (fig. 1) of monofilament line, marked "12 pound test", from WallMart.

2. 5 gallon plastic bucket filled with regular tap water to soak lines.

3. A Shimano hand-held spring scale (fig. 2) rated to 50 lb was used to measure line tension/breaking strength.

4. We also used a thin piece of wood with an anchor point (fig. 2) in it and we used a dark blue blanket so we could locate pieces of line after they broke.


Methods

We chose 12 pound rated test, a common line size used for Trout and Redfish, and tested 4 different lines to the same testing procedures. We assessed one thing we could measure, the breaking strength (fig. 3, Ziploc’s data samples 1- 9).

We cut twenty samples from each spool into about 2 foot sections of line. Tied a simple and consistent knot to form loops in each end. My assistant (Dad) tied a Spider hitch knot with one wrap to form a loop at both ends (fig 2.). The loop ends of the line were attached, one to the scale and two, to a similar diameter hook on the anchor board. Using both hands I lifted the spring scale ( fig. 2) slowly with line sample attached to the spring scale and the other end to the anchor point until the line broke (fig. 4). Tested each line sample dry and wet (. Determine and record the failure mode of line, either breaking at the knot or line segment.

I recorded the breaking “weight”, of the scale readings in pounds (lbs) to the nearest half of a pound of twenty breaks for each line type. So, test breaking strength with a spring scale to nearest 0.5 lb reading.

Another one of our methods is that when we popped the line sometimes the line would go every where our solution was that we put a dark blue blanket under our piece of wood with a anchor point in it and that way we could see the missing piece of line on the dark blue blanket (fig. 2). After we popped the line we mark each test sample with blue tape around the sample loops and put them in a zip lock bag.

To test wet line samples we placed all 20 samples of each line type in a bucket of water for at least 15 minutes.

Data Analysis Methods

I calculated the averages by hand in my notebook. I used two different websites to generate graphic data plots, generated ordered values (min, median, and max) to build a table of results (table 1) in Power Point and report statistics. My Dad put the sample data values into Excel spreadsheet to make x, y plots of the samples. He tried to show me how to generate the graphs using a wizard but it was confusing. The range of variation in data sampled shows up in these two plots (line graphs 1 and 2.). Generally, the more data you record the more extreme your highs and lows will be. The most common way to describe the range of variation is standard deviation (usually denoted by the Greek letter sigma: ). The standard deviation is simply the square root of the variance. I used a website http://www.physics.csbsju.edu/stats to generate the standard deviations. But really, I don’t understand the math used,
variance = ( (X01-Xavg)2 + (X02-Xavg)2 + (X03-Xavg)2 + ••• + (X20-Xavg)2 )/19
but wanted to try and understand what the data were showing jumping around. The standard deviations for wet showed that the samples had more variability or less consistency. He said I needed to explain this so it was added to line graphs 1 and 2..

He also made me throw out calculated data results that had been calculated with more precision than 0.5 lb.


Dry line test results

Overall the dry line breaking strength was greater (table 1) than when we tested the same lines wet.

Our dry line test results (table 1, bar graph 1) were that Trilene Big Game was our strongest with the average breaking strength at fifteen pounds (lbs). Our second strongest line was Stren high impact with the average breaking strength at thirteen point five lbs. Our third strongest was Cajun line with its average breaking point at thirteen lbs. Our weakest line was Zebco with it average breaking point at eleven point six lbs. I also realized that Cajun line and Trilene both have the same results after you subtract the minimum and the maximum they both get seven point five. When the dry tests broke, I think friction cut the knot in the dry tests.

Wet line results

The wet line test results were totally different from the dry test results (Table 1, bar graph 1). Our strongest wet line was Stren with the average breaking strength at eleven. Our second strongest line was Cajun line with the average breaking strength at ten point three. Our third strongest line was Trilene big game with the average breaking strength at nine point five. The weakest line was Zebco with its average breaking strength at eight point five.

The big change that happened between the lines is that when we soaked the lines with water I believe they lost tension or tightness and the knot got loose and cut the knot.

There was more variability in the data I recorded with wet samples.
The values jumped around a lot and you can se this in the wet x, y plot of the samples.

