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Evaluating Student Performance in Applied Statistics C Evaluating Student Performance in Applied Statistics C

Evaluating Student Performance in Applied Statistics C - PDF document

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Evaluating Student Performance in Applied Statistics C - PPT Presentation

Introduction In professional fields such as education psychology sociology etc applied statistics courses emphasise developing skills in planning quantitative research studies properly analysing data and correctly interpreting the results of analysi ID: 82783

Introduction professional fields

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The sample and number to appreciate the data handling bases problems the small data sets .to recognise the importance (ii) There should values for that students the missing-data conventions being used and that analytical during the (i) For example, databases categorical variables permit regression modelling with coded etc. (iv) outliers or other suspicious values data screening prior to preplanned the above databases should have some taking the courses. Related this, there should be sufficient diversity variables that interesting research hypotheses have found useful in our courses variables abstracted the 1979 I@onnation world and variables comprise demographics, economics, health and education. the demographic variables are collinear values results the cases analysed together. SESAME - variables for 240 pre-school children involved national assessment Sesame Street from Glasnapp Poggio, 1985). The variables include measure, several pre- achievement measures, viewing behaviour and missing data it is for one parental variables. FAC86 variables reported 140 tenure-track faculty in our College for the 1986 (with individual identifying The variables department affiliation, rank, length-of-service measures, measures, and measures. There the cases. GSS84 microcomputer package. variables come from social science survey and represent nearly 1500 respondents. C9