定量数据解析Quantitativedataanalysis.pptVIP

Will G HopkinsAuckland University of TechnologyAuckland NZ Quantitative Data Analysis Summarizing Data: variables; simple statistics; effect statistics and statistical models; complex models. Generalizing from Sample to Population: precision of estimate, confidence limits, statistical significance, p value, errors. Reference: Hopkins WG (2002). Quantitative data analysis (Slideshow). Sportscience 6, /jour/0201/Quantitative_analysis.ppt (2046 words) Evaluation only. Created with Aspose.Slides for .NET 3.5 Client Profile . Copyright 2004-2011 Aspose Pty Ltd. Summarizing Data Data are a bunch of values of one or more variables. A variable is something that has different values. Values can be numbers or names, depending on the variable: Numeric, e.g. weight Counting, e.g. number of injuries Ordinal, e.g. competitive level (values are numbers/names) Nominal, e.g. sex (values are names When values are numbers, visualize the distribution of all values in stem and leaf plots or in a frequency histogram. Can also use normal probability plots to visualize how well the values fit a normal distribution. When values are names, visualize the frequency of each value with a pie chart or a just a list of values and frequencies. Evaluation only. Created with Aspose.Slides for .NET 3.5 Client Profile . Copyright 2004-2011 Aspose Pty Ltd. A statistic is a number summarizing a bunch of values. Simple or univariate statistics summarize values of one variable. Effect or outcome statistics summarize the relationship between values of two or more variables. Simple statistics for numeric variables… Mean: the average Standard deviation: the typical variation Standard error of the mean: the typical variation in the mean with repeated sampling Multiply by ?(sample size) to convert to standard deviation. Use these also for counting and ordinal variables. Use median (middle value or 50th percentile) and quartiles (25th and 75th percentiles) for grossly non-normally distributed data. Summar