Stat Project Essay Example for Free

Stat Project Essay In order to figure out how variables relates to each other and the connections among the variables, or one can predict the other. I will choose three quantitative variables or two quantitative variables and one categorical variable on each pairs. I will also use graphs of scatter plots; regression and correlation to understand that how one variable affect other two variables. There are six groups below: Group one: High School Percentile (HSP), Cumulative GPA (GPA), and ACT Composition Score (COMP) a) HSP vs GPA b) HSP vs COMP c) COMP vs GPA From graph a, we can find out that there is moderate positive liner relationship between HSP and GPA; the correlation is 0. 552; the equation of regression is GPA=0. 0163928*HSP+1. 84804; the slope is 0. 0163928 which is positive; when the predictor variable HSP increase, the response variable GPA also moderately increase; for instance, when HSP increase by 1, GPA will increase 0. 0163928. From graph b, there is also weak positive liner relationship between HSP and COMP scores; the correlation is 0. 357; the equation of regression is COMP=0. 069129*HSP+18. 3131; the slope is 0. 069129 which is positive; when the predictor variable HSP increase, the response variable COMP scores also weakly increase; for example, when HSP increase by 1, the COMP scores will increase 0. 069129. From graph c, there is another weak positive liner relationship between COMP scores and GPA; the correlation is 0. 342; the equation of regression is GPA=0. 0524047*COMP+1. 243; the slope is 0. 0524047 which is positive; when the predictor variable COMP increase, the response variable GPA also weakly increase; for example, COMP scores increase by 1, the GPA will increase 0. 0524047. Based on the graphs and data which I got, I think there are only a little relation among HSP, COMP scores and GPA. I can find out a student with high HSP, has high GPA and high COMP scores; the student with high COMP scores has high GPA. Group Two: ACT math score (MATH), ACT English score (ENGLISH) and Cumulative GPA (GPA) a) MATH vs GPA ) ENGLISH vs GPA c) ENGLISH vs MATH From graph a, we can see that there is weak positive liner relationship between MATH scores and GPA; the correlation is 0. 307; the equation of regression is GPA=0. 0395427*MATH+2. 12892; the slope is 0. 0395427 which is positive; when the predictor variable MATH scores increase, the response variable GPA also weakly increase; for instance, when MATH increase by 1, GPA will increase 0. 0395427. From graph b, there is also weak positive liner relationship between ENGLISH scores and GPA; the correlation is 0. 45; the equation of regression is GPA=0. 0411408*ENGLISH+2. 11295; the slope is 0. 0411408 which is positive; when the predictor variable ENGLISH scores increase, the response variable GPA also weakly increase; for example, when ENGLISH scores increase by 1, the GPA will increase 0. 0411408. From graph c, there is moderate positive liner relationship between ENGLISH scores and MATH scores; the correlation is 0. 475; the equation of regression is MATH=0. 440334*ENGLISH+13. 2567; the slope is 0. 40334 which is positive; when the predictor variable ENGLISH scores increase, the response variable MATH scores also weakly increase; for instance, when ENGLISH scores increase by 1, the MATH scores will increase 0. 440334. According to the graphs and data, I can find out a student who has high English score and Math score also has high GPA and the student with high English score has high Math score. Group Three: Cumulative GPA (GPA), age (AGE) and Total Credits Earned (CREDITS) a) AGE vs GPA b) CREDITS vs GPA c) AGE vs CREDITS From graph a, we can see that there is a weak negative liner relationship between AGE and GPA; the correlation is -0. 103; the equation of regression is GPA=-0. 0240245*AGE+3. 55195; the slope is -0. 0240245 which is negative; when the predictor variable AGE increase, the response variable GPA will weakly decrease; for instance, when AGE increase by 1, GPA will decrease 0. 0240245. From graph b, there is weak positive liner relationship between CREDITS and GPA; the correlation is 0. 106; the equation of regression is GPA=0. 00141886*CREDITS+2. 94831; the slope is 0. 0141886 which is positive; when the predictor variable CREDITS increase, the response variable GPA also weakly increase; for example, when CREDITS increase by 1, the GPA will increase 0. 00141886. From graph c, there is a strong positive liner association between AGE and CREDITS; the correlation is 0. 668; the equation of regression is CREDITS=11. 7475*AGE-174. 356; the slope is 11. 7475 which is positive; when the predic tor variable AGE increase, the response variable CREDITS also strongly increase; for instance, when AGE increase by 1, the CREDITS will increase 11. 7475. There are some outliers may affect the correlation. Based on the graphs and data above, we can find out a student who is older with a litter lower GPA, but has very higher credits; the student with higher credits also has high GPA. Group Four: ACT English Score (ENGLISH), ACT Composition Score (COMP) and Age (AGE) a) AGE vs ENGLISH b) AGE vs COMP c) ENGLISH vs COMP From graph a, we can see that there is a weak negative liner relationship between AGE and English scores; the correlation is -0. 042; the equation of regression is ENGLISH=-0. 0814809*AGE+24. 469; the slope is -0. 814809 which is negative; when the predictor variable AGE increase, the response variable English scores will weakly decrease; for instance, when AGE increase by 1, GPA will decrease 0. 0814809. From graph b, there is weak negative liner relationship between ENGLISH scores and COMP scores; the correlation is-0. 038; the equation of regression is COMP=-0. 0584814*AGE+24. 6029; the slope is -0. 0584814 which is neg ative; when the predictor variable CREDITS increase, the response variable GPA also weakly increase; for example, when AGE increase by 1, the COMP scores will decrease 0. 0584814. From graph c, there is a strong positive liner association between ENGLISH scores and COMP scores; the correlation is 0. 843; the equation of regression is COMP=0. 65656*ENGLISH+8. 43327; the slope is 0. 65656 which is positive; when the predictor variable ENGLISH scores increase, the response variable COMP scores also strongly increase; for instance, when ENGLISH scores increase by 1, the COMP scores will increase 0. 65656. According to the graphs and data above, we can find out a student who is older with a litter lower English and Comp scores; the student with higher English score has very high Comp score. Group Five: Quantitative variables High School Percentile (HSP), Age (AGE) and a categorical variable Sex (SEX) a) HSP vs GPA (both sex) b) HSP vs GPA (males) c) HSP vs GPA (females) From graph a, we can see that there is a moderate positive liner relationship between HSP and GPA; the correlation is 0. 552; the equation of regression is GPA=0. 0163928*HSP+1. 8408; the slope is 0. 0163928 which is positive; when the predictor variable HSP increase, the response variable GPA will moderate increase.

