Academic performance Essay Example for Free

Academic per classance EssayPurpose of projectOver the grades at male monarchs Royal College I admit seen t all(prenominal)ers having stern conversations with students for r separatelying to school modern habitually. These students are confront with consequences such as in-house suspension or community service for regular deep coming. I myself have been a victim of these punishments. It is believed that students who are frequently deeply are indiscip course of study, and this canful spill over into their get habits, hence affecting their overall performance in their internal exameninations. On the other hand, some dower different views that punctuality has no effect on a students performance.Reason being, students do extra studies at home, hence making up for lost time at school. In that setting I would like to de nameine through a statistical study whether or not in that respect is a correlation between students punctuality and schoolman performance in queens Roya l College. I chose to study the present fifth form year group because this is the year they will be heading into the CXC CSEC examinations, assuming at this point, their attitude towards their school work will be serious.See more Strategic Management Process EssayVariablesLet X be the fundamental sum of form 5 students turning of generation late Let Y be the centre sum of form 5 students average end of bourne examination tonsOther educationseldom number of times lateexcessive number of times late30% 49% bad average sum up50% 69% just average score70% 89% nice average scoren is the number of students in the sample sizeLet x work the sum of all the times late by the form 5 students Let y represent the sum of all the form 5 students end of term exam averagescores Let xy represent the sum of the multiples of form 5 number of times late and form 5 end of term exam average scores Letx2 represent the sum of the squares of the form 5 students number of times late Let y2 repres ent the sum of the squares of the form 5 end of term exam average scores Let represent the sample mean of XLet represent the sample mean of YMethod of selective information collection1. I collected a simulate of the roll books for the various form 5 classes for term 1 (September to December) from the various form teachers. 2. The average score for each student was obtained from the dean of the form 5 year. 3. I counted the number of times late for each student and totaled it. 4. Then I sampled the data. To do this, I used systematic random sampling, I used the lottery method. I wrote each of the student numbers for a particular class (R first) on a separate little piece of paper and put them all into a hat.Then I picked out 10, wholeness at a time without replacement, and for each one I chose, I wrote tear the number of times late and the identical average score. 5. I repeated this for the classes O, Y and L. So in the end I had a sample size of 40, 10 from each class. 6. Afterw ards I make the data, making lists of the student number and their corresponding number of times late and average end of term exam scores for from 5 classes R, O, Y and L and put it into a table.Presentation of data physical body 1.1Fig 1.1 is a table showing n of forty form 5 students chosen and their corresponding punctuality and average score obtained at the end of the term. Of the forty students chosen twenty-five were seldom late and fifteen students were excessively late. It also shows that, eight did bad in the end of term exam, twenty-one did grievous and eleven did excellent. Of the eight that did bad 2 were excessively late and 6 were seldom late. Of the twenty-one that did good 12 were excessively late and 9 were seldom late. Of the eleven that did excellent 1 was excessively late and 10 were seldom late.Fig 1.2Fig 1.2 is a bar graph showing performance aim attributed to students who were seldom late and excessively late. Of the eight that performed badly see Table 1.1 , 75% were seldom late and 25% excessively late. Of the twenty-one that performed good 43% were seldom late and 57% were excessively late. Of the eleven that did excellent 91% were seldom late and 9% were excessively late.Fig 1.3Fig 1.3 is a dot plat showing form 5 students average end of term score in relation to the number of times they were late.Analysis of dataChi-square turn up of independenceA 2- prove of independence at the 5% level of substance will be used to reconcile whether the form 5 students number of times late and average end of term scores are in leechlike of each other, or if there is a descent between them. H0 represents the null hypothesis H1 represents the alternative hypothesis O represents observed frequencies E represents expect frequencies represents the level of significance v represents the number of degrees of freedom H0 A students form 5 end of term average score is independent of his number of times late. H1 A students form 5 end of term average score is dependent on his number of times late.In Fig 1.4, from the points a regression line was drawn which passes through the mean of both sets of data, . The line shows y tends to decrease extremely gentltly as x, increases. Also, the points are scattered about the regression line. This shows that there is a very faint-hearted negative correlation between X and Y.Discussion of findingsMy purpose was to investigate the family relationship between students punctuality (X) and academic performance (Y) in a form 5 year group in Queens Royal College. After I collected my data and sampled it, I put it into a table (Fig 1.1), and then decided to put it in a scatter plot (Fig 1.3) and a bar graph (Fig 1.2). This made the relationship between X and Y easy identifiable. It was also now easier to compare them both. After appropriately representing my data, I chose to do a Chi-square test of independence. This was to determine whether X and Y are independent of each other or not. My decis ion, at the 5% significance level was to reject the alternative hypothesis, meaning that X and Y are not dependent of each other, and so a students form 5 end of term average exam score does not depend on his punctuality record. However, that was not the case and the Chi-square test proved that X and Y are dependent of each other.After determining that X and Y are dependent on each other in the Chi-square test another test was carried out. Details of the relationship were necessary, and so r, the linear product importee correlation coefficient, and the equation of the regression line were calculated. The linear product moment correlation coefficient goes from 1 to -1 and indicates the strength of the linear correlation between two variables. In this experiment, r was shew to be -0.141. This value is negative and very low i.e. near to 0, indicating that there is a very weak negative linear correlation between X and Y. Therefore, from this test, it is safe to say that there is no re lationship between X and Y. r also indicates the strength of the to the lowest degree squares regression line that was install.A least squares regression line of Y on X minimizes the sum of the square of the y differences, therefore it is the intimately accurate representation of the data in the scatter plot, and i.e. the best fit line. The equation of this line was found to be y = 62.12 + -0.2x, and the point ( lies on this line, this was demonstrated on the second scatter plot (Fig 1.4). Since r is very low, this regression line is very weak, and therefore the predictions made from it will be inaccurate. The value of b, -0.2 represents the derive by which y decreases for every unit increase in x, i.e. the number of supererogatory marks in form 5 end of term exams that a student will lose for every additional number of times they were late. The value of a, 62.12, would represent the score a student wouldget in form 5 end of term exams if he is late 0 times for the term.Limitat ionsThis sample was only interpreted from one year group, and so it does not necessarily accurately represent future year groups. This test was done using only scores from one specific examination, there may be errors out-of-pocket to this because students may not have performed at their usual abilities for various reasons, such as an illness or a family problem and also students varying choice of subjects in that some may be doing comparatively easier subjects than others and some may be doing less subjects than others. While collecting my data I observed that it had a lot of students who were absent. Therefore, besides punctuality, absenteeism could have affected their end of term average scores.ConclusionIn this study, one test proved that X and Y were dependent of each other while the other test proved that there was no correlation between them. Therefore no clear cut endpoint can be made as to whether or not a students academic performance depends on their punctuality record in Queens Royal College. This study however, can be improved by collecting data from a larger sample to increase truth of data and carrying out the test for different year groups.ReferencesJ. Crashaw J. Chambers, A Concise Course In advance Level Statistics, Nelson Thornes Ltd, 2002 H. Mulholland J.H.G. Phillips, Applied Mathematics for Advanced Level, Butterworths 1969 http//archive.bio.ed.ac.uk/jdeacon/statistics/tress9.html