WEKA Analysis: Machine Learning Techniques

However, we must perform some analysis on this data to confirm that the results for our class really are significantly different from the average. To do this, we performed a chi-square analysis on our data. Our chi-square value is 14.733, and the degrees of freedom are 6. The resulting p-value falls between 0.05 and 0.02. Therefore, we conclude that the data from our class indeed does not fit the average.

The lab uses the measurements of a wooden dowel in length and diameter to collect data in order to interpret data in report form. The data is used to produce statistical data and how to correctly present it. A ruler and micrometer were used to measure the dimensions. Spreadsheets are then constructed in order to generate standard deviation, mean, median, mode, frequency, as well as variation of length, diameter, volume, and cross sectional area of the

This paper explores the documentary Training Rules directed by Dee Mosbacher and Fawn Yacker, who released the film in March 2009. This film is about how women’s collegiate sports dealt with homophobia in the league, and it focuses primarily on Pennsylvania State University’s Rene Portland, who was the women’s basketball coach. Under her coaching, she had a “No Lesbians” policy that all the girls had to accept or else they risk being kicked off the team and losing their scholarship. This documentary recounts the instances where some of her players felt discriminated against when they just wanted to play basketball, and their testimonies tell how the university’s basketball program came under fire because of sexual orientation discrimination.

Statistical results of the data analysis have been received by using the Gauss curve, as preferred distribution function, and the

Iterations of analysis eliminated data points that were listed as “unusual observations,” or any data point with a large standardized residual. After 5 iterations, the analysis showed improved residual plots. Randomness in the versus fits and versus order plots means that the linear regression model is appropriate for the data; a straight line in the normal probability plot illustrates the linearity of the data, and a bell shaped curve in the histogram illustrates the normality of the data.

This is a test of your ability to locate information and identify data accurately. The test contains a number of graphs and tables that present detailed information. Each is followed by one or more questions that can be answered by identifying the correct information in the graph or table.

The comprehensiveness and appropriateness in the data from exhibit 12a and 12b for the segmentation analysis are very accurate and on point. The questions asked in exhibit 12a, the segmentation variables, are really right

After the end of WWI the roaring 20’s came to life, the economy was booming, and the U.S. felt a sense of pride. Sadly though all was not as grand as it seemed the land was stained with blood from racial issues. In the book The Learning Tree by Gordon Parks a coming to age story Newt Winger tells the tale of “how it feels to be black in the white man’s world.” He faces the struggle of the racial issue as he grows up in his little town of Cherokee Flats. The young twelve year old lives in a society where blacks are slowly being integrated but are still left with the shortest straw; not being allowed to eat or work in some places and the high schools black students are not allowed on the football nor the baseball teams while a certain teachers

The goal of the feature extraction and selection is to reduce the dimension of the data. In this experiment the dimension of the AVIRIS and HYDICE images reduced to 20 from 220 and 191 respectively using PCA. From the PCA analysis we can see that image of principal component 1 is brightest and sharpest than other PCA image which is illustrated in figure-2.

Table 5 illustrates the cross over frequency for our individual, group, and section data based on the formulas listed in the methods. The individual and section data turned out to be very similar, while the group data was about 9 percent higher. We also used the formula for the mapping distance as listed above to calculate the map units. Again, the individual and section data are similar, while the group data is about 4.5 map units higher. Based on these results, the individual, section and class data are all proven to be very similar while the group data is slightly higher.

Class data will be collected and averaged. Make a graph of your data on the graph paper provided below. (4

First I would approach Latinka and ask her about her daily routine as well as how much excercise she puts in weekly. Once gathered this information I would analyse it and then dicuss options with Latinka reguarding her weight. We would talk together to come to a goal. Once we set the goal, we will then decide what the best way to approach her "goal" would be by applying the options I gave her. I as a nurse would give her charts to abide as well as other options to reach her goal. During this process I would make sure she undestood the information I was telling her by evalutating her and her "learning style." Also, making her feel the upmost comfort through this process, leading her to her healthy and goal weight while making sure she does

The Pika lives near the mountain areas. They eat different types of grasses, weeds, and thistles but their favourite food is grass. The Pika’s special and distinctive features are that they have no tail and they have a very tiny body size. You don’t really need to worry about them because they are perfectly fine. They have fur to keep them warm.

Every morning I walk through the school hallways before class with pride, the love of my life in hand. Her name is Aika, meaning ‘love song’ in Japanese. One of the first friends I’d made in elementary school, our relationship continued into high school where we kindled an intense love for each other, unmatched by any high school couple. But as intimate as we are, nothing about our relationship comes naturally.

Support Vector Machine (SVM) is primarily a classier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables. For categorical variables a dummy variable is created with case values as either 0 or 1. Thus, a categorical dependent variable consisting of three levels, say (A, B, C), is represented by a set of three dummy variables: