Asian Long Horned Beetles
Null hypothesis: the number of this invasive species in a Bucks county sample should not differ from the state of Pennsylvania averages.
Alternative Hypothesis: the number of this invasive in Bucks county is different from the state of Pennsylvania averages.
I reject the null hypothesis that there is no difference in this number of invasive species between Bucks County sample and the state of Pennsylvania averages. This is because Z-score of the given sample is -7.7519 which falls outside of the critical value of +/- 1.96.
Emerald Ash Borer Beetle
Null hypothesis: the number of this invasive species in a Bucks county sample should not differ from the state of Pennsylvania averages.
Alternative Hypothesis: the number of this invasive in Bucks county is different from the state of Pennsylvania averages.
I reject the null hypothesis that there is no difference in this number of invasive species between Bucks County sample and the state of Pennsylvania averages. This is because Z-score of the given sample is 9.249 which falls outside of the critical value of +/- 1.96.
Golden Nematode
Null hypothesis: the number of this invasive species in a Bucks county sample should not differ from the state of Pennsylvania averages.
Alternative Hypothesis: the number of this invasive in Bucks county is different from the state of Pennsylvania averages.
I reject the null hypothesis that there is no difference in this number of invasive species between Bucks County sample and the state of Pennsylvania averages. This is because Z-score of the given sample is 2.47 which falls outside of the critical value of +/- 1.96.
In conclusion, all of these samples reject the null hypothesis.This means that something is happening in Bucks county that makes it less habitable for these invasive species.
3.
Null hypothesis: The number of people per party has no difference in the intervening years.
Alternative hypothesis: The number of people per pasty has a difference in the intervening years.
t-score: 4.92
The corresponding probability value for the t-score is 1.711 for a one tailed test at 95% confidence level.
4.
In this assignment I have been hired by the tourism board of Wisconsin to analyze the concept of "Up-North." Northern Wisconsin is home to many cabins and is where many go to vacation for the summer. Being able to understand aspects of tourism of Northern vs. Southern Wisconsin could lead to better marketing and planning for such activities.
Fishing is the focus of this analysis. Fishing is an activity many people partake in, northern and southern Wisconsin may have a difference in who and how many people are fishing there.
Methods
The State of Wisconsin provided a broad data set (SCORP) where 3 different variables were to be chosen. The chosen variable are state fishery areas, non-residential fishing licenses, and residential licenses.\
A shapefile of Wisconsin was obtained from the U.S. Census FactFinder. This shapefile was joined with the given dataset table. The 3 variables are broken down using natural breaks into 4 classes for statistical analayis and mapping.
These classes were added as another field and exported as a dBASE table for use with SPSS.
SPSS was used to run a chi-squared analysis of the 3 variable data against the northern vs. southern data. Chi-square tests whether or not observed values differ from expected values. All three variables were calculated at a 95% confidence level to determine significance.
Results
Tourism in northern Wisconsin (Figure 1) proves not to be a different than the South, except for resident fishing licenses. State Fishery Areas (Figure 2) show that there are a few hot spots for fishing around Wisconsin, but it is not limited to the north. This is further backed up with a chi-square test that fails to reject the null hypothesis that there is not a difference between the north and south. With a significance value of .192, it is greater than .05, there is not a significant difference between the northern and southern acres of Wisconsin State Fishery Area locations.
The number of non-resident fishing licenses (Figure 3) shows popularity in both northern and southern counties. While the north may look like it has a lot more non-residential fishing going on, it is not a significant amount. The result for the chi-square fails to reject the null hypothesis that there is not a difference between non-residential fishing licences in northern and southern Wisconsin. At a 95% confidence interval, the significance value is .144. This number is greater than .05, which supports that there is no difference.
The number of residential fishing licenses per county shows different story (Figure 4). The map shows looks as though there is more residential fishing going on in southern counties. This is supported by a chi square test that rejects the null hypothesis that there is no difference between northern and southern counties. Tested at a 95% confidence level, the significance value is just less than .05 at .049. This means that there is a significant difference between residential fishing licenses, and the map shows that there seems to be more in the south.
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| Figure 1 |
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| Figure 2 |
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| Figure 3 |
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| Figure 4 |
Conclusions
The tourism for fishing in Wisconsin does not differ between the north and south, only residential fishing does. This could be because there is not a significant difference between State fishery areas. If I could further investigate this, I would try and find if population per county and residential fishing licences correlates.
