Chapter 11. Suka nyerrr...

Suka ke? Ngehehe. Dalam keadaan tanpa laptop dan masa yg suntuk, saya masih sempat mencuri-curi masa ntok smbg novel. Actually saya tak ada idea sgt nak sambung, tp semalam, idea dtg mencurah2. sama mencurah mcm air yg bocor kt dlm bilik tidur tu. Bak kata Yem(buah hati khairan), dark water.Ceh. melampau betul. eah...lari tajuk pulak...

Macam ni, chapter 11, saya cuba merungkai(terungkai ke?) cuba merungkai bagaimana kesulitan berlaku apabila Ali mulakan hidup baru. Sebenarnya, nama Ali tu tak begitu komersial, jd sya pk itu hanya akan berbentuk sementara saje. Cuba jgn bygkan dgn mana2 ali yg kamu kenal, sebb kebykkan ali yg sya kenal, x hensem. ngahaha. Ali kena mulakan hidup baru, lepas dia kena buang kerja dan kena halau dari family. Syifa' yg tolong dia. Dgn syarat, Syifa' x boleh bagitahu Azua yg dia yg tolong Ali.(eh, mcm pening je ayat ni?)

Si Syifa' pulak, dh mulakan operasi menggatal(actually x d lah menggatal mana pon...), cuma dia mula mengambil langkah untuk mengurat Azam. Errrr...mengurat mcm x sesuai je. Dia mcm....nakkan Azam...err...bukan....paham2 sendiri je lah... tp Syifa' tu bukan jenis yg akan menggatal tau... jgn bygkan yg lebih2.

Yg pasal si Azua pulak.......urm... sebenarnya saya nak buat yg parents dia balik, maksud saya, datang Paris mencarinya(ceh). Maksudnya, diaorg nak cepatkan majlis perkahwinan(fuyooooh. Actually ngah mood gatal nak kahwin neyh...).Tak, maksudnya, parents dia dtg ke paris, cari dia dn beritahu yg dia perlu pulang ke Malaysia utk berkahwin dgn si Adam gatal. Si Adam tu akan cari pasal nanti. tengoklah mcm mne...

Okay. oleh kerana nak exam, peluang ntok sambung novel ni sgt tinggi sbb ianya membantu saya hilangkan stress di masa stress. Dan kebetulannya masa stress ntok sem ni agak banyak, jd, makin byklah update novel tu nanti. sabar-sabar je lah. novel ini akan habis maybe dlm chapter 19 atau 20. Sebelum ke masa itu, kena cari publisher house yg best2. mksud saya, yg x brp nak cerewet. Tolong2...klw ade mner2 contact numb. saya ada contact gak neyh, tp, nak byk2 ag. lg byk ag bagus....ngahahahahah..... so...

n_n

mY favourite regression project.

INTRODUCTION & OBJECTIVE

This data set was obtained from the Statistical Online Computational Resource database.
From this data set, I wish to study the average mercury concentration in muscle tissue of one type of fish, which is called, Largemouth Bass.
Largemouth bass were studied in 53 different Florida lakes to examine the factors that influence the level of mercury contamination. Water samples were collected from the surface of the middle of each lake in August 1990 and then again in March 1991. The pH level, the amount of chlorophyll, calcium, and alkalinity were measured in each sample. Next, a sample of fish was taken from each lake with sample sizes ranging from 4 to 44 fish. The age of each fish and mercury concentration in the muscle tissue was measured.
For this research, there are, 11 variables available, but to regress it into my regression model, I choose 5 variables from it, which is,

Independent Variable (Y) Predictor Variable (X)
Average Mercury concentration in the muscle tissue of the fish X1 : pH (pH concentration)
X2 : Alkalinity (Mg/L of
Calcium Carbonate)
X3 : Calcium (Mg/L)
X4(Qualitative):
Age Data
1: Data has been recorded
0:No data was recorded on the age

I wish to examine the level of mercury by regressing the pH concentration, alkalinity in the water, calcium concentration in the water and as well as the fish ages. Mercury is an alkali, that’s why; I’m choosing, alkalinity and calcium concentration for this research. For the age data, since, the mercury concentration is increasing by the age of the fish, and this research was conduct twice, first in 1990 and the second one is in 1991, not all of the fish in the lake was contaminate with mercury. The label ‘1’ means, the fish had been labeled before as contaminate and of course, its age is jotted to. While, the label ‘0’ means, the fish’s data was never jotted, whether it is a new fish in the lake or fish that comes from out of the lake.

