#6

Assalamualaikum, hello and salam sejahtera everyone.


https://www.youtube.com/watch?v=x3u1G5JAuiY



Here is the first video made by me berkaitan dengan research methodology dan EFA (SPSS), one of assignment yang diberi oleh Prof Dr Zainuddin :)


And before this dah post bagitahu yang if ada readers yang interested untuk dapatkan ebook / software AMOS / file tentang Research Methodology dari Prof Dr Zainudin, boleh email ke saya di :

adiyanasuhaimi@gmail.com


Harga? FOC pastu kalau ada sesiapa yang nak share notes or anything yang boleh dijadikan panduan atau apa-apa saja yang berkaitan dengan ilmu statistics, dialu-alukan untuk email saya okay? Kecil tapak tangan, nyiru saya tadahkan. Gittew :p



Kalau ada sesiapa yang berminat untuk dapatkan servis (amboinyah!) or any helps regarding AMOS or SPSS software, boleh lah email jugak heeeee. Servis kita kira kawan-kawan lah hiks.

See you soon!
Byeeeeeee :]

#5

Assalamualaikum, hello and salam sejahtera everyone.

In this entry, we will proceed with Time Series ( Forecasting ). To be honest, subject ni merupakan one of feveret subject sebab lecturer kami, Prof Madam Napisah adalah sangat cool dan cara beliau mengajar pun sangatlah details. Cumanya mungkin masa final exam tu tak berapa perform sebab jawapan-jawapan yang iolls bagi tak memuaskan beliau tsk tsk. Buku yang akan dijadikan reference ialah buku tulisan Mohd Alias Lazim, bertajuk " Introductory Business Forecasting, a practical approach. "




Definitions of Time Series data:

• A set of data collected or arranged in a sequences of order over a successive equal increment of time
• Data recorded at equal interval time for certain period
• yt = f(t)

1) It is significant to note that for most economic time series, they have tendency to be influenced either directly or indirectly by one or more interrelated events/factors. 

Contohnya macam kalau production sesebuah kilang ataupun hasil ladang tu mungkin akan ada sedikit kesan akibat weather pattern, ups and down of economic cycles, war and change in the demand level of particular product. 

2) There are also series that exhibit the regular recurring effects, moving up and down at regular, fixed or unfixed, intervals.

Contohnya macam seasonal pattern of road accidents masa musim perayaan, increase in sales of train/flight tickets during school holiday session and etc etc.



3) The effect of occurrence of certain unpredictable events leave varying degrees of impact on the time series, some permanent, some temporary. Some effects take shorter time (short term memory), others may take longer time (long term memory) to diminish.



Objectives:

1. To identify and to describe the underlying structure and the phenomenon as depicted by the sequence of observations in the series
2. To determine the most suitable mathematical model to fit the data series and subsequently use the model to generate forecast values

Approaches:

1. Time domain - commonly used for stochastic observations - moving averages (MA), detrending and regression methods to detect and remove autocorrelation
2. Frequency domain - use spectral analysis, harmonic analysis, periodogram - much more difficult to understand (outside of scope of the book :p)

The Classical Decomposition Method

Component types:

1. Trend component       - Tt     
2. Cyclical component    - Ct
3. Seasonal component   - St 
4. Irregular component    - It

( ps: all 't' are subscript t, indicate that these are time related components for which t=1,2,3......T. )

Relationship between Components

• Multiplicative Effect - If the sizes of seasonal variation increase in accordance with the increase in the level of data series then the following relationship is appropriate -- yt is the product of all component

• Additive Effect - The series interacted in an additive manner, used when the absolute sizes of the seasonal variation are independent of each other. The seasonal variation are not affected by change in the level of the series -- additive assumption is the sum of all components.

And details about each of components (trend, cyclical, seasonal, irregular) semuaaaaaa ada dalam buku, dengan steps macam mana nak calculate using excel spreadsheet, nak lukis graph forecast, reason-reason bagai tu semua confirm ada dalam tu, so all we need to do is refer to the book. Yeah! :p 

See you soon!
Byeeeeeee :]

#4

Assalamualaikum, hello and salam sejahtera everyone.

For this entry, I'll explain more about SAMPLING DESIGN. Saya akan refer buku Research Methodology and Data Analysis, written by Prof Dr Zainudin Awang sebab for me, buku inilah yang paling complete dan senang untuk faham :'3 kalau nak lebih details, boleh beli buku direct dari Prof ya? Hiks.


Well everyone knows that kalau population adalah total set of items of interest yang kita berminat nak kaji. Subset untuk population pula dipanggil sample.


