Jeff Richard's Blog

Project 5 – Internet Usage With Regard to Income in the US

jeffrichardmth322s08 — Mon, 11 May 2009 16:38:38 +0000

Internet Usage With Regard to Income in the US

Team – - Jason Motta, Scott O’ Meara, and Jeff Richard

All work is posted on Jason Motta’s Blog.

Chapter 7 – Dungeness Crab Growth

jeffrichardmth322s08 — Mon, 11 May 2009 16:03:42 +0000

Dungeness Crab Growth
Dungeness crabs are commercially fished between December and June along the Pacific coast of North America. In the U.S. waters, nearly the entire adult male Dengeness crab population is fished each year. Female crabs are not fished in order to maintain the viability of the crab population. The data for this lab was collected as part of a study of the adult female Dungeness crap. Two sets of data are provided. The first consists of premolt and postmolt widths of the carapaces(shells) of 472 female crabs. This data is a mixture of some lab data and some capture-recapture data. They were obtained by scientists and commercial fisheries over three fishing seasons.

Figure 1: This figure shows the Premolt with Postmolt size.

Figure 2: This figure shows the predicted premolt vs. the actual postmolt.

Figure 3: Shown above is the actual data plotted against a line forced through zero.

Figure 4: This figure shows the subset of data collected for crabs with postmolt carapace width between 147.5 and 152.5mm. After taking a ample from a smaller group of crabs, its shows that it has a greater error than the whole group.

Chapter 4- Patterns In DNA

jeffrichardmth322s08 — Mon, 27 Apr 2009 18:04:40 +0000

Chapter 4 – Patterns in DNA

The first investigation was to show the random uniform scatter of the palindromes across the DNA. To show this, a random number generator was used. Two sets of random data were created, all having 296 numbers between 1 and 229,354. This data simulates the 296 palindrome locations.

Figure 1: This Figure shows the locations of the Palindromes and the locations of the random date.

Figure 2: This figure shows the breakdown of the Palindromes.

Figure 3: This figure shows the breakdown of the first set of Random date used.

Figure 4: This Figure shows the breakdown of the second set of Random data used.

The next part of this experiment was to look at the Poisson Process. The Poisson process is a collection of random variables, where N(t) is the number of events that have occurred up to time t (starting from time 0). The number of events between time a and time b is given as N(b) − N(a) and has a poisson distribution. Each realization of the process {N(t)} is a non-negative integer-valued step function that is non-decreasing, but for intuitive purposes it is usually easier to think of it as a point pattern on [0,∞)].

For our experiment the poisson process is a good reference model because it is a natural model for uniform random scatter. The strands of DNA can be thought of as a line and the location of the palindrome can be thought of as a point on that line. Palindromes are scattered randomly and uniformly across the DNA.

The number that I got using Matlab was equal to 1.034783164, so that means that to the right of that number is the area.

Chapter 2-Video Games

jeffrichardmth322s08 — Mon, 06 Apr 2009 16:15:39 +0000

Chapter 2: Who Plays Video Games

The purpose of this exercise was to take a group of people and see who plays video games, when they play, what demographic plays, etc. The Stats Lab book provided us with this data for the survey, 95 of the 314 students in the class took the survey and completed surveys were obtained from 91 of the 95 students.

In statistics, interval estimation is the use of sample data to calculate an interval of possible values of an unknown population parameter, in contrast to point estimation, which is a single number. In statistics, a confidence interval (CI) is an interval estimate of a population parameter. Instead of estimating the parameter by a single value, an interval likely to include the parameter is given. Thus, confidence intervals are used to indicate the reliability of an estimate. How likely the interval is to contain the parameter is determined by the confidence level or confidence coefficient. Increasing the desired confidence level will widen the confidence interval. I used Mathimatica to prove this:

95% Confidence Interval:

MeanCI[{0.1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 5, 14, 14, 30}]

{1.35992, 5.29302}

99% Confidence Interval:

MeanCI[{0.1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1.5, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 5, 14, 14, 30}, ConfidenceLevel -> 0.99]

{0.684505, 5.96844}

The following estimate is for the average amount of time spent playing video games:

95% Confidence Interval:

MeanCI[{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1,
1.5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 5, 14, 14, 30}]

{0.450992, 2.0077}

99% Confidence Interval

{0.198422, 2.26027}

Figure 1: This figure shows the data for people that played video games daily, a week prior to the survey and for how many hours.