Discussion

I believe that I proved simple tools like a spring scale can be used to produce reliable line breaking results.

This took a lot of time to make samples and test them. I messed up a whole sample (Ziploc 5.) by tangling them up because I got in a hurry.

It really matters if the line is wet or dry. I don’t know all the reasons but I have some ideas that could be investigated. One of the reasons is that the knot got wet when you soaked it and it would probably loosen the knot. I think that when testing the pressure would cause the knot to slip, tighten and cut the line.
A surprising result, the least expensive, Zebco was very consistent but not that strong .


Conclusions:

It turns out that my hypothesis was kind of correct.
My data showed that Stren High Impact was the best line to use for world record fishing. But, Stren was almost two dollars cheaper than Trilene Big Game that tested highest on average dry, and less than Stren when wet.

Overall the more expensive lines were the strongest and the less expensive lines were weaker. The most expensive line was Trilene with its cost at seven dollars and thirty six cents, the second most expensive line was Stren with its cost at five dollars and forty seven cents, the third most expensive line was Cajun line with its cost at four dollars and ninety six cents, and our cheapest line was Zebco with its cost at two dollars and fifty cents.

Bibliography

Google Search on: Monofilament fishing line
“What You Need To Know About Monofilament Fishing Line” http://fishing.about.com/library/weekly/aa111702a.htm

Google Search on: Monofilament fishing line
http://en.wikipedia.org/wiki/Monofilament_fishing_line

Google Search on: Monofilament fishing line
http://fishing.about.com/library/weekly/aa111702a.htm

Google Search on: Create a graph.
http://nces.ed.gov/nceskids/createAgraph/default.aspx

Google Search on: Statistics and Sample variation.
http://www.physics.csbsju.edu/stats

Google Search on: International Game Fishing Association Rules


Web site used to calculate statistics
http://www.physics.csbsju.edu/stats/

http://www.physics.csbsju.edu/cgi-bin/stats/cstats

Typical Values
There are several common methods of selecting a "typical" value for data. The most common method is the average or mean.
To obtain an average value, add up all your data values and divide by the number of data items.
To obtain the median value, I sorted the list of breaking strengths from lowest to highest: and then select the value in the exact middle as the median. It turns out that if the number of items is even, as in this example (20 samples), there is no exact middle. So with even-numbered data sets, average the two near-middle values.
Estimates of the Range of Variation
The range of variation is limited only by the number of samples recorded. Generally, the more data you record the more extreme your highs and lows will be. Nevertheless, you should find that the range of line breaking strengths, that includes say 50% of your sample, remains about the same even if you produce more line break samples. That is to say, there is a common range of variation even as larger data sets produce rare "outliers" with ever more extreme deviation. Estimates of the range of variation seek to put a number to this common range of variation that doesn't depend on sample size.
The most common way to describe the range of variation is standard deviation (usually denoted by the Greek letter sigma: ). The standard deviation is simply the square root of the variance, so lets start by describing the variance. To obtain the variance start by subtracting the average from each data item. Since there will be about as many items above average as below average, the resulting list of numbers will have about as many positive values as negative values. (In fact this list of deviations-from-average must itself average to zero!) Square each deviation, and proceed to find the average of the squared-deviations. However, in finding the average squared-deviation, divide by N-1 rather than N. The result is the variance; take its square root to get the standard deviation.
variance = ( (X01-Xavg)2 + (X02-Xavg)2 + (X03-Xavg)2 + ••• + (X20-Xavg)2 )/19
.

For data that is "normally distributed" we expect that about 68.3% of the data will be within 1 standard deviation of the mean (i.e., in the range Xavg ± ). In general there is a relationship between the fraction of the included data and the deviation from the mean in terms of standard deviations.
It is expected that 95% of the data would be within 1.96 standard deviations of the mean (i.e., in the range Xavg ± 1.96 ). This is called a 95% confidence interval for the sample.


