Twilight by Stephenie Meyer - Book Review

Twilight by Stephenie Meyer - Book Review Theres a reason more than 10 million Twilight series books are in print. Twilight, the first in the series, is the addictive story of two teenagers –- Bella, a regular girl, and Edward, a perfect gentleman, and a vampire. This is the type of book you might read in just a few sittings, becoming engrossed in its fantastical world and oblivious to your physical surroundings. While not the next great thing in modern literature, its a fun book to get lost in and comes to an end much too quickly. Pros Highly entertaining, fast-paced story of romance and suspenseRelatively clean for a teenage vampire love storyThe concept of good vampires is unusual and intriguing Cons The writing can be clunky at timesEdwards perfection can be over-the-top, even for a fictitious super-humanAt times, Edward and Bellas relationship can seem more like that of a father and daughter Description Twilight by Stephenie Meyer was first published in October 2005.Publisher: Little, Brown512 Pages Twilight by Stephenie Meyer: Book Review Twilight is told by 17-year-old Bella Swan, who moves from Phoenix to the small town of Forks, Washington, to live with her dad for the remainder of high school. There, she meets Edward Cullen and his family, who possess an other-worldly and irresistible beauty and grace to which Bella is drawn. Twilight is the tale of Bella and Edwards burgeoning relationship, brimming with standard teenage drama alongside the unexpected, because, after all, Edward and his family are vampires. These undead friends have chosen to deny their urge to drink human blood, instead slaking their thirst with the blood of animals. Bella soon finds out, however, that not all vampires in her life are constrained by such scruples. The book has been praised for its treatment of sexuality and morality. Although theres plenty of yearning and sensuality, there is no sex, drinking, or drug use. Edward refuses Bellas desire to be turned into a vampire herself, on grounds that it wouldnt be the right thing to do. Twilight is an easy and enjoyable read. Its first-person viewpoint keeps the pages turning. This isnt a masterpiece of literary achievement, however. You have to take it for what it is –- a unique and entertaining, if not flawlessly written, story. Twilight will almost certainly appeal to teenage girls and many women of all ages, but probably not to the majority of males. Its sure to make readers eager to devour the next three novels.

Little Skate Characteristics and Information

Little Skate Characteristics and Information The little skate (Leucoraja erinacea) is also known as the summer skate, little common skate, common skate, hedgehog skate, and tobacco box skate. They are classified as elasmobranchs, which means they are related to sharks and rays. Little skates are an Atlantic Ocean species that that live on the ocean bottom. In some areas, they are harvested and used as bait for other fisheries.   Description Like winter skates, little skates have a rounded snout and pectoral wings. They can grow to a length of about 21 inches and a weight of about 2 pounds. The dorsal side of a little skate may be dark brown, gray or light and dark brown in color. They may have dark spots on their dorsal surface. The ventral surface (underside) is lighter in coloration and may be white or light gray. Little skates have thorny spines which vary in size and location depending on age and sex. This species can be confused with the winter skate, which has a similar coloration and also lives in the North Atlantic Ocean.   Classification Kingdom: AnimaliaPhylum: ChordataSubphylum: VertebrataSuperclass: GnathostomataSuperclass: PiscesClass: ElasmobranchiiSubclass: NeoselachiiInfraclass: BatoideaOrder: RajiformesFamily: RajidaeGenus:  LeucorajaSpecies:  erinacea Habitat and Distribution Little skates are found in the North Atlantic Ocean from southeastern Newfoundland, Canada to North Carolina, U.S.   These are a bottom-dwelling species that prefer shallow waters but may be found in water depths up to about 300 feet. They frequent sandy or gravel-covered bottoms. Feeding The little skate has a varied diet that includes crustaceans, amphipods, polychaetes, mollusks, and fish. Unlike the similar-looking winter skate, which seems to be more active during the night, little skates are more active during the day.   Reproduction Little skates reproduce sexually, with internal fertilization. One obvious difference between male and female skates is that males have  claspers  (near their pelvic fins, that lie on each side of the tail) that are used to transfer sperm to fertilize the females eggs. The eggs are laid in a capsule commonly called mermaids purse. These capsules, which are about 2 inches long, have tendrils on each corner so that they can anchor to seaweed. The female produces 10 to 35 eggs per year. Within the capsule, the young are nourished by egg yolk. The gestation period is several months, after which the young skates hatch. They are 3 to 4 inches long when they are born and look like miniature adults.   Conservation and Human Uses Little skates are listed as Near Threatened on the IUCN Red List. They may be captured for food and the wings sold as imitation scallops or for use as other dishes. More often, they are harvested to be used as bait for lobster and eel traps. According to NOAA, that harvest occurs in Rhode Island, Connecticut, Massachusetts, New York, New Jersey, and Maryland. References and Further Information: Bailly, N. 2014. Leucoraja erinacea (Mitchill, 1825). In: Froese, R. and D. Pauly. Editors. (2014) FishBase. Accessed through: World Register of Marine Species.Kittle, K. Little Skate. Florida Museum of Natural History. Accessed February 28, 2015.NOAA Fisheries: Greater Atlantic Region. What Were Doing to Learn More About Skates. Accessed February 28, 2015.Sulak, K.J., MacWhirter, P.D., Luke, K.E., Norem, A.D., Miller, J.M., Cooper, J.A., and L.E. Harris. Identification Guide to Skates (Family Rajidae) of the Canadian Atlantic and Adjacent Regions. Accessed February 28, 2015.Sulikowski, J., Kulka, D.W. Gedamke, T. 2009. Leucoraja erinacea. The IUCN Red List of Threatened Species. Version 2014.3. Downloaded on 28 February 2015.

