Project 1 Sta 108 Baru.docx 295i33

THE RELATIONSHIP BETWEEN THE TIME TAKEN FOR STUDENTS TO STUDY ON WEEKEND AND THE TEST SCORES FOR OPTIC AND WAVES (PHY260) ON MONDAY

STA108: STATISTICS AND PROBABILITY

PREPARED BY: NOR SYAZWANI BINTI SHAHNUN (2016975079) NUR AIN NAZURAH BINTI MOHD NOOR (2016789305) SITI SURAINI BINTI AHMAD (2016107743) NUR SYUHADA BINTI AHMAD TERMIZI (2016726471)

DIPLOMA IN SCIENCE FACULTY OF APPLIED SCIENCE

DISTRIBUTED TO: MADAM FADZILAH ABDOL RAZAK

CONTENT ACKNOWLEDGEMENT TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION 1.1 Background of study 1.2 Objectives of Study 1.3 Significance of Study 1.4 Limitation of Study

CHAPTER 2: METHODOLOGY 2.1 Data Description 2.2 Graphical Description 2.3 Numerical Technique

CHAPTER 3: RESULTS AND INTERPERATION 3.1 Data Representation 3.2 Descriptive Statistics Analysis 3.3 Correlation and Regression

CHAPTER 4: CONCLUSION 4.1 Report Summary 4.2 Appendix REFERENCES

PAGE

ACKNOWLEDGEMENT

CHAPTER 1: INTRODUCTION 1.1 Background of Study

This study was taken to analyse the relationship between the time spent by the students to study on weekend and the test scores on Optics and Waves (PHY260) on Monday. This study was participated by 20 students from 62 physics students from Diploma in Science, Faculty of Applied Science, UiTM Perak, Kampus Tapah which is 10 students from A4AS1204_E and another 10 students from A4AS1204_F.

The study was organized by semester 4 students of AS1204_E to satisfy the requirements for the syllabus of Basic Statistics and Probability (STA108). We chose to evaluate the relationship between the number of minutes the students took for self study on weekend and their performance for their test on Monday. In this study, it helps us to determine whether Applied Science students are investing their quality time well on weekend for studying as well preparation for test on Monday.

The time taken for self-study on weekend defined the test scores by students for test on Monday. The time taken for self-study on weekend (minutes) is measured from Friday until Sunday and no restrictions if it was day or night. The test scores by the student on Optics and Wave (PHY260) are in range 40% to 100%. This data will show whether the amount of minutes the students spent affected their test results.

From the study, the time taken for students to study (minutes) is an explanatory variable and the test scores by the students is response variable. This is because test scores is depends how long the students spent their weekend for self-studying. The data shows a positive linear correlation which is 0.9053. The longer the minutes taken for students to study, the higher their scores in their test.

1.2 Objectives of Study

The objective of this study are as follows: 1. To find the relationship between the time taken for students to do revision by self-study on weekend on the test scores by them. 2. To determine how this time taken for self-study effect the student’s scores. 3. To study how the student spent their hours effectively on their studies. 4. To understand whether student investing enough time for studying.

1.3 Significance of Study

A time study can establish a baseline from which to drive improvement efforts, or set a standard to control performance. Without basic time study measures, it is impossible to know whether work has improved or whether there are differences in performance in a unit. Several factors might be influence student performance on tests or exams besides working on their assignment or coursework. Many students carry a course load of 15 credits, or approximately 15 hours of class time each week. Doing some simple math indicates that student should be spending roughly 30 hours of study time and 15 hours in class. Time spent studying can improve student’s experience while at university, offer a better sense of self-confidence throughout their school career and improve their grades overall. From the study, it found that students who spent more hours on their weekend in studies performed better than who studied with minimum hours. The extra time you spend between the pages of a textbook can increase your knowledge and selfassurance to score higher grades. Some research found that hours studied was the most important factor contributing to exam grade improvement. In fact, it is advisable for student to spend 2 hours per day for their self-study and use it efficiently and wisely (study skills and strategies) which equivalent to 1 hour of lecture. Students should anticipate investing a minimum of 2 hours outside of class and about 4 hours on homework problems per week while an average should plan on investing 12 h per week to earn an A or B at least in tests.

1.4 Limitation of study

The limitation of this particular study is that only the applied science students were enrolled as respondents and can be in sampling frame due to the objective of the study which to analyse the relationship between the amount of minutes the students spend on their weekend for self-studying against the scores by the students on test.

CHAPTER 2: METHODOLOGY 2.1 Data Description I.