Dry Trilene Descriptive Statistics: Results
The results of some basic statistical tests performed at 11:14 on 24-JAN-2010.
20 data points were entered:
11.5 12.0 13.0 13.5 14.0 14.0 14.0 15.0 15.0 15.0 15.0 15.0 15.0 16.0 16.0 16.0 16.0 17.0 17.0 19.0
Mean = 14.9
95% confidence interval for actual Mean: 14.13 thru 15.77
Standard Deviation = 1.75
Hi = 19.0 Low = 11.5
Median = 15.0
Average Absolute Deviation from Median = 1.25





Dry Zebco Descriptive Statistics: Results
The results of some basic statistical tests performed at 11:21 on 24-JAN-2010.
20 data points : units in pounds (lb) were entered:
8.5 9.5 10.0 10.0 11.0 11.0 11.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.0 12.5 13.0 14.0 14.0
Mean = 11.6
95% confidence interval for actual Mean: 10.98 thru 12.27
Standard Deviation = 1.38
Hi = 14.0 Low = 8.50
Median = 12.0
Average Absolute Deviation from Median = 0.925
Dry Stren Descriptive Statistics: Results
The results of some basic statistical tests performed at 11:28 on 24-JAN-2010.
20 data points were entered:
10.0 11.0 11.0 11.0 11.0 12.0 13.0 13.5 14.0 14.0 15.0 15.0 15.0 15.0 15.0 15.0 15.5 15.5 16.0 16.0
Mean = 13.7
95% confidence interval for actual Mean: 12.75 thru 14.60
Standard Deviation = 1.97
Hi = 16.0 Low = 10.0
Median = 14.5
Average Absolute Deviation from Median = 1.62


Dry Cajun line Descriptive Statistics: Results
The results of some basic statistical tests performed at 11:34 on 24-JAN-2010.
20 data points were entered:
8.0 10.5 11.0 11.5 12.0 12.0 12.5 13.0 13.0 13.0 13.0 13.0 13.0 13.5 14.0 14.0 15.0 15.0 15.0 15.5
Mean = 12.9
95% confidence interval for actual Mean: 12.04 thru 13.71
Standard Deviation = 1.78
Hi = 15.5 Low = 8.00
Median = 13.0
Average Absolute Deviation from Median = 1.23

Wet Trilene big game Descriptive Statistics: Results
The results of some basic statistical tests performed at 11:42 on 24-JAN-2010.
20 data points were entered:
7.00 8.00 8.00 8.50 8.50 9.00 9.00 9.00 9.50 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.5 13.5 14.0 15.0
Mean = 9.97
95% confidence interval for actual Mean: 9.025 thru 10.92
Standard Deviation = 2.03
Hi = 15.0 Low = 7.00
Median = 10.0
Average Absolute Deviation from Median = 1.32

Wet Zebco Descriptive Statistics: Results
The results of some basic statistical tests performed at 11:49 on 24-JAN-2010.
20 data points were entered:
7.0 7.5 7.5 8.0 8.5 8.5 8.5 9.0 9.0 9.0 9.50 9.5 9.5 9.5 9.5 9.5 9.5 9.5 10.0 10.0
Mean = 8.93
95% confidence interval for actual Mean: 8.521 thru 9.329
Standard Deviation = 0.863
Hi = 10.0 Low = 7.00
Median = 9.25
Average Absolute Deviation from Median = 0.675


Wet Stren Descriptive Statistics: Results
The results of some basic statistical tests performed at 11:54 on 24-JAN-2010.
20 data points were entered:
8.5 8.5 9.0 10.0 10.0 10.0 10.0 10.0 10.5 10.5 11.0 11.0 11.0 11.0 11.5 12.0 13.0 14.0 15.0 15.0
Mean = 11.1
95% confidence interval for actual Mean: 10.19 thru 11.96
Standard Deviation = 1.89
Hi = 15.0 Low = 8.50
Median = 10.8
Average Absolute Deviation from Median = 1.38

Wet Cajun line Descriptive Statistics: Results
The results of some basic statistical tests performed at 11:57 on 24-JAN-2010.
20 data points were entered:
7.0 8.0 8.0 8.5 9.0 9.0 9.5 10.0 10.0 10.0 10.0 10.5 10.5 10.5 11.0 11.0 11.5 11.5 15.0 15.5
Mean = 10.3
95% confidence interval for actual Mean: 9.327 thru 11.27
Standard Deviation = 2.08
Hi = 15.5 Low = 7.00
Median = 10.0
Average Absolute Deviation from Median = 1.40