The objective of this study is, to examine the level of mercury in the lake that contaminated the fish. Largemouth Bass fish is not so affected with mercury, and also, the longer they live in the lake that contained mercury, the larger the mercury concentration in their muscle tissue. With higher concentration of alkalinity and mercury, the higher the concentration of the mercury will be.

DIAGNOSTIC CHECKING
Before doing any calculation, I checked all of my variables by using SPSS. I wish to know whether my variables that I’ve chosen are suitable enough for my regression model.



Checking the model is significant or not:
By using F-Test:

Ho : β1 = 0 (The model is not significant)
H1 : β1 ≠ 0 (The model is significant)
α = 0.05

Test Statistic:
According to the ANOVA Table, the p-value is 0.000

Decision Rule:
If, p-value < α , Reject H0

Decision:
Since p-value < α ,Reject H0
Conclusion: This model is appropriate, since F-test shows there's linear relationship between X and Y.





The equation: Average Mercury = 1.137 – 0.082(pH) – 0.005(Alkalinity) + 0.004(Calcium) + 0.056(Age Data)

Thus, for every unit pH concentration increase, the average mercury will drop by 0.082 parts per million, provided that Alkalinity and Calcium remain unchanged. Next, for every 1 Mg/L increase in Alkalinity, the Average Mercury will decrease by 0.005 parts per million, while pH and Calcium remain unchanged. Similarly, for every 1 Mg/L increase in Calcium concentration, the Average Mercury will increase by 0.004 parts per million. While, with the pH concentration, Alkalinity and Calcium held constant, the history of data that was never recorded before is found to be 0.056 higher than the recorded data.

The p-value for pH, Calcium and Age data all are greater than 0.05, thus, those three predictor variable are not significant for Average Mercury, while, the p-value for Alkalinity is 0.017, which is less than 0.05, then, Alkalinity is significant for Average Mercury.

The 95% confident interval for pH is [-0.167, 0.003], while Calcium is [-0.002, 0.009] and Age data is [-0.139, 0.251]. This indicates those 3 predictor variable are not significant because, the value of 0 falls within the interval. Where else, the 95% Confident Interval for Alkalinity is [-0.01,-0.001], show this predictor variable is significant where the value of 0 doesn’t fall within the interval.

The VIF values are below 5, indicating that there is no problem of multicolinearity.

KOLMOGOROV-SMIRNOV TEST OF NORMALITY
Ho: Distribution is normal
H1: Distribution is no normal
α = 0.05

Test Statistic:
p-value = 0.004

Decision Rule:
If p-value < α, Reject Ho

Decision:
Since p-value= 0.04 < α=0.05, Reject Ho

Conclusion:
The distribution is not normal. To remedy this problem, transformations need to be done.




From this Unstandardized Residual (e) Vs Unstandardized Predicted Value(Y) scatter plot, we can deduce that the error term have non-constant variance. The plots shows heteroscedasticity pattern, which concludes that the variance of the dependent variable is, varies across the data. It might complicate the analysis because in our assumptions, we assume that the error variance is constant. To solve this problem, transformation needs to be done.


TRANSFORMATION



Based on this scatter-plot matrix, the transformation that is appropriate for this model is logy.

AFTER TRANSFORMATION

2nd ANALYSIS REGRESSION MODEL (AFTER TRANSFORMATION)



The equation: Log Average Mercury = 0.101 -0.072(pH) – 0.006(Alkalinity) + 0.002(Calcium) + 0.194(Age Data)

Thus, for every unit pH concentration increase, the average mercury will drop by 0.072 parts per million, provided that Alkalinity and Calcium remain unchanged. Next, for every 1 Mg/L increase in Alkalinity, the Log Average Mercury will decrease by 0.006 parts per million, while pH and Calcium remain unchanged. Similarly, for every 1 Mg/L increase in Calcium concentration, the Log Average Mercury will increase by 0.002 parts per million. While, with the pH concentration, Alkalinity and Calcium held constant, the history of data that was never recorded before is found to be 0.017 higher than the recorded data.

The p-value for pH and Calcium all are greater than 0.05, thus, those three predictor variable are not significant for Log Average Mercury, while, the p-value for Alkalinity is 0.003, which is less than 0.05, then, Alkalinity is significant for Log Average Mercury. Lastly, the p-value for Age Data is 0.032 which is less than 0.05, so, Age Data is significant in this model.

The 95% confident interval for pH is [-0.149, 0.005], while Calcium is [-0.003, 0.007]. This indicates those 2 predictor variable are not significant because, the value of 0 falls within the interval. Where else, the 95% Confident Interval for Alkalinity is [-0.01,-0.001], and Age Data is [0.017, 0.372] show these predictor variables is significant where the value of 0 doesn’t fall within the interval.