The finding from that sample is generalizes on the whole population, and that is why the researcher needs to apply the proper sampling technique to obtain sample yang representation untuk population for our research project.

( Tak boleh lah kalau kita nak suka-suka macam main ambik je any sample lepastu kita buat conclusion daripada sample tu melambangkan population. Tu dah bias tuuuuu ha kenot-kenot )



Based from this book, dinyatakan main reasons for studying sample instead of whole population:

• Cost constraint            - cost untuk kaji sample < cost untuk kaji the whole population
• Time constraint            - working time with sample < working with whole population
• Manpower constraint   - logically, bila sample sikit, manpower yang diperlukan adalah sikit 

Factors that should be considered in choosing the appropriate sample design:

• Research Objectives & Research Questions
• Degree of Accuracy Required
• Availability of Resources
• Time constraint
• Scope of Research
• Statistical Analysis required

Okay selepas dah consider kan segalanya yang perlu di consider di atas, barulah kita proceed dengan which sample design should we used?


There are 2 types of sampling design:

Probability Sampling Design  

•  Commonly used because the sample is selected at random, all element inside the population has equal chances to be selected.

Type of Probability Sampling Design:

• Simple Random Sampling (SRS)

- Applicable for homogeneous population

- Characteristics : gender, socio-economic status, ethnic group, culture, religion

- Allow for equal chances for any element in the population to be selected as sample

- Steps:

                    a. The definition of target population

                    b. List of all elements in population (sampling frame)

                    c. Assignation of numbers to each element of population

                    d. Obtain the table of random numbers / generate it from the computer

                    e. Match the random numbers obtained to the numerical list of population 

                    f. Select the selected element as the respondents for data collection 

- The respondents selected this way are totally random, hence the problem of biasness does not arise. 

- Disadvantages: requires sampling frame, may take lots of time, and high cost.

• Systematic Sampling

- Applicable for homogeneous population

- The element which falls on kth number on the list is chosen as a respondent.

- This sampling procedure fixed the increment in order to determine the following respondent. 

- Steps:

                   a. Determine the size of population (N) and required sample size (n)

                   b. Determine the interval, k=N/n

                   c. Select one random number between 1 to k. 

                   d. Then the following respondent are K+k, K+(K+k), .........until the nth respondents is selected

    

• Stratified Sampling

- Applicable when the population is not homogeneous, then the researcher has to stratify the element into homogeneous groups.

- The process of selecting a sample that represents each stratum in a population
- The researcher can stratify the heterogeneous population into homogeneous population within a stratum in terms of socio-economic status, ethnic group, culture, religion, marital status, type of house, etc.

- Steps:

                   a. Specify the strata (within homogeneous, between heterogeneous)

                   b. Assign the elements according the strata having similar characteristics of interest in study

                   c. Select sample within each strata using SRS since the population is already uniform.

                   d. The number of samples from each stratum must be proportionate to the number of units in the                          stratum.

- Advantages : The estimate obtained from the sample is more accurate & researcher can compare whether different strata perform differently regarding certain variable of interest.

• Cluster Sampling

- Applicable when the study covers a large geographical area, the population is not homogeneous, sampling frame is not available.

- The clusters can be : residential area, growth corridor in country, development region

- Steps:

                   a. Specify the clusters of interest within the population

                   b. Select randomly a few clusters from the available clusters.

                   c. The respondents are selected from the selected clusters. 

Non-Probability Sampling Design 

•  The element in the population does not have equal chances of being selected as a sample. 
• The result of study is only applicable to the particular sample, do not reflect the whole population
• However, the sampling is very economical & easily performed.

Type of  Non-Probability Sampling Design:

• Convenience Sampling

- The sample is obtain at convenience. 

- The procedure is not random because the respondent is selected because of they happen to be at the right places and at the right time. 

- Advantages : least expensive, least time consuming, sampling units are accessible

- Disadvantages : data obtained is not randomly distributed, selection bias, sample is not representative, not suitable for descriptive & causal research.

• Judgmental Sampling

- The respondents are selected based on the judgement of researcher that they have the required characteristics to be included in the study

• Quota Sampling

- Similar to stratified sampling in that a particular stratum is the focus of the study

- Respondents are selected based on certain characteristics of interest by the researcher. 

• Snowball Sampling

- Researcher will selects one respondent who fulfills certain characteristics for the study
- After obtaining the required data from the first respondent, the researcher will asks this particular respondent to locate his friends/anyone who possess the same characteristics.
- The second respondent will also help the researcher to locate the next respondent.
- Typically used when : the target population is small, the characteristics of population is unique, no sampling frame.

