Informative Speech Essay - 639 Words

Introduction: I. Sports icons like; Usain Bolt, Michael Jordan, Lolo Jones, Maria Sharapova, Walter Payton, Cristiano Ronaldo and Muhammad Ali all have a common ability that we all possess and that’s the gift of running. [Visual #1/ 30sec.] II. My experience as a marine opened my eyes to how important running is. From running 5miles every morning and in turn accomplishing more than I’ve been set out to do that day. I stand before you as a civilian now and though my surrounding may have changed I still run. [Visual #2/ Pics.] III. My theory is why distance yourself from the athletes and celebrities you watch and applaud on TV, when we all have the same physical capability to run. IV. Running isn’t just a random activity†¦show more content†¦4. Overcoming the obstacles of running equips you with a better focus and determination for your life.[Ref.#2] Conclusion I. Sometimes we have the best intentions for ourselves especially when it comes to our health. II. Yet an estimation of 2,600 daily deaths in the U.S result from an inactive physical lifestyle. [Ref.#3] III. Why live a life leading towards chronic diseases and premature deaths. IV. Many excuses are made for reasons why he or she can’t run, but think about it. Your life may be yours to live but who are you influencing? Who and what are you living for? So why not prolong the gift of life. V. It all starts with you because to better your life, you must better yourself. References 1. Ririan Project . 10 Benefits of Running, and How To Do It . Ririan Project , n.d. Web. 11 Feb 2013. lt;http://ririanproject.com/2007/10/22/10-benefits-of-running-and-how-to-do-it/gt;. 2. Little , Charlene . Yahoo! Sports . Running 2012: Top Ten Health Benefits of Running . Yahoo! Inc , 10 May 2012. Web. 11 Feb 2013. lt;http://sports.yahoo.com/news/running-2012-top-10-health-benefits-running-183200080--spt.htmlgt;. 3. Fares, . Healthy Lifestyle. 2,600 daily deaths due to sedentary lifestyle. You may be next!!. WordPress, 23 January 2011. Web. 12 Feb 2013.Show MoreRelatedInformative Speech Essay855 Words   |  4 Pagestype of speech would the speaker choose. Well chapter 13 contents the creative process for informative speaking. What informative speaking is how to choose a focused informative topic, how to conduct a research and informative outline? The chapter also contains how to organize the body, introduction, and conclusion of the informative speech. Lastly chapter 13 con tents explain how to prepare to present the speech and evaluate and informative speech. In order to make a well informative speech the speakerRead MoreInformative Speech912 Words   |  4 Pagesï » ¿Informative Speech: The Causes of Homelessness Subject and Purpose 1. This informative speech on â€Å"The Cause of Homelessness â€Å"is very Inform able and worth listening to, because in today’s economy it could be you or me. Some seem to think homelessness is choice. 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Introduction A. According to the National Center for Education Statistics in a 2013 study, college students changed their major an average of three times. B. Trying to determine a college major is difficult for us. C. Last Spring, I was struggling to decide whether I should continue pursuing a degree in Civil Engineering or change majors to MIS, an interesting major I had recently discovered. I continued to have trouble makingRead Moreinformative Speech758 Words   |  4 Pagesï » ¿Informative Speech with Visual Aids Outline Specific Purpose: To inform my audience about extreme beauty practices in different cultures. INTRODUCTION I. I am pretty sure most of you here have heard the old saying that â€Å"Beauty is Pain.† II. We wake up striving to look better or beautiful everyday more and more. III. That is because as human beings we tend to be very visual and observe the look of a person before finding out who they really are. IV. So, today I will be sharing with you aRead MoreInformative Speech981 Words   |  4 PagesINFORMATIVE SPEECH (David Hurt, COM 108) Low Pay for Women Compared to Men in America How much did you pay for your car? Did you know that the difference between an average man and women’s salary is enough to purchases a new automobile in America. Below I’m going to discuss the pay gaps, Educational aspects with men and women, and why this issue is world wide. In order to better understand this issue of pay between men and women look at your wife’s or husband’s pay and you’ll see theRead MoreInformative Speech1145 Words   |  5 Pagesï » ¿Cheyenne Duran Comm 20 Kelly Glass October 23, 2013 Informative Speech Outline Topic: Gun Control Organizational pattern: Topical Specific purpose: To inform my audience about the controversial subject gun control. Primary audience outcome: I want my audience to know the two sides on the topic of gun control. Thesis statement: Gun control will create laws and licensing to possess a gun to help decrease crime and fatalities, but also people believe gun control is going against our constitutionalRead Moreinformative speech1255 Words   |  6 Pagesï » ¿ Title: Information Speech Outline â€Å"The Evolution of Animation† Subject Code: USEL 23 - Speech Communication Prepared For: Datin Minda Hassan Prepared By: Nur Rabiatul Adawiyah Bt Mohd Nor Kamarudin (1411170007) Class: Oregon Program: ADFP Summer 2014 Preparation Outline Informative Speech â€Å"The Evolution of Animation – From 2D Animation to 3D Animation† General Purpose: To inform Specific Purpose: To inform my audience the evolution of animation. Central