The VIF values are below 5, indicating that there is no problem of multicolinearity. But, the VIF values for Alkalinity Variables, is approaching 5, which conclude, there might be almost multicolinearity exist.



The R-Square value is 0.586, which means 58.6% of the variation in Log Average Mercury can be explain by Age Data, Calcium, pH and Alkalinity.


THE KOLMOGOROV-SMIRNOV TEST OF NORMALITY

Ho: Distribution is normal
H1: Distribution is not normal
α = 0.05

Test Statistic:
p-value = 0.200

Decision Rule:
If p-value < α, Reject Ho

Decision:
Since p-value= 0.200 > α=0.05, do not Reject Ho

Conclusion:
The distribution is normal. Transformation works.


Even though according to the VIF values of the predictor variables shows that multicollinearity problem exist and not serious, but there still relationship among the predictor variables. Between pH and Alkalinity, when the pH concentrations increase, Alkalinity will increase to. This is in violation of the assumption of independency of the predictor variable.

Multicollinearity is coined to express the situation where the independent variable are highly correlated or associated with each other. The presence of multicollinearity often makes the regression coefficient less reliable, over inflates the R-Square and makes it difficult to differentiate the more important predictor variable from the less important one.

There are many ways to remedy this problem, such as, dropped the variable. Adding the interaction term might help reduced the multicollinearity problem. So, I decide to do both, since, practically, Calcium and Alkalinity, any calcium solutions is an alkali, so Calcium and Alkalinity are correlated physically. By chance, according to the scatter-plot matrix too, it is highly correlated with each other.

Dropping the variable might help reducing the multicolinearity problem, and at the same time, I might want to interact both of them, since they are ‘interacted’ physically.

An interaction term is basically the product of two predictor variables of interest.
For this multiple regression model, I proposed to do interaction term on Calcium and Alkalinity and on the same time, dropped both of the predictor variables, deciding not to center their values.


Ngahaha... sampai situ je. Tak ley lbey2 sebab tkt2 de org tiru nnt. Ngehehe. Ini kerja projek yg ala-ala presentation untuk praktikal nanti. Untuk mereka yang nak tahu, macam ni lah lebih kurangnya kerja STATISTICIAN. Kerja cipta equation untuk estimate sbrg benda, kalau nak diikutkan, setiap bidang didunia ni perlukan statistician. Seorang statistician diajar untuk precise dalam kiraan, terperinci, kreatif dan otak yang memang seimbang, tak terlalu analitikal dan perlu rasional. Jangan pandang rendah pada bidang ni, sebab tak ramai orang yang boleh buat. Belajar dia pun separuh gila jugak! Ngahaha. Projek yang saya buat ni masih di peringkat awal. Peringkat asas. Maksudnya,data ni pun kitorang 'curik' kat internet je. Tak sempat rasanya nak kumpul data buat questionnare sendiri. Tak de masa! Ngahaha.

Tak faham jugak? Urm.. kira macam ni lah. Andai kata ada seorang doktor nak buat kajian tentang ubat yang dia baru cipta. Nak tahu, ubat tu berkesan ke tak, dia perlukan bantuan seorang statistician supaya kajian dia lebih terperinci. Tugas statistician tu akan jalan research macam, dia panggil 200 orang pesakit, bahagi dua kumpulan. kumpulan A dan B. Kumpulan akan terima rawatan dengan gunakan ubat tadi, manakala kumpulan B akan guna ubat yang dipanggil, placebo. Placebo tu cuma sejenis glukosa yang konon-kononnya ubat juga. Kajian mungkin akan dijalankan selama 2 bulan, lepas tu, statistician tu akan edarkan questionnare tentang tahap kesihatan mereka. Kemudian data-data tu akan dianalisis oleh statistician tadi, tengok data tu perlu dianalisis secara parametrik atau non-parametrik. Non-parametrik tu berlaku bila data yang diterima tak bertaburan normal, manakala parametrik sebaliknya. Kemudian, daripada analisis tadi tulah, baru si statistician tu bagitahu doktor, uba u berkesan ke tak, kemudian barulah doktor tu buat kajian guna cara saintifik dia sendiri.

Fuh! Tak sangka tugas seorang statistician ini begitu penting(ceh!). Di Malaysia, statistician masih kurang dan tak ramai yang berpengalaman.Harap-harap calon-calon statistician yang ada sekarang (termasuk saya) ni boleh berjaya dalam bidang ni. Perlukan ketekunan yang amat tinggi plus gaji pon besar! Ngahaha. Hai... silap-silap haribulan, tak kahwin lah saya.... ngahahaha... =p

Karnival WAJADIRI IPT Peringkat Kebangsaan.