Well kalian boleh jugak refer https://www.ma.utexas.edu/users/parker/sampling/srs.htm untuk lebih lanjut tentang type of sampling design ni :3

Semoga dapat input sikit-sikit dari entry ni. Jangan lupa refer buku untuk lebih mendalam occay!



By the way, if ada readers yang interested untuk dapatkan ebook / software AMOS / file tentang Research Methodology dari Prof Dr Zainudin, boleh email ke saya di :

adiyanasuhaimi@gmail.com


Harga? FOC, don't worry. Sharing is caring eit ;) tapi kalau ada sesiapa yang nak share notes or anything yang boleh dijadikan panduan atau apa-apa saja yang berkaitan dengan ilmu statistics, dialu-alukan untuk email saya okay? Kecil tapak tangan, nyiru saya tadahkan. Gittew :p



And kalau ada sesiapa yang berminat untuk dapatkan servis (amboinyah!) or any helps regarding AMOS or SPSS software, boleh lah email jugak heeeee. Servis kita kira kawan-kawan lah hiks.


See you soon!
Byeeeeeee :]

#3

Assalamualaikum, hello and salam sejahtera everyone.
So seperti mana yang dijanjikan last week (ngeh pemalasnya iolls :p) I'll be updating an entry about some important terminology used in statistics. Sampai bila-bila pun yang paling penting hat ni lah ha. Kalau tak faham, dia akan jadi susah untuk the next-next level, okeng? Don't worry, be happy, buku sentiasa ada as a references ngehngeh so untuk kali ni, saya refer buku Applied Nonparametric Statistics, written by Wayne W. Daniel :))


------------------------------


Last entry kan ada cakap pasal collecting data and stuffs, so bila related dengan data, mestilah ada dua jenis. Satu jenis data yang kita ambik dari population, dan ada juga data yang kita simplify kan, just consider the sample. Apakah perbezaan antara population dengan sample? Bila masa nak guna population and bila masa pulak nak guna sample? Haaaaa. Daripada tulisan Wayne W. Daniel, beliau kata....


Population :

• Collection of persons, places or things, depends on the investigator's or researcher's sphere of interest. It may be defined as the largest collection of persons, places or things in which we have interest. 
• It also may be finite (possible to count the element of which it is composed // boleh dikira) & infinite (composed of limitless number of elements // susah untuk dikira)
• May be either real or hypothetical (impractical to create it)

Sample :

• Part of population 
• Usually researcher akan guna sample bila population terlampau besar dan it is impossible to examine every element in it. So daripada sample yang kita ambil daripada population, conclusions about a population are usually based on the information contained in a sample that has been drawn from that population.
• There are two types of sample :- 

1. Random Sample : 

• Statistical inference consists of reaching conclusions about a population on the basis of information contained in a sample.
• Basically, kita tak boleh lah just select any type of sample, it is not necessarily appropriate kan. So validity of results based on statistical inference rests on the assumption that a special type of sample, called a random sample has been employed in the process. 
• Yang paling common adalah Simple Random Sampling ||  the sample of size n is selected in such a way that every one in the population has the same probability of being selected as a sample. Usually samples are selected through the use of a table of random numbers (dalam buku stats) or with the help of computer which generate the random numbers. The advantage of using SRS is it eliminate biased sebab semua ada equal chances to be selected.
• Btw, random sample bukan hanya ada SRS, ada banyaaakkk lagi, so kita akan go through random sampling dengan lebih mendalam in the next entry, okay? :)

    2.  Non Random Sample / Samples of Convenience

• Selalunya kalau people yang fresh from a statistics course akan macam terkejut mak aih susahnya nak buat random sampling, so instead of random samples drawn with the help of random number tables / random number generated from computer, so they (dari dalam buku Wayne ni ha, page 4) find that sample yang senang adalah sample yang available and convenient. 
• Logically, selalunya kalau samples of convenience ni kita tak boleh depends 100% dan tahap ke-rasional-an nya adalah diragui ramai pihak sebab it may be high biased. Sebolehnya kita mestilah nak conclude population kita takde bias apa-apa, ye dak? Ha centu. 

------------------------------

Next adalah perbezaan antara parameter and statistic. Benda ni simple dan perlu ingat sampai bila-bila ye kawan-kawan. Jangan tertukar. Hiks :3

Parameter :

• A constant that determines the specific form of a density function, from population.
• Example: population mean (μ ), population variance (σ² ), population correlation coefficient (ρ )

Statistic (without s) : 

• A function of one or more random variables, computed from sample. 
• Example: sample mean (x̄), sample variance (s² ), sample correlation coefficient (r)

------------------------------

And next adalah, type of variable. Okay daripada buku Wayne ni, beliau menyatakan ada 3 types of variable which are:

1. Random Variable
2. Continuous Variable
3. Discrete Variable

Random Variable : 

• Usually assume that the numerical data on which we perform statistical analyses are the outcomes of a random sampling procedure or a random experiment. 
• A set of outcomes is called a random variable.
• Observe one or more values of the random variable in the process of sampling or experimenting.