Physical Education Essay - 1726 Words

Physical Education The word philosophy is defined in the dictionary as the search for human life. There are five different aspects of philosophy: metaphysics, ethics, aesthetics, epistemology, and logic. I will be discussing two aspects of philosophy in my paper. I have many different views about education that I will use in my teaching profession and for my long-term goals as a professional educator. The first personal view that I will address is the nature of students. I believe that every student can learn but I also believe that learning occurs at different levels. I am studying the field of physical education and my goal is to teach and inform my students about the importance of physical education, certain health issues and†¦show more content†¦Education is important because children need to know how to come up with the best solution when they are faced with difficult decisions or problems. Students will learn as they are taught. If a child is pushed to do his/her best, then that child will succeed. If a student is always praised, he/she will strive to succeed because that student will know that they are special and that it is very important for him/her to do their best. As a teacher, I hope to get across to my students the health benefits that physically active people will have. With this in mind, I am going to teach them about their bodies and the lifestyles that can shorten their life. I believe that being physically fit can help strengthen a person mentally, spiritually, and emotionally and enable them to feel good about him/herself. A person who is physically fit and feels good about him/her self and their body image will most likely have a positive outlook on life. Teachers must let students know that all that is expected of them is their best. If a student is praised and guided by the teacher, they will succeed and become confident in themselves. Students will share their problems if they can trust the teacher to be supportive and understanding. As a future teacher, I have many beliefs as to how teachers must be to themselves and to their students. First and foremost, teachers MUST love children. If a teacher goes into the education profession and does not love children, they are not only makingShow MoreRelatedThe Diversity Of Physical Education1528 Words   |  7 Pagesdiversity, but especially in the profession of physical education. The diverse population physical educators work with vary in terms of race, socioeconomic standing, gender, ability and health levels (Gallo Matthews, 2003; Goodwin Watkinson, 2000; Greenleaf Weiller, 2005; McNeill, Kreuter, Subramanian, 2006; Sallis Glanz, 2009; Sarkin, McKenzie Sallis, 1997; Taliaferro, Hammond Wyant, 2015). 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In English, MrRead MoreBenefits of Physical Education Essay1424 Words   |  6 PagesBenefits of Physical Education in Elementary and Early Childhood Settings Tracy Linwood PED 212: Foundation of Movement amp; Motor Activities Instructor Carly Davenport June 6, 2012 Benefits of Physical Education in Elementary and Early Childhood Settings The need for quality physical education in elementary schools is an important needed foundation for young children to maintain a future healthy lifestyle. The benefits of having physical education in elementary schools are endless. ForRead MoreThe Pros and Cons to Physical Education Essay1662 Words   |  7 PagesThe Pros and Cons of Physical Education Most of us remember gym class. For many of us, it was the most hated or the most anticipated class. Those that were a little bit heavier or not as athletically inclined may remember the class as a nightmare, while others may remember it as the most exciting time of the day. Either way, physical education was required. Today, however, the necessity of physical education classes is up for debate. Parents, faculty, council members, and even theRead MorePhysical Education : Not Just Walking The Dog1576 Words   |  7 PagesPhysical Education: Not Just Walking the Dog A person might remember in elementary school a certified physical educator instructing the class, for example, on learning to run, balance, stretch, and climbing rope. Then, in spring, a big relay