Gay rights policy Essay Example | Topics and Well Written Essays - 1000 words

Gay rights policy - Essay Example The purpose of this paper is to explore whether such marriages are actually relevant and how far they fit into American federalism. It also explores the way various states view same-sex marriages and the consequences of their laws and enactments. LGBT Rights There is little or nil proof regarding the misconduct of homosexual people. Most of the homosexual couples are ardently religious, are taxpaying citizens and do not indulge in unnecessary violence. Long term studies have proven the children bought up by them are completely capable of living a heterosexual life. Sexual orientation is an object of pure personal taste and choice, rather than a misdemeanour. Hence, it is entirely unacceptable for someone else like the government to intervene in such activities as it is no national, moral or religious threat. History of the Important Homosexuality Acts The total number of households with same-sex partners in the US is estimated to be 2.9 million according the 2000 census. But, it is e stimated the number of gay and lesbian people who do not live as couple or have disclosed themselves might reach up to 29 million, ten times the recorded rate. Homosexuality is viewed differently by each state government based on the dominant religious faith followed in the region. Virginia was the first state to declare same gender sex as a criminal act way back in 1610. The case was similar in many European countries too (Cory, 1951). Such views changed drastically by mid-1900's with the voice of the discriminated like the immigrants and the slaves being heard and honoured by the society. Illinois was the first place to decriminalize homosexuality in 1961. Massachusetts became the first state to elect a gay state legislator in 1967. Massachusetts recognized legalized same-sex marriages in 2003 (Morris, 2013). Such acts gathered both public support as well as agitation. President Bill Clinton signed the Defense of Marriage Act (DOMA) in 1996 which gave the states the authority to l icense or cancel the same sex marriages based on their internal beliefs. DOMA was ruled out on June 26, 2013 by the US Supreme Court. The State Statutes and DOMA The US federalism grants majority rights to states to make up their own laws and retains certain important rights with the federal government. DOMA was signed using this feature, granting the states the autonomy to decide whether they can legalize the same-sex marriages or not. Nearly 10 states recognized the same-sex marriages starting from 2001 one after another and granted equal right to the married homosexual couples. But, most of them did little to ensure the disclosed homosexuals are treated equally or protected against discrimination in terms of employment or receiving benefits. Though removal of DOMA have now legalized such marriage and the same-sex couples are naturally entitled to all the parental benefits the normal citizens can receive including the rights to adopt a child. Previously the rules regarding child a daption varied according to each state. Nearly 20 states in the US consider sexual orientation discrimination as an outlaw. Washington D.C. Evasive Role of the State Governments Hate crimes are also punishable in all states under the federal law according to the Hate Crimes Prevention Act of 2009. Though it applicable to several Hate crimes from bullying in school to disability