Ini salah satu event yang paling saya tak sabar nak pergi. Tak tahu kenapa. Maybe ini first time saya join activities yang saya rasa best. Saya lagi suka jadi orang belakang daripada jadi peserta. Ngahaha.

Okay. Bagi mereka yang blur, apakah itu yang dibebelkan saya, itu ialah berkenaan Kem Wajadiri IPT peringkat kebangsaan yang akan diadakan di UiTM Puncak Alam bermula pada 12 disember dan berakhir pada 13 Disember(2 hari jerr). Tapi ianya peringkat kebangsaan. tu yang lagi syiokk. Kem Wajadiri ni, khas dan speacial untuk semua ahli Seni Silat Cekak Malaysia dan lebih-lebih lagi untuk pelatih-pelatih Seni Silat Cekak cawangan IPT UiTM Shah Alam. Memanglah sangat seronok.

Karnival Wajadiri ini akan dirasmikan oleh Yang Amat Berhormat, Menteri Pengajian Tinggi Malaysia, Datuk Ahmad Khalid Nordin. Manakala akan ditutup oleh Naib Presiden Persatuan Seni Silat Cekak Malaysia, Datuk Hj Maideen Bin Kadir Shah. Memanglah sekejap sebab 2 hari je, tapi kami AJK dah disuruh 'turun padang' bermula 30 November lagi. Memanglah cuti kali pendek betul.

Saya tak berani nak cerita banyak sebab semuanya masih dalam perbincangan. Cuma saya nak sarankan pada mana-mana sahabat saya yang dah tamat belajar Seni Silat Cekak ni, untuk sama-sama menjayakan Karnival Wajadiri IPT Peringkat Kebangsaan ini. Join lah. Sangat seronok. Anda boleh download borang permohonan di
http://psscmuitm.org/kwd2009/menu_kwd2009.htm

Saya rasa dia belum tutup lagi. Ngahaha. Okaylah.
Nak sambung siapkan kerja projek. Oh... nanti saya akan upload kisah MAJLIS PERKAHWINAN sepupu saya. Agak best dan agak gempak lar. Jeles saya dibuatnya. Buat masa ni, nak fokus study for final exam okay. Da-da.

나는 잊는다!

그의 생일이다! 나는 진짜로 유감스럽다…

나는 당신에게 축 생일을 바란다!
나는 당신이 성공 항상 희망한다.

행운을 빕니다!
당신은 좋은 일을 했다!
이렇게…
(n_n)

TENSion

Saya tak tahulah samada lifestyle saya ni yg x betul ke kenapa, saya tension(stress) sejak kebelakangan ni. Mungkin juga sebab final exam lagi seminggu.dan mungkin juga sebab banyak lagi projek yang on-going tapi tak going-going pun.

Tambahan lagi, saya stress dengan laptop saya yang buat hal secara tiba-tiba. Sewaktu saya sedang release my stress dengan menonton SPONTAN di dalam laptop, tiba-tiba, spontan itu tersangkut-sangakut, dan beberapa minit kemudian, spontan itu tertutup, lalu keluar skrin kegemaran ramai-BLUE SCREEN. Yang paling saya geram, BLUE SKRIN itu tidak tertutup kembali, malahan ia sedia menayangkan cerita BLUE SKRIN yang MENARIK itu.

Saya akhirnya geram, lalu menarik wayar extension, dan skrin tadi tertutup. saya tunggu hampir 5 minit, kemudian saya on semula laptop itu. Malangnya, dia on dan BLUE SKRIN sepanjang hayatnya. SAYA GERAM.

Sakitnya hati. Sudahlah begitu banyak projek yang nak di hantar. Dengan final examnination yang boleh saya kira, saya bergantung pada laptop itu dan mata pencarian saya-NOVEL. Kali terakhir saya update didalam email saya ialah pada chapter 5, sedangkan sekarang ni dah chapter 11.Sakitnya hati.


Tiada jalan penyelsaian lain selain perkataan kegemaran ramai-FORMAT. Tapi, AL-HAMDULILAH, saya akan format C sahaja memandangkan segala benda-benda kegemaran saya didalam D. SABAR JE LAH...

