59172888 introduction-to-statistics-independent-study-requirements-2nd-sem-2010-docx222222
- 1. Introduction to Statistics Independent Study Requirements Frances Ann I. Galanza 1.Define Statistics Statistics is the science of the collection, organization, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments. Data Data refers to qualitative or quantitative attributes of a variable or set of variables. Data are typically the results of measurements and can be the basis of graphs, images, or observations of a set of variables. Data are often viewed as the lowest level of abstraction from which information and then knowledge are derived. Raw data, i.e. unprocessed data, refers to a collection of numbers, characters, images or other outputs from devices that collect information to convert physical quantities into symbols. Variable A variable is a value that may change within the scope of a given problem or set of operations. In computer programming, a variable is a symbolic name given to some known or unknown quantity or information, for the purpose of allowing the name to be used independently of the information it represents. A variable name in computer source code is usually associated with a data storage location and thus also its contents, and these may change during the course of program execution. Statistic A statistic is a single measure of some attribute of a sample. It is calculated by applying a function to the values of the items comprising the sample which are known together as a set of data. More formally, statistical theory defines a statistic as a function of a sample where the function itself is independent of the sample's distribution; that is, the function can be stated before realisation of the data. The term statistic is used both for the function and for the value of the function on a given sample. A statistic is distinct from a statistical parameter, which is not computable because often the population is much too large to examine and measure all its items. However a statistic, when used to estimate a population parameter, is called an estimator. For instance, the sample mean is a statistic which estimates the population mean, which is a parameter.
- 2. Parameter a parameter (G: auxiliary measure) is a quantity that serves to relate functions and variables using a common variable when such a relationship would be difficult to explicate with an equation. In different contexts, the term may have special uses. Parameter- is a computation from data values recorded- but it is not actually a data value recorded from a subject. Example: for a population of test scores, a parameter would not be an actual score, but perhaps an average computed from all scores, or a percent computed from all scores. 2. What are the two branches of Statistics? Differentiate them by giving at least two examples. Two primary branches of statistics: (1) descriptive statistics - organizes raw data into meaningful information; The branch of statistics that focuses on collecting, summarizing, and presenting a set of data. a. The mean age of citizenswholive inacertaingeographical area,the meanlengthof all books aboutstatistics,the variationinthe weightof 100 boxesof cereal selectedfromafactory’s productionline. b. (2) inferential statistics - process of obtaining information about a large group from study of a smaller group.; The branch of statistics that analyzes sample data to reach conclusions about a population a. A surveythatsampled1,264 womenfoundthat45% of those polledconsideredfriendsorfamily as theirmosttrustedshoppingadvisersandonly7% consideredadvertisingastheirmost trustedshoppingadviser.ByusingmethodsdiscussedinSection6.4,youcan use these statistics to draw conclusionsaboutthe populationof all women. b. Descriptive statistics are used to organize or summarize a particular set of measurements. In other words, a descriptive statistic will describe that set of measurements. For example, in our study above, the mean described the absenteeism rates of five nurses on each unit. The U.S. census represents another example of descriptive statistics. In this case, the information that is gathered concerning gender, race, income, etc. is compiled to describe the population of the United States at a given point in time. A baseball player's batting average is another example of a descriptive statistic. It describes the baseball player's past ability to hit a baseball at any point in time. What these three examples have in common is that they organize, summarize, and describe a set of measurements. c. Inferential statistics use data gathered from a sample to make inferences about the larger population from which the sample was drawn. For example, we could take the information gained from our nursing satisfaction study and make inferences to all hospital nurses. We might infer that cardiac care nurses as a group are less satisfied with their jobs as indicated by absenteeism rates. Opinion polls and television ratings systems represent other uses of inferential statistics. For example, a limited number of people are polled during an election and then this information is used to describe voters as a whole.
- 3. 3. What are the different sources of data? Published Sources CONCEPT Data available in print or in electronic form, including data found on Internet websites. Primary data sources are those published by the individual or group that collected the data. Secondary data sources are those compiled from primary sources. EXAMPLE Many U.S. federal agencies, including the Census Bureau, publish primary data sources that are available at the www.fedstats.gov website. Business news sections of daily newspapers commonly publish secondary source data compiled by business organizations and government agencies. INTERPRETATION You should always consider the possible bias of the publisher and whether the data contain all the necessary and relevant variables when using published sources. Remember, too, that anyone can publish data on the Internet. Experiments CONCEPT A study that examines the effect on a variable of varying the value(s) of another variable or variables, while keeping all other things equal. A typical experiment contains both a treatment group and a control group. The treatment group consists of those individuals or things that receive the treatment(s) being studied. The control group consists of those individuals or things that do not receive the treatment(s) being studied. EXAMPLE Pharmaceutical companies use experiments to determine whether a new drug is effective. A group of patients who have many similar characteristics is divided into two subgroups. Members of one group, the treatment group, receive the new drug. Members of the other group, the control group, often receive a placebo, a substance that has no medical effect. After a time period, statistics about each group are compared. INTERPRETATION Proper experiments are either single-blind or double-blind. A study is a single-blind experiment if only the researcher conducting the study knows the identities of the members of the treatment and control groups. If neither the researcher nor study participants know who is in the treatment group and who is in the control group, the study is a double-blind experiment. When conducting experiments that involve placebos, researchers also have to consider the placebo effect—that is, whether people in the control group will improve because they believe they are getting a real substance that is intended to produce a positive result. When a control group shows as much improvement as the treatment group, a researcher can conclude that the placebo effect is a significant factor in the improvements of both groups.