Continuous Variable :

• A random variable is continuous if the values that it can assume consist of all real numbers in some interval; that is; a continuous variable can assume any of the uncountable and infinite number of values within a relevant interval.
• Example: Time interval, time of reaction to some stimulus.

Discrete Variable :

• The number of values that may be either finite or infinite but countable. 
• Might be able to assume values that are fractions or combinations of fractions and whole numbers.

Last but not least yang saya nak share dengan you guys adalah perbezaan antara parametric and non parametric (sebab selalu sangat lupa padahal dah nak grad oi kakaka ), daripada website:-

 (http://changingminds.org/explanations/research/analysis/parametric_non-parametric.htm)

So untuk next entry hmmm we'll see later okay apa yang akan saya update? Stay tuned!

See you soon!
Byeeeeeee :]

#2

Assalamualaikum, hello and salam sejahtera everyone.

So basically, what is statistics? What is the importance of statistics?

(Normally people akan rasa macam arghhh kenapalah kena belajar statistics ni, bukan apply dalam real life pun. Hey salah sebenarnya tanggapan itu ye kawan-kawan.)


Kalau nak term statistics sebeno benonya boleh le google dekat wiki (http://en.wikipedia.org/wiki/Statistics) ye dak. Tapi untuk mudah faham, statistics sebenarnya sangat berkaitrapat dengan data.

Kita ada data (sama ada primary data or secondary data), kita run data tu menggunakan stats software eg:- SPSS / AMOS/ MINITAB dan lain-lain lagi untuk dapatkan result. Dari result tu kita interpret dan bagi suggestion yang membantu dan kemudiannya kita present depan lecturer (?) ataupun management of any company. From the interpretation of that result, management will make further decision untuk kebaikan company dia ataupun long term punya planning. Ha gitu. So siapa kata statistics tak apply dalam real life? *kekening*


For example, ada company A.  Top of management mestilah duduk satu meja dan decide untuk plan for the sake of future for the company. Company mana nak rugi ye dak? So company A kenalah well-planned supaya kos atau rugi dapat diminimumkan dan untung dapat dimaksimakan untuk kebaikan semua pihak.  There are two type of planning, short term planning and long term planning. Kita kena ingat, planning berjalan seiring dengan kos / duit. So company A akan hire statistician sebab statistician basically adalah orang yang expert dalam hal-hal collect data, run analysis dan present result. Dari situ company A akan buat decision untuk run business apa, dekat mana, berapa target keuntungan, apa bajet 10 tahun akan datang etc etc.


Okay nak kena explain lagi ke apa importance of statistics? Rasanya dari example atas tu dah boleh extract the importance of statistics ;) takpe, you guys still can google-ing or refer dalam buku fundamental of stats for more eite.

Next entry kita pergi kepada term-term yang diguna dalam statistics okay?
See you soon!
Byeeeeeee :]

#1

Assalamualaikum, hello and salam sejahtera everyone (sounds so poyo tiba-tiba lel)

Tujuan utama blog ni dibuat ialah :]

1. Sebagai reference sampingan (p/s:  mungkin akan menggunakan terms/perkataans/bayangans/examples yang simple dan mudah untuk difahami)

2. Untuk tak lupa apa yang belajar dekat UiTM sepanjang 3 tahun yang lepas :'>

3. Untuk share knowledge yang tak seberapa. Ngeh.

4. Sebab memang berhasrat untuk buat blog (?)

5. Basically, this blog memang akan menggunakan bahasa rojak. Tapi tak semua la kot, don't worry.

6. Please, please and pleaseeeee, sila rujuk BUKU / STATS LECTURERS dalam apa-apa hal pun, sebab saya sendiri belajar dari mereka :'>



Siapa saya? :]

1. Insan yang lemah lagi hina (cliche lel)

2. Student Bsc Statistics, UiTM Kelantan, bakal graduate May 2015.

3. Idakle teman pandai bebeno, tapi I'll try my best untuk share apa yang saya tahu dan apa yang saya belajar heeeeeee.

4. Kalau ada apa-apa yang saya salah taip / any enquiry untuk penjelasan lebih lanjut, boleh email adiyanasuhaimi@gmail.com or search dekat facebook Amalina Diyana.



See you soon!
Byeeeee :]