Presentation on theme: "Statistical basics Marian Scott Dept of Statistics, University of Glasgow August 2008."— Presentation transcript:
Statistical basics Marian Scott Dept of Statistics, University of Glasgow August 2008
What shall we cover? Why might we need some statistical skills Statistical inference- what is it? how to handle variation exploring data probability models inferential tools- hypothesis tests and confidence intervals
Why bother with Statistics We need statistical skills to: Make sense of numerical information, Summarise data, Present results (graphically), Test hypotheses Construct models
statistical language variable- a single aspect of interest population- a large group of individuals sample- a subset of the population parameter- a single number summarising the variable in the population statistic- a single number summarising the variable in the sample
statistical language- Radiation protection- C-14 in fish variable- radiocarbon level (Bq/KgC) population- all fish caught for human consumption in W Scotland sample- 20 fish bought in local markets parameter- population mean C-14 level statistic- sample mean C-14 level
Variables- number and type Univariate: there is one variable of interest measured on the individuals in the sample. We may ask: What is the distribution of results-this may be further resolved into questions concerning the mean or average value of the variable and the scatter or variability in the results?
Bivariate Bivariate two variables of interest are measured on each member of the sample. We may ask : How are the two variables related? If one variable is time, how does the other variable change? How can we model the dependence of one variable on the other?
Multivariate Multivariate many variables of interest are measured on the individuals in the sample, we might ask: What relationships exist between the variables? Is it possible to reduce the number of variables, but still retain 'all' the information? Can we identify any grouping of the individuals on the basis of the variables?
Data types Numerical: a variable may be either continuous or discrete. For a discrete variable, the values taken are whole numbers (e.g. number of invertebrates, numbers of eggs). For a continuous variable, values taken are real numbers (positive or negative and including fractional parts) (e.g. pH, alkalinity, DOC, temperature).
categorical Categorical: a limited number of categories or classes exist, each member of the sample belongs to one and only one of the classes e.g. compliance status is categorical. Compliance is a nominal categorical variable since the categories are unordered. Level of diluent (eg recorded as low, medium,high) would be an ordinal categorical variable since the different classes are ordered
Inference and Statistical Significance Sample Population inference Is the sample representative? Is the population homogeneous? Since only a sample has been taken from the population we cannot be 100% certain Significance testing
what are your objectives? describing a characteristic of interest (usually the average, but could also be the variability or a high percentile), describing spatial patterns of a characteristic,mapping the spatial distribution, quantifying contamination above a background or specified intervention level detecting temporal or spatial trends, assessing environmental impacts of specific facilities, or of events such as accidental releases,
the statistical process A process that allows inferences about properties of a large collection of things (the population) to be made based on observations on a small number of individuals belonging to the population (the sample). The use of valid statistical sampling techniques increases the chance that a set of specimens (the sample, in the collective sense) is collected in a manner that is representative of the population.
Variation soil or sediment samples taken side-by- side, from different parts of the same plant, or from different animals in the same environment, exhibit different activity densities of a given radionuclide. The distribution of values observed will provide an estimate of the variability inherent in the population of samples that, theoretically, could be taken.
Representativeness An essential concept is that the taking of a sufficient number of individual samples should reflect the population. Representativeness of environmental samples is difficult to demonstrate. Usually, representativeness is considered justified by the procedure used to select the samples
What is the population? The population is the set of all items that could be sampled, such as all fish in a lake, all people living in the UK, all trees in a spatially defined forest, or all 20-g soil samples from a field. Appropriate specification of the population includes a description of its spatial extent and perhaps its temporal stability
What are the sampling units? In some cases, sampling units are discrete entities (i.e., animals, trees), but in others, the sampling unit might be investigator-defined, and arbitrarily sized. Example- technetium in shellfish The objective here is to provide a measure (the average) of technetium in shellfish (eg lobsters for human consumption) for the west coast of Scotland. Population is all lobsters on the west coast Sampling unit is an individual animal.
Summarising data- means, medians and other such statistics
plotting data- histograms, boxplots, stem and leaf plots, scatterplots
median lower quartile upper quartile
Preliminary Analysis There is considerable variation –Across different sites –Within the same site across different years Distribution of data is highly skewed with evidence of outliers and in some cases bimodality