Jainism, Sikhism Essay Example | Topics and Well Written Essays - 500 words

Jainism, Sikhism - Essay Example Sikhism is considered to be a new religion in India when compared to that of other religions such as Jainism or Hinduism. The religion has been founded by Guru Nanak. Some of the unique characteristics of the religion are that they do not belief in pilgrimage, superstitions, fasting as well as other such kinds of rites. It tries to provide services to the community and thus tries to extend its help to the ones who need them. The Sikhs are supposed to dress as per the Guru Gobind Singh’s order. According to the religious doctrines, the Sikhs should also wear turbans. Regular pray and meditation is done by means of repeating the name of the God. The Khalsas’ in the Sikh religion are expected to monitor five K’s such as Kaccha, Kara, Kirpan, Khanga and Kes. This religion does not follow any symbolism or ritualism. There are not any altars or idols in the Gurudwara. The fact that every Gurudwara keeps the holy Sikh Scripture, which is also known as Guru Granth Sahib or Satguru, is a unique characteristic (Pecorino, â€Å"Philosophy of Religion†). It can be mentioned that both the religions namely the Sikhism and the Jainism are of the belief that they are inhabitants to the Indian subcontinent. It was found that like Sikhism, Jainism also refused the power of the Vedas and thus developed independent textual norms and traditions that were based upon the words as well as the illustration of their early teachers. It finally evolved complete new ways of communicating their thoughts with the common people (Apex Learning, â€Å"3e Jainism and Sikhism†). The main similarities of both the religions are that both of them commemorate Diwali, a festival of lights. Jains are strictly vegetarians but the Sikhs are non-vegetarians. However, it can be observed that in Gurdwaras, the food that is served is completely vegetarian so that it is capable of obliging all the segments of the

Debut albums Essay Example for Free

Debut albums Essay High School. Those two words can either bring fear or happiness to anyone. Besides college, the most life altering period in one’s lifetime is the four years we must go through to finally reach that milestone of getting a diploma. High school is that time to find yourself. It’s that time where you’re expected to conduct yourself like an adult, but you still get treated like a kid, a time of confusion. Because I cannot speak for everybody’s opinions about high school, I will just share mine. I’ve realized that there is no other place in this world where you will find such a large array of people other than a public high school. You have your jocks, your honors students, your geeks, your goths, your skaters, your princesses (a. k. a. cheerleaders), your drama kids, and then you have those kids that don’t really fit into any other category but â€Å"you know, those kids. † Logically, you would think that there is absolutely no way all of those different types of people could get along, but for some unknown reason we do. School is like a whole other separate community, a business community. The C. E. O. and head honchos are the main office, the teachers are the workers, and we students are merely the entrepreneurs. With that said, I have figured out a reason on why we all get along. Every entrepreneur does what they do for all the same reasons, just like all of us students attend school for the same thing. Of course some of us attend more voluntarily than others but nevertheless, we all show up every morning for the same reason. We show up to learn, to prepare ourselves for the real world and what is to come. Most of us show up to better our chances of succeeding in education after high school. Another thing I’ve learned about high school is that for a place that is supposed to be all good and fun and educational, there sure are a lot of things that aren’t. The food isn’t good, the stressful work isn’t fun, and some classes might be educational but I sure don’t understand why the heck I have to take them, like Introduction to Animal Sciences for example. How is that helping me be prepared for the real world? However, because of these problems in the system of just about every high school, I’ve learned possibly the most valuable lessons of all: Life isn’t fair, you don’t always get what you want, and you can’t change people. The point of me telling you all of this isn’t to say what a great learning experience I’ve had or how I know that my way of life has been shaped by my experiences in school, but rather to point out that you can’t always focus on the differences, or the negatives in life, but instead to realize what you have in common, or what is good.

The Mill on the Floss :: Free Essays Online

The Mill on the Floss George Eliot and The Mill on the Floss: Understanding the Woman and the Work George Eliot was born Mary Ann Evans in 1819. Mary Anne was one of seven children. Eliot often incorporated depictions of her siblings’ and father’s personal characteristics into her literary works. We see her brother Isaac appear as Tom Tulliver in The Mill on the Floss; It is said that her relationship with her brother Isaac is unmatched, even by her father. They had a special bond. That bound was broken when she meets George Lewes in 1854. Lewes was a married man. Eliot fell in love with him anyway. They eloped in 1854. Eliot was ostracized by her society and, perhaps more damaging, her brother refused to speak to her. This had a profound affect on Eliot’s works and her life. The fact that Eliot was involved with Lewes is only one aspect of her life. When doing my research, I was pleased to see that she was an editor at the Westminster Review in 1851. She wanted to be independent, so she decided to take up journalism at the age of thirty-one. After writing critically, she decided to began writing her own work. She published three long stories, which were later published in volume form. These complied works became her first book Scenes from Clerical Life in 1958 (Ashton, 187). Though she had been writing professionally, Mary Ann Evens wasn’t known by George Eliot until 1857. She came up with the pen name â€Å"George Eliot† to elude to the public that she was a clergyman friend of her boyfriend George Lewes. She was forced to come â€Å"out† when Dickens suspected thought she was really a woman. She reveals her true identity to the public in 1959. Her books were successful, but she was consistently aware that her professional work was being judged based on how she decided to live out her personal life. George Lewes dies in 1878. Two years later she marries John Cross who was a family friend. He was twenty years her junior. George Eliot dies in 1885 from kidney problems. The Mill on the Floss is Eliot’s most autobiographical book. The scenery of the book was based on the community Eliot grew up in—Arbury on the outskirts of Warwickshire. Eliot knew that she wanted the story to include a flood. She did research at the London Library.