Comparing Social Classes in Toni Morrisons Recitatif and Guy de Maupas

Comparing Social Classes in Toni Morrison's Recitatif and Guy de Maupassant's The Necklace  Ã‚   Toni Morrison's "Recitatif" and Guy de Maupassant's "The Necklace" portray social classes according to the influence of the narrator. Therefore, the overview of the presented classes is biased. Although "Recitatif" and "The Necklace" provide images of several different classes, the class level of the narrator conveys generalizations about each of the respective class levels relative to the story. While the society level of the narrator of "The Necklace" is fairly obvious through careful reading, the social status of Twyla, the narrator of "Recitatif" is directly stated. Twyla's husband, Josh, is a firefighter. Therefore, he is a member of the working, middle class society. Their extended household lives in an average neighborhood and the family members lead common lives. Although the main character of "The Necklace" is also a member of the middle class, the narrator belongs to a wealthier society. This is evident through the narrator's description of Mathilde. For example, the very first sentence of "The Necklace," "She was one of those pretty and charming girls who are sometimes, as if by a mistake of destiny, born in a family of clerks" (67), indicates that Mme. Loisel is shallow and self-centered. Although the narrators of "Recitatif" and "The Necklace" are from different classes, each employs similar methods to create sympathy for their respective society and malevolence toward the class of the story's antagonist. Although Twyla and Roberta both display several character flaws in "Recitatif," Roberta is the ultimate wrongdoer. Twyla and Roberta begin having problems with their friendship followin... ... of fiction. Though frequently overlooked, this factor often affects a story's plot with as much of an impact, if not more, as the setting and point of view. Generalizations regarding the different levels of society are subtly intertwined with other important facts. The society of a short story's narrator, whether the narrator is an active character or outside the story, is an influential factor concerning the presentation of the different classes in the story, therefore directly affecting the plot. Works Cited de Maupassant, Guy. "The Necklace." Understanding Fiction. 3rd ed. Eds. Cleanth Brooks and Robert Penn Warren. Englewood Cliffs, NJ: Prentice Hall, 1979. 67-74 Morrison, Toni. "Recitatif." New Worlds of Literature: Writings from America's Many Cultures. 2nd ed. Eds. Jerome Beaty and J. Paul Hunter. New York: Norton, 1994. 210-225   

