SYLLABUS <br />COURSE : CW 305 INDUSTRIAL STATISTICS<br />CREDIT : 3.0<br />CONTACT HOURS : <br /> 30 HRS (THEORY) & 90 HRS (TUTORIAL/PRACTICAL)<br />PRE-REQUISITE : NONE<br />SYNOPSIS:<br /> INDUSTRIAL STATISTICS introduces students to the basic probability concept and descriptive and inferential statistics. It includes to the collection, analysis and graphic presentation of data and application of statistical method. The emphasis is on application rather than on the theory and calculation. Implementation of SPSS in presenting data.<br />
SYLLABUS<br />LEARNING OUTCOMES<br /> <br />Upon completion of this course, students should be able to:<br />1. Identify correctly the terms and types of statistics used in the industry.<br />2. Explain briefly about the types of sampling methods and data collection to be used in industrial statistics and research purposes.<br />3. Explore widely about various ways to present data including frequency distributions, graphic presentations and SPSS .<br />4. Precisely compute and interpret the data using the measures of central tendency, position and dispersion to get accurate results.<br />5. Solve probability problems correctly including joint and conditional probabilities, using addition, multiplication, permutations, and combination formulas as well as contingency tables and tree diagrams.<br />
ASSESSMENT<br />The course assessment is carried out in two sections:<br />Continuous Assessment(CA) - 50 % <br />Final Examination(FE) - 50% <br />CONTINUOUS ASSESSMENT (CA): <br /> a. Quiz (minimum 3) 20% <br /> b. Test (minimum 2) 40%<br /> c. Others Assessment Task (minimum 3) 40%<br /> <br />i. Tutorial Exercise<br /> ii. Project<br /> iii. Reflective Journal<br /> [ Assessment Task above (a – c (i-iii)) to be executed during Lecture/Practical /Tutorial hour]<br />FINAL EXAMINATION (FE):<br />Final Examination is carried out at the end of the semester.<br />
REFERENCES<br />Downing, Douglas and Jeffrey Clark (2003). Business Statistics: 4th ed. Barron’s Educational Series Inc.<br />J. Medhi. (2005). Statistics Methods: An Introductory Text. New Age International Publishers.<br />Lau Too Kya, PhangYookNgor and ZainuddinAwang (2006). Statistics For UiTM. FajarBaktiSdn. Bhd.<br />Robert A. Donnelly Jr. (2004). The complete idiot’s guide to statistics. Marie Butler Knight.<br />
Chapter 1INTRODUCTION TO STATISTICS<br />1.1 Understand statistics.<br />1.2 Explain types of statistics (descriptive and <br /> inferential statistics)<br />1.3 Explain statistical terms (population, sample, <br /> census, sample survey and pilot study)<br />1.4 Identify types of data (primary and secondary <br /> data) <br />1.5 Explain the variables of statistics (quantitative <br /> discrete & continuous and qualitative)<br />1.6 Understand the scale of measurement (nominal, <br /> ordinal, interval and ratio)<br />
Types of statistics<br />Statistical techniques can divided into 2 categories descriptive and inferential (inductive) statistics.<br />
Types of variables<br />A variable measures a characteristic of the population that the researcher wants to study. <br />-Numerical response which arises from a counting process.<br />-Measured on numerical scale.<br />- Yields numerical response<br />-Numerical response which arises from measuring process.<br />-Measured with non-numerical scale.<br />-Yields categorical response.<br />
Scale of measurement<br />Basically data can be divided into numerical and categorical data. <br />Usually data are classified as nominal, ordinal, interval or ratio.<br />
Chapter 2 SAMPLING AND COLLECTION METHODS<br />2.1 Understand sampling.<br />2.2 Explain the types of sampling methods (non-<br /> probability and probability sampling techniques) <br />2.3 Understand the data collection methods (face –to – <br /> face interview, telephone interview, postal/mail <br /> questionnaire and direct observation)<br /> 2.4 Know how to designing and prepare a questionnaire.<br /> <br />
Chapter 3 DATA PRESENTATION<br />3.1 Understand the data. <br />3.2 Present the qualitative data (plot frequency <br /> distribution, pie chart, pictograph and bar chart: <br /> vertical, horizontal, cluster and stacked bar chart)<br /> 3.3 Present quantitative data(stem and leaf plot, <br /> frequency distribution, histogram, cumulative <br /> polygon and ogive)<br /> 3.4 Present qualitative and quantitative data in SPSS<br />
Chapter 4 NUMERICAL DESCRIPTIVE MEASURES<br />4.1 Understand the measures of central tendency (mean, <br /> mode and median for grouped and ungrouped data) <br />4.2 Understand the measures of position(quartiles, <br /> percentiles and deciles for grouped and ungrouped <br /> data)<br /> 4.3 Understand the measures of dispersion(range, inter-<br /> quartile range, quartile deviation, mean deviation, <br /> variance and standard deviation for grouped and <br /> ungrouped data)<br />
Chapter 5 PROBABILITY<br />5.1 Understand probability.<br />5.2 Identify permutations and combinations.<br />5.3 Apply the rules of probability (event and sample <br /> space, addition rules; mutually exclusive and non-<br /> mutually exclusive events, multiplication rule; <br /> independent and non-independent event)<br />5.4 Construct tree diagrams.<br />5.5 Determine Bayer’s theorem.<br />
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