Regression Analysis

REGRESSION ANALYSIS Correlation only indicates the degree and direction of relationship between two variables. It does not, necessarily connote a cause-effect relationship. Even when there are grounds to believe the causal relationship exits, correlation does not tell us which variable is the cause and which, the effect. For example, the demand for a commodity and its price will generally be found to be correlated, but the question whether demand depends on price or vice-versa; will not be answered by correlation. The dictionary meaning of the ‘regression’ is the act of the returning or going back. The term ‘regression’ was first used by Francis Galton in 1877 while studying the relationship between the heights of fathers and sons. â€Å"Regression is the measure of the average relationship between two or more variables in terms of the original units of data. † The line of regression is the line, which gives the best estimate to the values of one variable for any specific values of other variables. For two variables on regression analysis, there are two regression lines. One line as the regression of x on y and other is for regression of y on x. These two regression line show the average relationship between the two variables. The regression line of y on x gives the most probable value of y for given value of x and the regression line of x and y gives the most probable values of x for the given value of y. For perfect correlation, positive or negative i. e. for r=  ±, the two lines coincide i. e. we will find only one straight line. If r=0, i. e. both the variance are independent then the two lines will cut each other at a right angle. In this case the two lines will be  ¦to x and y axis. The Graph is given below:- We restrict our discussion to linear relationships only that is the equations to be considered are 1- y=a+bx – x=a+by In equation first x is called the independent variable and y the dependent variable. Conditional on the x value, the equations gives the variation of y. In other words ,it means that corresponding to each value of x ,there is whole conditional probability distribution of y. Similar discussion holds for the equation second, where y acts as independent variable and x as dependent variable. What purpose does regression line serve? 1- The first object is to estimate the dependent variable from known values of independent variable. This is possible from regression line. – The next objective is to obtain a measure of the error involved in using regression line for estimation. 3- With the help of regression coefficients we can calculate the correlation coefficient. The square of correlation coefficient (r), is called coefficient of determination, measure the degree of association of correlation that exits between two variables. What is the difference between correlation and linear regression? Correlation and linear regression are not the same. Consider these differences: †¢ Correlation quantifies the degree to which two variables are related. Correlation does not find  a best-fit line (that is regression). You simply are computing a correlation coefficient (r) that tells you how much one variable tends to change when the other one does. †¢ With correlation you don't have to think about cause and effect. You simply quantify how well two variables relate to each other. With regression, you do have to think about cause and effect as the regression line is determined as the best way to predict Y from X. †¢ With correlation,  it doesn't matter which of the two variables you call â€Å"X† and which you call â€Å"Y†. You'll get the same correlation coefficient if you swap the two. With linear regression, the decision of which variable you call â€Å"X† and which you call â€Å"Y† matters a lot, as you'll get a different best-fit line if you swap the two. The line that best predicts Y from X is not the same as the line that predicts X from Y. †¢ Correlation is almost always used when you measure both variables. It rarely is appropriate when one variable is something you experimentally manipulate. With linear regression, the X variable is often something you experimental manipulate (time, concentration†¦ and the Y variable is something you measure. Regression analysis is widely used for  prediction  (including  forecasting  of  time-series  data). Use of regression analysis for prediction has substantial overlap with the field of  machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer  causal relationships  between the independent and dependent variables. A large body of techniques for carrying out regression analysis has been developed. Familiar methods such as  linear regression  and  ordinary least squares  regression are  parametric, in that the regression function is defined in terms of a finite number of unknown  parameters  that are estimated from the  data. Nonparametric regression  refers to techniques that allow the regression function to lie in a specified set of  functions, which may beinfinite-dimensional. The performance of regression analysis methods in practice depends on the form of the data-generating process, and how it relates to the regression approach being used. Since the true form of the data-generating process is not known, regression analysis depends to some extent on making assumptions about this process. These assumptions are sometimes (but not always) testable if a large amount of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However when carrying out  inference  using regression models, especially involving small  effects  or questions of  causality  based on  observational data, regression methods must be used cautiously as