- 4. Surveys CONCEPT A process that uses questionnaires or similar means to gather values for the responses from a set of participants. EXAMPLES The decennial U.S. census mail-in form, a poll of likely voters, a website instant poll or “question of the day.” INTERPRETATION Surveys are either informal, open to anyone who wants to participate; targeted, directed toward a specific group of individuals; or include people chosen at random. The type of survey affects how the data collected can be used and interpreted. 4. What are the different types of quantitative data? 5. What are the different Scale of Measurements? Give 15 example for each scale. Nominal: A scale that measures data by name only. For example, religious affiliation (measured as Jewish, Christian, Buddhist, and so forth), political affiliation (measured as Democratic, Republican, Libertarian, and so forth), or style of automobile (measured as sedan, sports car, station wagon, van, and so forth). Ordinal: Measures by rank order only. Other than rough order, no precise measurement is possible. For example, medical condition (measured as satisfactory, fair, poor, guarded, serious, and critical); social-economic status (measured as lower class, lower-middle class, middle class, upper-middle class, upper class); or military officer rank (measured as lieutenant, captain, major, lieutenant colonel, colonel, general). Such rankings are not absolute but rather “relative” to each other: Major is higher than captain, but we cannot measure the exact difference in numerical terms. Is the difference between major and captain equal to the difference between colonel and general? You cannot say. Interval: Measures by using equal intervals. Here you can compare differences between pairs of values. The Fahrenheit temperature scale, measured in degrees, is an interval scale, as is the centigrade scale. The temperature difference between 50 and 60 degrees centigrade (10 degrees) equals the temperature difference between 80 and 90 degrees centigrade (10 degrees). Note that the 0 in each of these scales is arbitrarily placed, which makes the interval scale different from ratio. Ratio: Similar to an interval scale, a ratio scale includes a 0 measurement that signifies the point at which the characteristic being measured vanishes (absolute 0). For example, income (measured in dollars, with 0 equal to no income at all), years of formal education, items sold, and so forth, are all ratio scales. 6. Enumerate and explain each of the ff. a. Six Different Data gathering procedure (give 2 illustrations for each) b. Five Different Sampling technique (give 2 illustrations for each)
- 5. c Five Methods of presenting data (give 2 examples for each) 1. Accidental Samplingatype of nonprobabilitysampling whichinvolvesthe sample beingdrawn fromthat part of the populationwhichisclose tohand.That is,a sample populationselected because itisreadilyavailableandconvenient.The researcherusingsuchasample cannot scientificallymake generalizationsaboutthe total populationfromthissample becauseitwould not be representative enough.Forexample,if the interviewerwastoconduct such a surveyat a shoppingcenterearlyinthe morningona givenday,the people thathe/she couldinterview wouldbe limitedtothose giventhereatthatgiventime,whichwouldnotrepresentthe views of othermembersof societyinsuchanarea, if the surveywasto be conductedatdifferent timesof day andseveral timesperweek.Thistype of samplingismostuseful forpilottesting. 2. 2. Clustersampling 3. Quota Sampling 4. Simple RandomSampling 5. StratifiedSampling d. Three measuresof central tendency (a) solve 2problemsforeachmeasuresb) cite 3 advantages of eachmeasurescompare withthe other.) 1. mean - are the sum of the numbers divided by the number of numbers. This is also known as average and commonly being used by the psychologist to get the norms in setting standards 2. median- is the midpoint of a distribution: the same number of scores are above the median as below it. commonly being used in the test of relativity and standards 3. mode -is the most frequently occuring value e. Four measures of variability (solve 2 problems for each and compare the advantages each measure over the other) 7. Give 3 solved problems on each of the ff. statistical tests. a. hypothesis testing b. normal distribution c. pearson moment correlation d. spearman rho correlation e. regression analysis f. chi-square test 8. Read one (1) non-MAMC thesis and determine the ff. a. statement of the problem b. statistical test used to solve/answer each identified problem in 8a. c. get the result and interpretation for each for each problem in 8a. Research Paper help https://www.homeworkping.com/ Research Paper help https://www.homeworkping.com/