Population -

II.

Sample -

III.

All student in UiTM Campus Tapah

20 student in UiTM Campus Tapah

Sampling technique -

Simple random sampling is used in this survey. The sample was collected randomly from sampling frame which is a list of all UiTM Campus Tapah students who take the PHY 260 exam.

IV.

Data collection method -

Direct questionnaire. Printed questionnaires and distributed by hand to all students by face to face. The advantages of this method are allow the respondents to be honest answering the question and give time to respondents to understand and share the opinion from a large number of student while the weaknesses are the some respondents do not complete the questionnaire and allow missing values.

V.

Descriptions of variable -

The variables of this survey are the time taken, the test score for optic and wave subject of student to study on weekend

Variable

Test score of student

Type of variable

Level of measurement

Quantitative continuous

Ratio scale

- The data is expressed in number

- Top of measurement

- The number of data can be in - The scale allows the multiplication and

Time taken for student study

fraction

division

Quantitative continuous

Ratio scale

- The data is expressed in number

- Top of measurement

- The number of data can be in - The scale allows the multiplication and fraction

division

2.2 Graphical Description First, bar chart is graphical methods for categorical frequency distribution. There are several types of bar chart which are simple bar chart, multiple bar charts and component bar chart. A sample bar chart is consists of a grid and some vertical or horizontal columns/bars. Each column represents quantitative data Bar Chart Minutes of Study vs Test Score 2.0

Count

1.5

1.0

0.5

0.0

43

45

46

49

50

53

55

58

60

65

68

70

78

84

85

88

90

95

Test of Scores

Figure 1: simple bar chart

Bar Chart Minutes of Study vs Test Score 2.0

Count

1.5

1.0

0.5

0.0

105 120 125 140 150 165 180 185 190 200 210 240 270 300 330 350 375 390 425

Minutes of study

Figure 2: simple bar chart Then, for multiple bar chart is by multiple bars diagram or two or more sets of interrelated data was represented multiple bar diagram the relationship between time taken for

students to study on weekend and the test scores for optic and waves (Phy260) on Monday. The technique of simple bar chart was used to draw this diagram but difference is that used shades, colour or dots to distinguish between different phenomena. In this bar chart we were used to draw the multiple bar charts when the total of different phenomena is meaningless. Minutes of Study vs Test Score Test of Scores 95 90 88 85 84 78 70 68 65 60 58 55 53 50 49 46 45 43

2.0

Count

1.5

1.0

0.5

0.0 Minutes of study

5 0 5 0 0 5 0 5 0 0 0 0 0 0 0 0 5 0 5 10 12 12 14 15 16 18 18 19 20 21 24 27 30 33 3 5 37 39 42

Figure 3: Multiple bar chart

Next, pie chart is also a graphical method for categorical frequency distribution. For pie chart also can be used for compare the relationship between the whole and its components. For pie chart is a circular diagram and the area of the sector circle was used in pie chart. To construct a pie chart (sector diagram), the circle was drawing with radius (square root of the total). The total angle of the circle is 360°. The angles of each component can be calculated by the formula. These angles are made in the circle by mean of the protractor to show different components. All the arrangement of these sectors is anti-clock wise.

Minutes of study vs test scores 43 1, 5.0% 45 1, 5.0%

70 2, 10.0% 55 2, 10.0%

Category 43 45 46 49 50 53 58 60 65 68 78 84 85 88 90 95 55 70

46 1, 5.0% 49 1, 5.0%

95 1, 5.0%

50 1, 5.0%

90 1, 5.0%

53 1, 5.0%

88 1, 5.0%

58 1, 5.0%

85 1, 5.0%

84 1, 5.0%

78 1, 5.0%

68 1, 5.0%

65 1, 5.0%

60 1, 5.0%

Figure 4: Pie Chart

Besides, histogram also was included in graphical methods for grouped frequency distribution. For the histogram the graph that displays the data by using vertical bars of data to represent the frequency of the class. For histogram it was used class boundaries for ( xaxis ) and frequency of the classes at (y – axis ). Histogram of minutes of study 5

Frequency

4

3

2

1

0

100

150

200

250

300

minutes of study

Figure 5: Histogram

350

400

450

Histogram of test scores 5

Frequency

4

3

2

1

0

40

50

60

70

80

90

100

test scores

Figure 6: Histogram

Scatter diagram it was described about the nature of a relationship between independent and dependent variables. Scatter diagrams also can help to show different possible correlation categories, namely, positive correlation, negative correlation, no correlation, perfect positive correlation and perfect positive correlation. First, for positive correlation, it was shown the existence of a positive relationship between two variable which is x and y. The direction of change for both variables is same which is if x- axis increase, then y would increase too. Then, for negative correlation it was shown the existence of a negative relationship between two variable x and y. For this both x and y was changed in the opposite direction of each other which mean if x increase, y would decrease. Next, for no correlation is simply means there exist no relationship between two variables such as x and y. in this case we cannot relate the changes that occur between x and y, in any way. Lastly, for the perfect negative and perfect positive correlation, its show a perfect result and the data shows in a one straight line.