event would happen, and all the students were able to race, or show off the skills they learned through the year. At these events, every student won a prize, whether, first, second, third, or recognition of achievement for each race. Today, many peopleRead MoreThe Importance Of Physical Education On American Schools1412 Words   |  6 Pages The Importance of Physical Education in American Schools Review Hommes, Tiffany J., Bemidji State University, USA Background Many children in America do not receive the needed amount of physical activity needed to maintain a healthy weight. Approximately17% of children are obese (Robert Wood Johnson Foundation, 2016) and 1 in 3 children are overweight or obese (American Heart Association, 2016). This is a local and national emergency that needs to be addressed to keep children healthyRead MoreImportance of Physical Education in Elementary Schools988 Words   |  4 PagesImportance of Physical Education in Elementary Schools Importance of Physical Education in Elementary Schools Chris Williams ECE 315 Terri Malone June 6, 2011 Importance of Physical Education in Elementary School Physical education is sometimes viewed as merely a chance for students to have fun, get the wiggles out. 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Statistics Coursework Free Essays

Coursework This handout provides information about the module’s second coursework. Below, you will find the coursework as well as information about the marking scheme. * The coursework requires you to engage with regression analysis by performing various regressions in Eviews and by commenting on the main results. We will write a custom essay sample on Statistics Coursework or any similar topic only for you Order Now * The aim of the coursework is to test your ability to handle datasets with the use of a specialist software and to provide critical and informative comments on the outcome of the analysis. You are expected to use Eviews for your analysis. The use of any other alternative software should be negotiated with the lecturer; * This coursework is an individual piece of work and accounts for 20 marks * The deadline is: Friday 16th December * Submission: your report should be typed with all tables, graphs and text included in one single doc or pdf file. You can submit the file via Blackboard or you can submit a hardcopy to the reception office. Notice that if you submit online the deadline will be 11. 9pm on Friday 16th December while if you submit a hardcopy the deadline is 4pm on the same day. * Marking scheme * A first class mark is awarded to the work that: * Contains well drawn and clear graphs, tables and regression output; * The analysis is clear, informative, detailed and makes references to economic theory and to technical aspects of regression analysis; * All questions are addressed in a comprehensive way with the use of appropriate graphs and tables. While answering each individual question, the good student will also appreciate that each question is part of the overall investigation of the determinants of labour supply; * The coursework is well presented, written in good English with an accurate use of referencing to external resources * A grade B is awarded to the work that broadly meets the requirements above but that shows some inaccuracies in the analysis and in the presentation of the information; * A grade C is awarded to the work that shows broad knowledge and understanding of the matter. However, the work is likely to reveal only a partial understanding of either the economic and statistical features of the analysis; * A grade D is awarded to the work that shows a very basic understanding of regression analysis. There is evidence of limited ability to use Eviews and, more broadly, to engage in a meaningful and informative analysis of the data; * A fail mark is awarded to the work that shows no understanding of regression analysis and no ability to handle data and to meaningfully interpret the outcome from the regression analysis. Please turn page for the coursework text The government is interested in understanding what factors affect a household’s annual expenditures on food. Previous research and evidence from various pressure groups suggest that factors such as household income, household size and number of children all play a role in explaining annual household expenditures. You have been asked to produce a report that investigates the determinants of household’s food expenditures and to present the report to local policymakers. To carry out your analysis you have been supplied with the dataset ‘foodexpenditures. wf1’ available in the Coursework folder on Blackboard. The dataset contains data for a random sample of size 300 from a large population of households about the following variables: FOODEXP = expenditures on food (thousands of Euros) HINC = household income (thousands of Euros) HSIZE = number of people