# Lecture 1

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07-Dec-2014Category

## Education

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### Transcript of Lecture 1

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2. Statistics is the science of data collection,organising and interpreting numerical facts. Gaining information from numerical data ormaking sense of data. Descriptive Statistics Organising and summarising data condense large volumes of data into a few summary measures. Statistical inference Generalises subset data findings to the broader universe.2 3. Statistical analysis in management decision-making InputProcessOutput Statistical DecisionData Information AnalysisMakingUseful, Raw TransformationUsableObservationsProcess MeaningfulMANAGEMENT DECISION SUPPORT SYSTEM 3 4. Approach for the Statistical process Research process becoming a cycle PLANNINGDECISION-DATA MAKING COLLECTIONPrimary and Descriptive Statistics secondary Statistical inferencesources EDITING CONCLUSIONSand CODING ANALYSIS 4 5. Basic concepts of Statistics Parameter Computed from the universe. Statistic Computed from the subset taken from the universe. Variable Characteristic of the item being observed or measured. Data Collection of observations on one or more variable.5 6. Basic concepts of Statistics Population Entire group we want information about. Sample The proportion of the population we actually examine. Representative and not biased. Random sampling. 6 7. Basic concepts of Statistics Census Investigate the whole population Expensive Time consuming Sections of population is inaccessible Units are destroyed Inaccurate7 8. Sampling methods Probability sampling Each element has a known probability of beingselected as part of sample. Unbiased inference about the population. Non-probability sampling Element from the population are not selectedrandom. The elements are selected without knowing theprobability of being selected as part of sample. We can not use results of these samples to makeconclusions about the population. 8 9. Sampling methods Probability sampling Simple random sampling Number the elements of the population from 1 to N. Select a random starting point in the random table. From the starting point read systematically in any direction. Divide the digits in the random table into groups with the same number of digits as the number of digits in the population size (N). Find n random numbers from 1 to N no duplicates. Identify each of the chosen random numbers in the population.9 10. Sampling methods Probability sampling Stratified random sampling Population heterogeneous with respect to the variable under study. Population divided into N = N1 + N2 + .. + Nk homogeneous sub- populations called strata. (k = number of stratum) Sample size form each n = n1 + n2 + .. + nk sample proportional to (k = number of stratum) stratum size. Draw a simple random sample N from each of the stratum. n i n, i 1...k i N10 11. Sampling methods Non-probability sampling Convenience sampling Not representative of the target population. Items being selected because they are easy to find, inexpensive and self selected. 11 12. Sampling methods Non-probability sampling Quota sampling Population divided into sub-classes according to a certain characteristic. A non-sampling method is used to select a sample from each stratum. It is a technique of convenience. Researcher attempts to fill the quota quickly. Sample is not representative of the population.12 13. Sampling methods Non-probability sampling Judgement sampling Elements from the population are chosen by the judgement of the researcher. The probability that an element will be chosen cannot be calculated. Sample is biased. 13 14. Sampling methods Non-probability sampling Snowball sampling Is used where sampling units are difficult to locate and identify. Find a person who fits the profile of characteristics of the study. From this person obtain names and locations of others who will fit the profile. 14 15. DIFFERENT TYPES OFDATAQUANTITATIVEQUALITATIVE (numerical scale)(categorical)Discrete Continuous(integer) (any numerical value)15 16. DIFFERENTTYPES OF DATA QUANTITATIVE QUALITATIVE(numerical scale) (categorical)NominalOrdinalInterval Ratio scaledscaledscaled scaled 16 17. Problems associated with the collectionof data: Characteristics have to be measured. Measurements can be complicated. Measurements must be valid and accurate. Secondary data not easy to validate. Data can be incomplete, typographical errors,small sample. Biased or misleading responses.17 18. Problems associated with the collectionof data: Make sure of the following: Who conducted the study? What data was collected? What sampling method was used? Sample size? Chance of bias? Is data relevant to the problem at hand?18 19. How to design a questionnaire Questions should: Be simply stated. Have no suggestion of a specific answer. Be specific and address only one issue. Carefully word sensitive issues. Not require calculations or a study to be answered. Types of questions: Closed Open Combined19 20. Appearance and layout of a questionnaire Attractive look. Coloured paper. Clear instructions on how to complete. Reasonably short. Enough space to complete questions. Mother-tongue language. Interesting questions first. Simple questions first, controversial questions later. Complete one topic before starting the next. Important information first. 20 21. Interview Fieldworker completed questionnaire Higher response rate and data collection is immediate. Mailed questionnaires When population is large or dispersed. Low response rate. Time consuming. Telephone interview Lower costs. Quicker contact with geographically dispersed respondents.21 22. Editing the data Obvious errors should be eliminated. Eliminate questionnaires that are incompleteand unreliable. Questionnaires should be pre-tested on a smallgroup of people. 22