Selain laptop, saya kena tahan dengan perangai beberapa sahabat. Tak apalah. Saya cuba bersabar. Selagi saya hidup, saya tidak mahu menggelapkan hati saya dengan dendam. Cukup. SAya dah mula merepek. saya tahu. Memang sangat merepek. Cukuplah saya mengumpat sekali dengan HATTADI hari tu. Tak sangka HATTADI menjadi tempat saya bergantung, cerita segala masalah dan beban. Dan, TERIMA KASIH HATTADI. Ngahaha.

Saya nak bagi orang-orang tertentu je baca. Maaf lah. Ini kod rahsia. Cuma mereka yang high-quality otaknye yang faham apa yang saya cuba tulis ni.

나 정말 사랑해요...
너무 사랑해...
네 살미 선물...

Faham tak?? ngehee... sorry lah, saya tengah mood menggatal ni. Haha. Musim mengawan lah katakan. Tapi jangan fikir yang bukan-bukan. Saya cuma sedikit stress nak dekat2 final ni. Muka pun dah masam jer. Tapi maintain chomey jangan risau.
Haha.

Kengkawan lamerrrku...

Semalam ada meeting dengan BYTES=Editorial Board fakulti. Janji pukul 9.00 mlm. Saya dan Khairan pun bergegas pergi memandangkan ini meeting pertama selepas satu semester berlalu. Memang geram sebab tak ada sesiapa yang tunggu. Sudah lah kami kene tunggu di Laman Najib tu dalam 15 minit. Akhrinya ada seorang mesej beritahu tentang tempat meeting.

Cuma ada 5 orang. saya tertanya-tanya; mana ahli BYTES yang lain?. Takkan ramai ni je yang nak buat kerje?. Ada seorang lelaki yng datang menghampiri kami, mengedarkan majalah BYTES sesi 2008. Berbincang2...akhirnya 2 orang ahli baru bertanya;
BYTES ada tak buat dinner ker? Makan malam ke?... Saya terasa. Bukan kerana memang tidak pernah ada, cuma, kami tak pernah terfikir pun nak buat majlis keramaian macam tu. Saya cuba mendalami apa yang difikirkan mereka. Tanpa sempat Aho menjawab, mereka bertanya lagi; Masuk BYTES ni dapat sijil kan?. Saya cuma tersenyum sinis. Di dalam hati saya berkata; Kalau tak dapat nak keluar ke?. Saya tahu kenapa, diorang masuk BYTES suntuk nama, jawatan, sijil, makan malam percuma di hotel, populariti, bukan dengan objektif ingin menghasilkan majalah terbaik untuk pelajar baca, nak tunjukkan kepada orang luar, tentang kelebihan FSKM di UiTM ni, bukan dengan niat berbakti untuk masyarakat(ceh)!.

Saya sendiri tak rasa sijil BYTES ni boleh bantu kita mencari kerja(mungkin boleh), cuma saya masuk BYTES ini untuk kepuasan. Dapat join teamwork macam ni memang kegemaran saya dari sekolah. Kalau dulu di sekolah, dari form 1 sampai form 5, jawatan dari ahli Unit Publicity sampai lah ke Ketua Unit, masuk KMJ, dapat jadi Marketing Manager pulak, dan sekarang, Reporters pulak...rasanya semua tu sekadar untuk kepuasan sendiri. Seronok buat bende macam ni... menyibuk2 kan diri dengan segala xtvt student...ngahaha.

hurm.....rasanya benda2 ni buat saya rindu pada team yg pernah kerja sama-sama dengan saya dulu...Team dekat KMJ-Buletin(Shuha, Ryn, Afiq, Syazwan,Thirah, dan semua orang lagi[sory x dpt mention semua]), dkt MRSM Mersing-EdBoard(Miss Ezi, Miss Ina, Wafi, Afiqah, Helmi, Fizah, Od, {ramai lagi..])... Itu semua team yang paling seronok yang pernah saya jumpa dalam hidup saya...truthfully... huhuu...

Sedeynyer bile ingat kat semua orang. Harap-harap semuanya dah berjaya, sama-sama berjaya. Masing-masing dah ada jalan sendiri. Mungkin dah lupa pada saya. Tak apa. Janji kita pernah kerja sama-sama.Itu pun dah cukup untuk saya(ceh...dramatik pulak..)...

Oklah, tengah kelas ni. saya cuma curi2 masa nak tulis kat sini. Haha. nanti saya post update novel. Nak siapkan chapter 10 tuh dah x sempat lah. BZ sangat!!!!! Okay?? Chiau!

Ini Reply untuk pemilik blog Dari Kacamata NEOslick...ngeheheeee

Saya sayang anda juga, anda pun teruskan usaha!
Terima Kasih
Hehehe…

=)