Academic performance Essay Example for Free

Academic per arrive atance Es declarePurpose of projectOver the years at Queens over-embellished College I have seen t for each oneers having stern conversations with scholars for reaching to school ripe habitually. These students are faced with consequences such as in-house suspension or community service for fastness new-fangled coming. I myself have been a victim of these punishments. It is believed that students who are frequently late are indiscip flexure, and this can spill over into their study habits, hence touch their overall per buildance in their internal examinations. On the other hand, some share different views that punctuality has no effect on a students per impressance.Reason being, students do extra studies at home, hence making up for lost time at school. In that context I would manage to de stipulationine through a statistical study whether or not in that respect is a correlation between students punctuality and pedantic performance in Queens Royal College. I chose to study the present fifth form year assemblage 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 total sum of form 5 students number of measure late Let Y be the total sum of form 5 students second-rate block up of term examination scoresOther informationseldom number of clock lateexcessive number of times late30% 49% bad intermediate score50% 69% good amount score70% 89% sensitive average scoren is the number of students in the sample sizeLet x personify the sum of all the times late by the form 5 students Let y demo 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 L et y2 represent 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 data collection1. I collected a copy 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 apply systematic random sampling, I used the lottery method. I wrote each of the student numbers for a particular class (R first) on a give away little piece of paper and prepare them all into a hat.Then I picked out 10, genius at a time without replacement, and for each one I chose, I wrote down the number of times late and the corresponding 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. Afterwar ds I organized 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 dataFig 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, eighter did bad in the end of term exam, twenty-one did good 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 level attributed to students who were seldom late and excessively late. Of the eight that performed badly see Table 1.1, 75% wer e 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 scatter plot showing form 5 students average end of term score in relation to the number of times they were late.Analysis of dataChi-square test of independenceA 2-test of independence at the 5% level of significance will be used to determine whether the form 5 students number of times late and average end of term scores are in subject of each other, or if there is a relationship between them. H0 represents the null hypothesis H1 represents the alternative hypothesis O represents observed frequencies E represents expected 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 i s dependent on his number of times late.In Fig 1.4, from the points a relapse stemma was drawn which passes through the mean of both sets of data, . The line shows y tends to fall extremely gentltly as x, increases. Also, the points are scattered about the regression line. This shows that there is a very weak negative correlation between X and Y. countersign of findingsMy purpose was to investigate the 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 easily identifiable. It was also at once 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 decision, at the 5% signi ficance 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 slip of paper 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 moment 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 found to be -0.141. This honor 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 relationship b etween X and Y. r also indicates the strength of the least squares regression line that was found.A least squares regression line of Y on X minimizes the sum of the square of the y differences, therefore it is the most accurate representation of the data in the scatter plot, and i.e. the best apparel 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 amount by which y decreases for every unit increase in x, i.e. the number of additional 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.LimitationsThis sample was only taken from one year group, and so it does not necessarily accurately represent future year groups. This test was done utilize only scores from one specific examination, there may be errors due 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 relatively 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 conclusion 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 m odify by collecting data from a larger sample to increase accuracy of data and carrying out the test for different year groups.ReferencesJ. Crashaw J. Chambers, A crisp Course In Advanced 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