living in the household NKIDS = number of children under the age of 18 Use Eviews to address the following tasks that policymakers have asked you to perform: a) Produce scatter-plots of the relationship between FOODEXP and the other explanatory variables. Comment on the graphs. Produce a correlation table and comment on the statistics. (no more than 200 words)(10 marks) b) Regress FOODEXP on HINC only. Conduct a statistical analysis and give your comments. Does the regression outcome fit with your prediction of the relationship between the two variables? Explain. (no more than 300 words)(30 marks) c) Regress FOODEXP on all three regressors. Conduct a statistical analysis and give your comments on the regression outcome. (no more than 300 words)(30 marks) d) Suppose that you are told that an increase of â‚ ¬1000 in the annual household income, while household size and number of children remain unchanged, will on average, lead to an increase of more than â‚ ¬220 in annual food expenditure. Does the evidence confirm this suggestion? no more than 100 words)(10 marks) e) You present the results of your regression analysis to government policy makers. a. In view of the results from your research, what kind of policies would you suggest the government introduces? (no more than 100 words)(5 marks) b. After looking at your research results some policymakers raise some issues about the validity of your research. What kind of criticism do you think they are likely to make? Explain. (no more than 100 words)(15 marks) How to cite Statistics Coursework, Essay examples Statistics Coursework Free Essays string(62) " of TV hours watched per week by the pupils against their IQ\." 1st Hypothesis – For my first hypothesis I will investigate the relationship between the number of TV hours watched per week by the pupils against their IQ. You read "Statistics Coursework" in category "Papers" I am going to use the columns â€Å"IQ† and â€Å"Average number of hours TV watched per week† taken from the Mayfield high datasheet. I think that there will be a relationship between them and will attempt to reveal it. We will write a custom essay sample on Statistics Coursework or any similar topic only for you Order Now 2nd Hypothesis – For my second hypothesis I will investigate the relationship between â€Å"Average number of TV hours watched per week† and â€Å"weight (kg)†. I think that there will not be any major relationship between as they will not affect each other greatly. I will present my analysis and the results in graphs and tables and explain the results using the correlation of the graphs and arrangements of the figures. I will select a number of pupils to base my data on and will use random sampling to ascertain the correct number of male and female pupils needed to make the investigation fair. Stratified Sampling I do not want to use all of the data in the database for my analysis so I will need to take a sample of the number of people in the school. I would like to take about 10% of the overall figure. I will also need to use stratified sampling to make it an equal proportion of the number of males and females in the school to make it fair. The total number of pupils at the school is 813 so I will need to take 10% as my number, 81.3 is rounded down to 81. The overall ratio for boys and girls in the school is: 414:399 Now I will need to do my sampling Males = 414 multiplied by 81 = 41 813 Females = 399 multiplied by 81 = 40 813 Random Sampling Now I have the number of samples I will need to select the samples I will be taking. To do this I will use random sampling. I will take random samples until I have 81. I can do this on Excel using the following formula: = round(round()*120. Once I have gathered the samples I am ready to start analyzing my samples. Analysis Hypothesis 1 Males The first thing I need to do in my analysis is to analyze my graphs which are the source of the investigation. I have created scatter graphs to show the relationship if the two data sources for my first hypothesis. I have separated them into male and female graphs as there is a separation in the numbers. First male scatter graph: This first graph presented a bit of a problem. There was an anomalous result that affected the trend line and the scale of the graph. I decided to create a new graph that didn’t include that 1 piece of data. This way it would help me to analyze the rest of the data. Second male scatter graph: This graph showed the data much clearer and I could then start analyzing it. There is no correlation between the 2 sets of data. This means that it is unlikely that there is a relationship between IQ and Average number of TV hours watched per week. In this it may be that my hypothesis is incorrect. There is only a very slight gradient on the trendline that leans towards a negative correlation, but the gradient is not steep enough to draw any conclusions about the