Minutes of study vs test scores 100

90

test scores

80

70

60

50

40 100

150

200

250

300

350

400

450

minutes of study

Figure 7: Scatter diagram

For figure 7 the scatter diagram is show the positive correlation. So for positive correlation, it was shown the existence of a positive relationship between two variable x and y. The direction of change for both variables is same which is if x- axis increase, then y would increase too.

CHAPTER 3: RESULTS AND INTERPRETATION 3.1 Data Presentation Table of minutes of study versus test scores in PHY 260 Student

Minutes of Study

Test Scores (%)

A

105

43

B

120

45

C

125

46

D

140

49

E

150

50

F

165

53

G

180

55

H

185

55

I

190

58

J

200

68

K

210

60

L

240

65

M

270

70

N

300

70

O

330

78

P

350

84

Q

300

85

R

375

88

S

390

90

T

425

95

3.1.1 Bar Graph

Bar Chart Minutes of Study 2.0

Count

1.5

1.0

0.5

0.0

105 120 125 140 150 165 180 185 190 200 210 240 270 300 330 350 375 390 425

Minutes of study

This chart shows the minutes of study for 20 students. The highest is 300 minutes, with over 2.0 count whereas 105, 120, 125, 140, 150, 165, 180, 185, 190, 200, 210, 240, 270, 330, 350, 375, 390, 425 have the same count.

3.1.2 Bar Graph

Bar Chart Test Scores 2.0

Count

1.5

1.0

0.5

0.0

43

45

46

49

50

53

55

58

60

65

68

70

78

84

85

88

90

Test of Scores

This chart shows the test scores PHY 260 for 20 students. The highest is 55% and 70%, with over 2.0 count whereas 43, 45, 46,49, 50, 53, 58, 60, 65, 68, 78, 84, 85, 88, 90, 95 have the same count

95

3.1.3 Pie Chart

Pie Chart Test Scores

88 1, 5.0%

95 90 1, 5.0% 1, 5.0%

43 1, 5.0% 45 1, 5.0%

85 1, 5.0%

46 1, 5.0% 49 1, 5.0%

84 1, 5.0%

50 1, 5.0%

78 1, 5.0%

53 1, 5.0%

70 2, 10.0%

55 2, 10.0% 68 1, 5.0%

65 1, 5.0%

58 60 1, 5.0% 1, 5.0%

From the pie chart above, 10% of the respondents get 70 marks for test scores in PHY260. While 5%, of the respondents that get marks 43, 45, 46, 49, 50, 53, 58, 60, 65, 68, 78, 84, 85, 88, 90, 95, 55 and respectively.

Category 43 45 46 49 50 53 55 58 60 65 68 70 78 84 85 88 90 95

3.1.4 Pie Chart

Pie Chart Minutes of Study

375 1, 5.0%

425 390 1, 5.0% 1, 5.0%

105 1, 5.0% 120 1, 5.0%

350 1, 5.0%

125 1, 5.0% 140 1, 5.0%

330 1, 5.0%

150 1, 5.0% 165 1, 5.0%

300 2, 10.0%

180 1, 5.0% 270 1, 5.0%

240 1, 5.0%

210 1, 5.0%

190 200 1, 5.0% 1, 5.0%

185 1, 5.0%

From the pie chart above, 10% of the respondents take 300 minutes for study in PHY260. While 5%, of the respondents that take minutes of study which are 105, 120, 125, 140, 150, 165, 180, 185, 190, 200, 210, 240, 270, 330, 350, 375, 390 and 425 respectively.

Category 105 120 125 140 150 165 180 185 190 200 210 240 270 300 330 350 375 390 425

3.1.5 Histogram

Histogram of minutes of study 5

Frequency

4

3

2

1

0

100

150

200

250

300

350

400

450

minutes of study

The above graph shows a symmetric data set, it represents the amount of times of minutes of study each of 20 surveys participants took to fill out a certain survey. Based on the histogram above, the most average time of minutes of study by the respondents to make revision for PHY260 test is 200 minutes. The distribution of the histogram above is skewed to the right. The mean and standard deviation for this graph are 237.5 minutes and 2.51832 respectively.