Explore the impact of social, cultural and historical on your play

The historical, social and cultural influenced our play immensely because in order to create an understanding of our stimulus gender we needed to explore different sides to gender. We decided that in order for the audience to work out the meaning behind our play they needed to fully understand the idea of gender both past and present. During the creation of our play we thought it was important to bring up certain topical issues, we decided the main issues would be how women were treated in the past and present, domestic violence, love and relationships, stereotypes and social roles. We chose the idea of ‘lion tamers' because it was based on a circus however we twisted it so the scene was ‘lady tamers' to show how women were treated in the past. We started off with the ladies as wild animals with the men in the middle shouting out the orders such as ‘wash the floor'. Then we went onto more advanced techniques were we stood in a ‘Stepford wife pose' and spoke lines such as ‘have dinner ready, plan even the night before so he can have a warm meal on time. In order to create a realistic impression of what women's roles were in the past we looked at The Good housewife's guide this was a booklet of instruction from the 50s telling young women how to be a good housewife and because some of the instruction were incredibly †¦Ã¢â‚¬ ¦ and unrealistic which we thought the audience would find humorous. We then decided to do go the other extreme with the scene where the ladies take over, we wanted the women to be powerful both mentally and physical this was to show how times have changed and in particular how women in society have changed. A very important issue we wanted to concentrate on was domestic violence because this was a hard hitting issue involving gender. We researched different aspects of domestic violence and found that it is equally common nowadays as it was in the past and although it isn't necessarily true for every case men seem to be the†¦Ã¢â‚¬ ¦ of the problem. We had a real problem incorporating this issue into the initial idea of the circus because we didn't know how to make a serious topic humorous. Eventually we came up with a Punch and Judy sketch and this was Perfect because it showed the issue of domestic violence and stuck to the idea of the circus. As a group we decided that it would be nice to look at the positive side of gender because up till then it had all been negative. We used the idea of a tight rope act to show the ups n downs of relationships and love, we had a boy at one end of the tight rope and a girl at the other and they would walk along and take turns saying what they didn't like about each other and the other person would begin to wobble on the tight rope however after each little speech they would end it with ‘but I love him/her. ‘ We wanted to embrace the opposite sexes differences and make the audience view gender in a positive light.

How to Convert Nanometers to Meters Example Problem

This example problem demonstrates how to convert nanometers to meters, or nm to m units. Nanometers are a unit most commonly used to measure wavelengths of light. There are one billion nanometers (109) in one meter. Nanometers to Meters Conversion Problem The most common wavelength of the red light from a helium-neon laser is 632.1 nanometers. What is the wavelength in meters? Solution:1 meter 109 nanometersSet up the conversion so the desired unit will be canceled out. In this case, we want m to be the remaining unit.distance in m (distance in nm) x (1 m/109 nm)Note: 1/109 10-9distance in m (632.1 x 10-9) mdistance in m 6.321 x 10-7 mAnswer:632.1 nanometers is equal to 6.321 x 10-7 meters. Meters to Nanometers Example Its a simple matter to convert meters to nanometers using the same unit conversion. For example, the longest wavelength of red light (almost infrared) that most people can see is 7.5 x 10-7 meters. What is this in nanometers? length in nm (length in m) x (109 nm/m) Note the meters unit cancels out, leaving nm. length in nm (7.5 x 10-7) x (109) nm or, you could write this as: length in nm (7.5 x 10-7) x (1 x 109) nm When you multiply powers of 10, all you need to do is add together the exponents. In this case, you add -7 to 9, which gives you 2: length of red light in nm 7.5 x 102 nm This  may be rewritten as 750 nm. Quick Tips for Nanometers to Meters Conversion Remember, if youre working with exponents, you simply add 9 to the meters value to get the answer in nanometers.If you write the number out, move the decimal point nine places to the left to convert nanometers to meters or to the right to convert meters to nanometers.