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.
There are many factors that I can improve on in the future for the next observations. First of all the organization of the data along with the time measurement are improvements that can be made. Instead of measuring five minutes I would measure every 15 minutes, making it four rounds in one hour. Within that hour we can also use a timer so that we don’t lose track of time. Also the time that we had to measure was on a Monday and Wednesday, for the next observation I would add in a Tuesday or Thursday. Since the observation was done on a campus we didn’t get different age ranges besides the college age. By measuring data at a Chic-Fil-A in the public we would get a wider range of ages, genders, and race. The diversity level was also low so in
The student data file was used as the data source. The sample size included one hundred men and one hundred women. Thirty-five out of one hundred men had not declared for a degree. Fifteen out of one hundred women had not declared for a degree. The level of
When looking at figure 1, which is the stem and leaf plot displayed above, we can determine whether or not these data sets are positively skewed or negatively skewed. Both of which have been classified as positively skewed. This is known as on both; the mean is higher than the median, which is important as it tells us that the middle most number is below the average set of records. In this data however, having positively skewed data represents the higher time taken to complete the concentration section of the Census. This can show us that there are areas of the students data students
The given chart provides data about the percentage of personnel's contenment in college A, B, C and D from 1991 to 2002. Overall, there was a unpredictable raise in the figure of college C which changed from the lowest to highest position of the totall given information in the final phase.
A statistical chart was created to analyze novels read my students in a 12th grade Literature class. There were a total of four classes and the teachers of the literature classes were Dr. Adhanom, Ms, Zadnichek, SGT Kulokowski, and Mr. Radoslovich. Each Literature class had a wide variation of students per class ranging anywhere from ten to forty-five students. The point of the statistical model was to show the teacher’s name of the 12th grade Literature class, the number of students in the class, the total novels that were read, and the average of novels that were read per student. Thus, the statistical chart allows people to see what class read the most and least amount of books. In addition, the chart shows an average of books read per students.
My project took a little bit of time, sweat, and hard work but was a success in conserving water. My project is a gutter system, that leads to a rain barrel, which collects water for later use, like watering the garden. The reason I built this project was because my garden is under the eaves so the garden is not able to absorb the water when it rains, and this project solves that. The people who helped me with my project are my mom and brother. They helped me cut and attach the gutters to the roof. The water collection system was built at my house, and I had to go to Home Depot for supplies. My mom drove me to Home Depot where I picked up the supplies to build the gutter system. I assembled the gutter system, but most of the structure was a
Finally, for the last question students were asked about their salary expectations. The results were varied which were : I) 6 students(29%) said 50-60K. II) 5 students(24%) said 60-70K. III) 4 students (19%) said 40-50K. IV) 3 students (9%) said they were going to get an additional engineering degree after this program hence why they had high salary expectation which was 110-120K. V) 2 students(9%) said 70-80K. VI) One student(5%) said 30-40 K. The data is shown on the pie chart
Prior to the write up of this report each student’s height was measured in the laboratory. Each height was rounded off to the nearest centimetre for simplicity. When the data for this investigation was collected and posted online. I examined the data and I created a table to show the frequency of male and female heights in the class sample. These tables then aided me to make a graph to provide a visual of the frequency of the heights of the males and females in the class sample of a population.
I collected my data for my visualization by using a google survey created by me teacher. The survey was not anonymous. It was a survey of general information for the students of the class to describe their day. It also requested information about how the students spent their previous nights, including work, relaxing and free time. The students filled out the survey in there respective periods. All data was collected around the same time. The data was collected from 10/26/16 - 11/3/16.
Application of appropriate data collection method is important to avoid invalid evaluation results after implementation. As the data collection method plays an important role in impacting evaluation, a qualitative data collection method was chosen for this particular study. It is because; qualitative data provides useful information to understand the process behind the observed process and also helps in understanding nurses and patients perception towards the change management. Qualitative data will also help in generating evaluation hypothesis by strengthening the survey questionnaire and other evaluation findings (University of Wisconsin, n.d). In addition to this, as the evaluation method includes interview, observation and documentation,
In this assignment, we were asked to analyze D relationship through data that we either collected or found. The research question that was proposed was, “Is there a difference in hours of class between underclassman and upperclassman?” The participants were asked what year they were in (grade) and how many hours of class they have. I believe that this is a crucial question because if these groups are significantly different then students should be aware that as the years progress they will either have more or less class time. Informing students about the results of this study will allow them with sufficient time to prepare themselves for the upcoming years.
To run my statistical analysis, I created a Google Sheet document with three columns including the student name (1), their percent correct on the December 2014 Acuity Diagnostic test (2), and their scale score for the Spring
Research used in this study involved both qualitative and quantitative aspects, as we collected quality thoughts along with additional data used for statistical analysis. The survey method was the ideal data collection strategy for our analysis because it allowed us to gather information that answered CBG’s questions while ensuring DAI’s specific inquiries were being answered. Once the information was collected, each of the clients’ responses were compared and graphed to present the information in a graphic manner. In order to conduct the surveys, members of the client list were contacted via telephone. We anticipated that a problem might arise while scheduling interview times with clients. Thus, our survey was formatted to be easily answered in person or over the phone, which allowed us to work around the clients’ time constraints.
This research project was accomplished based on the information and data collected from Canadian Community Health Survey (CCHS) 2007. The main purpose of this research project is to discover and compare the high BMI risk among different marital status groups. For the past few years, the growth of obese and overweight individuals has become a huge crisis. Furthermore, the increase in BMI results in cardiovascular problems. According to a recent article, both married male and female have high BMI. There is a direct relationship and positive correlation existing between marital status and BMI. In general, married individuals especially those with kids, who are no longer concerned about attracting a mate and