3.1.6 Histogram

Histogram of test scores 5

Frequency

4

3

2

1

0

40

50

60

70

80

90

100

test scores

The above graph shows a symmetric data set, it represents the test scores each of 20 surveys participants took to fill out a certain survey. From the histogram above, the most highest test score of the respondents is 50%. The skewness of the histogram is skewed to the right. The mean and standard deviation for this graph are 65.35 and 16.52 respectively.

3.1.7 Boxplot

Boxplot of minutes of study 450

400

minutes of study

350

300

250

200

150

100

The median number of minutes of study is 205.00. The interquartile range is about 168.80 minutes of study, which means about 50% at the students have between 153.8 and 322.50 minutes of study.

3.1.8 Boxplot

Boxplot of test scores 100

90

test scores

80

70

60

50

40

The median test scores is 62.50. The interquartile range is about 31.75 test scores, which means about 50% at the students have between 50.75 and 82.50 test scores.

3.2 Correlation and Regression Analysis 3.2.1 Frequencies Minutes of Study and Test Scores

Variable

Total Count

N

N*

Percent

Cummulative Percent

Minutes of

20

20

0

100

100

20

20

0

100

100

study

Test Scores

From the table above, the cumulative percent for respondents in minutes of study and test score are 100% respectively.

3.2.2 Descriptive

Descriptive Statistics N

Minimum

Maximum

Mean

Standard Deviation

Minutes of

20

105.0

425.0

22.0

98.5

20

43.0

95.0

3.69

16.52

Study Test Score

From the table above, the minimum and maximum minutes of study are 105.0 minutes and 425.0 minutes respectively. While the mean and standard deviation for minutes of study are 22.0 minutes and 98.5 minutes respectively. The minimum test score in PHY 260 for the students are 43.0% and the maximum score in PHY 260 are 95.0%. The mean for test score are 3.69% and the standard deviation are 16.52%.

3.2.3 Scatter Plot

Minutes of study vs test scores 100

90

test scores

80

70

60

50

40 100

150

200

250

300

350

400

450

minutes of study

From the scatter plot above, there a positive linear relationship between minutes of study and test score in PHY 260.

3.2.4 Regression Variable Entered Model

Variable Entered

Variable

Method

Removed 1

Minutes of study

-

Enter

a. Dependent variable: Test score in PHY 260. b. All requested variables entered.

Model Summary Model

1

R

R Square

95.86%

a. Predictors: ( Constant), Minutes of Study

Adjusted R

Std. Error of

Square

the Estimated

95.63%

95.23%

Coefficients Term

Coefficients

SE

T-Value

P-Value

Coefficients Constant

26.35

2.06

12.80

0.000

Minutes of

0.16421

0.00804

20.43

0.000

study Pearson correlation of minutes of study and test score in PHY 260 is 0.979. This means there is a moderate positive linear relationship between the minutes of study and test score. The regression equation: Y= 26.35 + 0.16421X b = 0.16421 indicates that for every minutes of study increase, the test score in PHY 260 increase by 0.16421.

CHAPTER 4: CONCLUSION 4.1 Report Summary

With this report, we can see that the test score with minutes of study tend to affect the marks of student get. The data shows that students who were took 425 minutes to study prior to taking a test, preformed higher 95% then when they were took less mintes to study such as 105 minutes, it is performed a lower marks which is 43%. So, the specific percentages vary based on the amount of time that was taken to study. In conclusion, the data suggest that if students study for 425 minutes prior to taking a test, that they will perform better that if they study for less than 425 minutes. This data is also importance of study time when preparing students to take the examintation. From correlation and regression analysis, we find out that There is strong positive correlation between the test scores and the minutes of studied for examination in PHY 260. An increase in minutes of study causes the test scores increases because of the student get more knowledge when she or he take more times to study.

4.1 Appendixes Formulas: Mean, x =

fx

n

n [  f Median, ~ x = Lm  2 [f ]

m 1

C

m

Mode, xˆ =

Q1 =

L

1

L

m

n  4 [

[

f f

1 ]C 1   2

m 1

]  C1

1

Variance,

s

2

 f (x  x) 

2

n 1

Standard deviation, s 

 f (x  x)

2

n 1

Coefficient of Variance , CV 

s x100% x

Pearson’s Coefficient of skewness,

x  xˆ s