they can easily give misleading results. Underlying assumptions Classical assumptions for regression analysis include: ? The sample must be representative of the population for the inference prediction. ? The error is assumed to be a  random variable  with a mean of zero conditional on the explanatory variables. ? The variables are error-free. If this is not so, modeling may be done using  errors-in-variables model  techniques. ? The predictors must be  linearly independent, i. e. it must not be possible to express any predictor as a linear combination of the others. SeeMulticollinearity. The errors are  uncorrelated, that is, the  variance-covariance matrix  of the errors is  diagonal  and each non-zero element is the variance of the error. ? The variance of the error is constant across observations (homoscedasticity). If not,  weighted least squares  or other methods might be used. These are sufficient (but not all necessary) conditions for the least-squares estimator to possess desirable propertie s, in particular, these assumptions imply that the parameter estimates will be  unbiased,  consistent, and  efficient  in the class of linear unbiased estimators. Many of these assumptions may be relaxed in more advanced treatments. Basic Formula of Regression Analysis:- X=a+by (Regression line x on y) Y=a+bx (Regression line y on x) 1st – Regression equation of x on y:- 2nd – Regression equation of y on x:- Regression Coefficient:- Case 1st – when x on y means regression coefficient is ‘bxy’ Case 2nd – when y on x means regression coefficient is ‘byx’ Least Square Estimation:- The main object of constructing statistical relationship is to predict or explain the effects on one dependent variable resulting from changes in one or more explanatory variables. Under the least square criteria, the line of best fit is said to be that which minimizes the sum of the squared residuals between the points of the graph and the points of straight line. The least squares method is the most widely used procedure for developing estimates of the model parameters. The graph of the estimated regression equation for simple linear regression is a straight line approximation to the relationship between y and x. When regression equations obtained directly that is without taking deviation from actual or assumed mean then the two Normal equations are to be solved simultaneously as follows; For Regression Equation of x on y i. e. x=a+by The two Normal Equations are:- For Regression Equation of y on x i. e. y=a+bx The two Normal Equations are:- Remarks:- 1- It may be noted that both the regression coefficient ( x on y means bxy and y on x means byx ) cannot exceed 1. 2- Both the regression coefficient shall either be positive + or negative -. 3- Correlation coefficient (r) will have same sign as that of regression coefficient. Regression Analysis REGRESSION ANALYSIS Correlation only indicates the degree and direction of relationship between two variables. It does not, necessarily connote a cause-effect relationship. Even when there are grounds to believe the causal relationship exits, correlation does not tell us which variable is the cause and which, the effect. For example, the demand for a commodity and its price will generally be found to be correlated, but the question whether demand depends on price or vice-versa; will not be answered by correlation. The dictionary meaning of the ‘regression’ is the act of the returning or going back. The term ‘regression’ was first used by Francis Galton in 1877 while studying the relationship between the heights of fathers and sons. â€Å"Regression is the measure of the average relationship between two or more variables in terms of the original units of data. † The line of regression is the line, which gives the best estimate to the values of one variable for any specific values of other variables. For two variables on regression analysis, there are two regression lines. One line as the regression of x on y and other is for regression of y on x. These two regression line show the average relationship between the two variables. The regression line of y on x gives the most probable value of y for given value of x and the regression line of x and y gives the most probable values of x for the given value of y. For perfect correlation, positive or negative i. e. for r=  ±, the two lines coincide i. e. we will find only one straight line. If r=0, i. e. both the variance are independent then the two lines will cut each other at a right angle. In this case the two lines will be  ¦to x and y axis. The Graph is given below:- We restrict our discussion to linear relationships only that is the equations to be considered are 1- y=a+bx – x=a+by In equation first x is called the independent variable and y the dependent variable. Conditional on the x value, the equations gives the variation of y. In other words ,it means that corresponding to each value of x ,there is whole conditional probability distribution of y. Similar discussion holds for the equation second, where y acts as independent variable and x as dependent variable. What purpose does regression line serve? 1- The first object is to estimate the dependent variable from known values of independent variable. This is possible from regression line. – The next objective is to obtain a measure of the error involved in using regression line for estimation. 