relationship between the two sets of data. I will have to use the cumulative frequency graphs and boxplots to see if any conclusions can be made. Cumulative frequency graphs for IQ and Average number of TV hours watched per week: From these graphs I could create box plots and compare the two sets of data. Before that I analyzed the cumulative frequency graphs to draw initial conclusions. The majority of the IQs for males are between 90 – 105, this shows that the data is quite spread out as this section only covers a small area of the graph. For the TV hour’s graph, again the data is spread among 1 main area; in this case it is between 5-25. There is almost a straight line near the top of the graph; this shows that there is likely to be some anomalous results and 0 pupils in between that result and the main bulk. Now I will create box plots so I can compare the two graphs together. Box plots for cumulative frequency graphs of IQ and average number of TV hours watched per week: (for interquartile ranges look at copies of graphs at the back) From the box plots I can see that the data spread is relatively the same apart from a possible anomalous result in the TV hour’s data. This similarity is the reason why the scatter graph had no correlation and therefore no relationship. This means that my hypothesis is wrong. Hypothesis 1 Females Again I will start with the scatter graphs. As with the male graph I had an anomalous result that spread out the data and scale down the graph so most of the relevant data couldn’t be analyzed. I then did another graph without that specific piece of data. Scatter Graphs 1 and 2 to show the relationship between IQ and average number of TV hours watched per week for Females: As you can see on both the graphs there is no correlation between the two sets of data. This again means that my first hypothesis is unlikely to be correct. There is only a slight gradient on the trend line which is not steep enough to draw any conclusions from it. There is another anomalous result on the graph but it doesn’t affect the trend line and my conclusions so I left it on the graph. I will now crate cumulative frequency graphs to see if they can help me to draw conclusions. Cumulative frequency graphs for the IQ and number of TV hours watched per week: I will now analyze the graphs before drawing box plots to compare the graphs. The IQs graph is much more erratic which means that the data is spread over a larger range. Although there is 1 area where the data is concentrated and the gradient very steep, between 95-105. The TV hours graph is much smoother and the data less spread. The data number of hour’s increases steadily to a certain point then it goes flat until the end. This means that there is a n anomalous result somewhere. I know that it can only be 1 or 2 anomalous because the point where it goes flat is at about 38 and there are only 39 sets of data in the graph. I will now look at the box plots to compare the two cumulative frequency graphs. Box plots for cumulative frequency graphs of IQ and number of TV hours watched for females: The box plots for these graphs show me that the IQ data has a much larger range and that it is quite evenly spread. I can see this because the interquartile range is quite large and the median evenly spread. There may be a few exceptions as 1 pupil is likey to have a very low IQ which is why the lowest value is so low. The TV hour’s data seems to be much more concentrated and the data is generally lower. This shows that there can’t be any relationship between them as they each grouped in certain areas. Also the box plot for TV hours shows that there is likely to bge an anomalous result as the highest value is so far out of the upper quartile. Hypothesis 2 Males In this hypothesis I will be comparing the Average number of TV hours watched per week and Weight, to see if there is any relationship between them. I will again start with Males and the Scatter graphs. Scatter graphs 1 and 2 to show the relationship between Weight and the Average number of TV hours watched per week for males: In these scatter graphs there is a slight negative correlation. This means that as the number of TV hours goes up Weight goes down. This may not be an accurate graph as there are a few anomalous results that may have caused the trend line to be that gradient. If this is so my hypothesis would have been correct, if it is not the gradient of the trend line isn’t steep enough to say that it is 100% certain that it is accurate. I will need to use the cumulative frequency graphs to draw complete conclusions. Cumulative frequency graphs for the number of TV hours watched and Weights of males: These two graphs look quite different; the weights graph has most of its data concentrated in the middle of the range, between 30-50 and looks like a normal cumulative frequency curve. Whereas the number of TV hours has most of