Figure 2: This figure shows the data for people that played video games weekly, a week prior to the survey and for how many hours.

Figure 3: This figure shows the data for people that played video games monthly, a week prior to the survey and for how many hours.

Figure 4: This figure shows the data for people that played video games in a semester, a week prior to the survey and for how many hours.

Figure 5: This pie graph shows what percentage of the group found video games educational and which ones did not.

Figure 6: This Figure shows where the majority of people play video games.

Legned:

1=Arcade
2=Home on a system
3=Home on a computer
4=Home on computer and system
5=Arcade and Home(system or computer)
6=Arcade and home (both system and computer)

Figure 7: This figure shows the amount of girls that play video games during this survey

Legend:

1=Never played

2=Very much

3=Somewhat

4=Not really

5=Not at all

Figure 8: This figure shows the amount of girls that play video games during this survey

Legend:

1=Never played

2=Very much

3=Somewhat

4=Not really

5=Not at all

Figure 9: This figure shows the people that worked for pay and the people that didn’t work for pay the week before the survey.

Figure 10: This figure shows the amount of people like video games and dont own a computer.

Legend:

1=Never played

2=Very much

3=Somewhat

4=Not really

5=Not at all

Figure 11: This figure shows the amount of people that play video games that own a computer.

Legend:

1=Never played

2=Very much

3=Somewhat

4=Not really

5=Not at all

Figure 12: This graph shows the amount people that expect to get a grade of an ‘A’ in the course.

Legend:

1=Never played

2=Very much

3=Somewhat

4=Not really

5=Not at all

Figure 13: This pie chart shows the grade that they think they will obtain in the class.

Figure 14: This pie chart shows the types of video games the students in the survey like to play

Figure 15: This pie charts shows the percentages/reasons why people don’t like to play video games.

Figure 16: This pie chart shows the percentages/reasons why people play video games.

MTH332_Assignment1

jeffrichardmth322s08 — Tue, 03 Feb 2009 21:46:25 +0000

In this experiment we are looking at birth weights of babies that were born to mothers that were smokers and nonsmokers. We are going to be looking at total number of babies that were born, the total number of babies that were born to smokers and nonsmokers, the average of the birth weights in each category, the middle number of the birth weights, the standard deviation, and the skewing of each category. Skewness is a measure of the degree of asymmetry of a distribution. If the left tail (tail at small end of the distribution) is more pronounced that the right tail (tail at the large end of the distribution), the function is said to have negative skewness. If the reverse is true, it has positive skewness. If the two are equal, it has zero skewness. If you look at all the graphs below the skewness is negative on all them. According to the data, in our numbers, there are more babies that are born to nonsmoking mothers than to mothers that smoke. Standard Deviation is a measure of the dispersion of data about a mean value. A low standard deviation indicates that the data is clustered around the mean, whereas a high standard deviation indicates that the data is widely spread with significantly higher/lower figures than the mean. The standard deviation is pretty low, staying around 17-19 depending on what graph you are looking at. With that in mind that means that the standard deviation is staying right around the mean. The average weight of babies born to nonsmoking mothers is generally higher than that of babies that are born to smoking mothers.

Additional Data

Sum

147797

Mean

120

Median

115

Standard Dev.

18.23645

Skew

-0.14082

Weights	Babies
50	0
60	2
70	6
80	21
90	41
100	104
110	183
120	278
130	281
140	175
150	93
160	36
170	11
180	5
Sum	1236

Figure 1.

This graph above shows the babies born between both groups of women, smoker and nonsmokers.

NONSMOKERS

Weights	Babies
50	0
60	1
70	3
80	9
90	14
100	35
110	91
120	165
130	190
140	135
150	61
160	24
170	9
180	5
Sum Skew	742 -.18736
Standard Dev.	17.39869

Figure 2.

The graph above shows the number of babies that were born to nonsmokers.

SMOKERS

Weights	Babies
50	0
60	1
70	3
80	12
90	26
100	69
110	90
120	112
130	89
140	40
150	30
160	10
170	2
180	0
Sum Skew	484 -.0337
Standard Dev.	18.09895

Figure 3.

The graph above shows the number of babies that were born to smokers.

Figure 4.

The graph above is a comapison between babies born to smokers and nonsmokers.