3- With the help of regression coefficients we can calculate the correlation coefficient. The square of correlation coefficient (r), is called coefficient of determination, measure the degree of association of correlation that exits between two variables. What is the difference between correlation and linear regression? Correlation and linear regression are not the same. Consider these differences: †¢ Correlation quantifies the degree to which two variables are related. Correlation does not find  a best-fit line (that is regression). You simply are computing a correlation coefficient (r) that tells you how much one variable tends to change when the other one does. †¢ With correlation you don't have to think about cause and effect. You simply quantify how well two variables relate to each other. With regression, you do have to think about cause and effect as the regression line is determined as the best way to predict Y from X. †¢ With correlation,  it doesn't matter which of the two variables you call â€Å"X† and which you call â€Å"Y†. You'll get the same correlation coefficient if you swap the two. With linear regression, the decision of which variable you call â€Å"X† and which you call â€Å"Y† matters a lot, as you'll get a different best-fit line if you swap the two. The line that best predicts Y from X is not the same as the line that predicts X from Y. †¢ Correlation is almost always used when you measure both variables. It rarely is appropriate when one variable is something you experimentally manipulate. With linear regression, the X variable is often something you experimental manipulate (time, concentration†¦ and the Y variable is something you measure. Regression analysis is widely used for  prediction  (including  forecasting  of  time-series  data). Use of regression analysis for prediction has substantial overlap with the field of  machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer  causal relationships  between the independent and dependent variables. A large body of techniques for carrying out regression analysis has been developed. Familiar methods such as  linear regression  and  ordinary least squares  regression are  parametric, in that the regression function is defined in terms of a finite number of unknown  parameters  that are estimated from the  data. Nonparametric regression  refers to techniques that allow the regression function to lie in a specified set of  functions, which may beinfinite-dimensional. The performance of regression analysis methods in practice depends on the form of the data-generating process, and how it relates to the regression approach being used. Since the true form of the data-generating process is not known, regression analysis depends to some extent on making assumptions about this process. These assumptions are sometimes (but not always) testable if a large amount of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However when carrying out  inference  using regression models, especially involving small  effects  or questions of  causality  based on  observational data, regression methods must be used cautiously as they can easily give misleading results. Underlying assumptions Classical assumptions for regression analysis include: ? The sample must be representative of the population for the inference prediction. ? The error is assumed to be a  random variable  with a mean of zero conditional on the explanatory variables. ? The variables are error-free. If this is not so, modeling may be done using  errors-in-variables model  techniques. ? The predictors must be  linearly independent, i. e. it must not be possible to express any predictor as a linear combination of the others. SeeMulticollinearity. The errors are  uncorrelated, that is, the  variance-covariance matrix  of the errors is  diagonal  and each non-zero element is the variance of the error. ? The variance of the error is constant across observations (homoscedasticity). If not,  weighted least squares  or other methods might be used. These are sufficient (but not all necessary) conditions for the least-squares estimator to possess desirable propertie s, in particular, these assumptions imply that the parameter estimates will be  unbiased,  consistent, and  efficient  in the class of linear unbiased estimators. Many of these assumptions may be relaxed in more advanced treatments. Basic Formula of Regression Analysis:- X=a+by (Regression line x on y) Y=a+bx (Regression line y on x) 1st – Regression equation of x on y:- 2nd – Regression equation of y on x:- Regression Coefficient:- Case 1st – when x on y means regression coefficient is ‘bxy’ Case 2nd – when y on x means regression coefficient is ‘byx’ Least Square Estimation:- The main object of constructing statistical relationship is to predict or explain the effects on one dependent variable resulting from changes in one or more explanatory variables. Under the least square criteria, the line of best fit is said to be that which minimizes the sum of the squared residuals between the points of the graph and the points of straight line. The least squares method is the most widely used procedure for developing estimates of the model parameters. The graph of the estimated regression equation for simple linear regression is a straight line approximation to the relationship between y and x. When regression equations obtained directly that is without taking deviation from actual or assumed mean then the two Normal equations are to be solved simultaneously as follows; For Regression Equation of x on y i. e. x=a+by The two Normal Equations are:- For Regression Equation of y on x i. e. y=a+bx The two Normal Equations are:- Remarks:- 1- It may be noted that both the regression coefficient ( x on y means bxy and y on x means byx ) cannot exceed 1. 2- Both the regression coefficient shall either be positive + or negative -. 3- Correlation coefficient (r) will have same sign as that of regression coefficient.