its data concentrated at the beginning between 0-30, showing that there is likely to be an anomalous result at the end of the range. These anomalous results on the TV hours graph are what caused the slight negative correlation on the trend line. I will be able to make complete conclusions after looking at the female sample and seeing if that graph follows suit. The box plots for these graphs will look quite different and will make it easy to make a simple comparison. Box plots for Cumulative frequency graphs IQ and Weight for males: From the box plots I can see that the two sets of data are almost identical in range which would cause a straight line on the scatter graph it is because of the anomalous results on the TV hours which caused the slight negative correlation. The weights box plot shows me that the data is quite evenly spread in the middle of the range apart from a very heavy person at the end which is why the highest figure is so far apart from the upper quartile. Overall the box plots show me that the similarity in the data means there is no relationship and hypothesis was correct. Hypothesis 2 Females Again I will start with the scatter graphs to show the relationship between Number of TV hours watched and weight. The graphs should be similar to the males and the conclusions the same. Again I had an anomalous result and had to create a second scatter graph without it there. Scatter graphs 1 and 2 to show the relationship between the Number of TV hours watched per week and Weight: The second scatter graph in this section, without the anomalous result completely changed the trend line. The first graph looks a lot more like the male graph whereas the second follows my hypothesis a lot better. In graph 1 there is a slight gradient on the graph which points towards a negative correlation, like those of the male sample. On the graph without the anomalous result there is clearly no correlation whatsoever as the line is nearly horizontal. I will take the results of the male sample to be wrong as I said earlier there are a few anomalous results which caused the trend line to be at that gradient. Now I will look at the cumulative frequency graphs to see what results I get from them. Cumulative frequency graphs for Average number of TV hours watched per week and Weight for Females: As on the males graph the TV hours for females have a lot of anomalous results. But for the scatter graphs I cancelled them all out which gave no correlation. If the line at the top of the TV hours graph is blanked out the two graphs look almost identical. This is why the scatter graph got a near horizontal trend line. The box plots for these to graphs will look alike apart from there will be a much longer line at the end of the TV hours graph because of the anomalous results. Box plots of cumulative frequency graphs for Number of TV hours watched and weights of females: These box plots show me the same as the males did, that the data is almost identical if placed 1 on top of the other. This is what caused the horizontal line in my scatter graphs and proves my hypothesis. Conclusion Hypothesis 1: My first hypothesis has been proved incorrect. The scatter graphs show that there is no correlation between the two sets of data. For my hypothesis to have been correct there would have needed to be a strong positive correlation. The cumulative frequency graphs and box plots again proved my hypothesis incorrect, the similarities in the two sets of data’s box plots showed that there was no relationship and showed why the scatter graphs showed a straight line. Both the male and female samples showed that my hypothesis was incorrect although some anomalous results created a slight negative correlation in both it was obvious that it was still wrong. Hypothesis 2: My second hypothesis was proved correct. The scatter graphs showed that there was absolutely no correlation on the graphs which means no relationship. Although the male graphs did show a a negative correlation it was proved to be made by a few anomalous results by the cumulative frequency and later the inconsistency with the female sample. The female scatter graph showed a near horizontal trend line which was what I needed to prove my hypothesis. The similarities on the cumulative frequency graphs and box plots further proved my hypothesis was correct. Evaluation The investigation went quite well although my first hypothjesis was incorrect it showed that careful analysis of data is needed before drawing conclusions. When I next do an investigation into data I will use histograms to aid me in my analysis as they come in useful when looking for relationships in two sets of data as the cumulative frequency graphs do. I could have made the cumulative frequency graphs a little better as the program I used did not put a scale on the x axis but only the length of the range. How to cite Statistics Coursework, Papers