![]() \(\lambda\) The speed with which events occur: e.g., earthquakes per earth-day, or heart attacks or traffic fatalities per (population)-year. At a lower level, we might be interested in personal proportions, such as what proportion of the calories a person consumes come from fat, or the proportion of the year 2020 the person spent on the internet, or indoors, or asleep, or sedentary. \(\pi\) Prevalence or risk (proportion): e.g., proportion of the earth's surface that is covered by water, or of a human population that has untreated hypertension, or lacks internet access, or will develop a new health condition over the next x years. Later on we will address sitautions where the mean \(\mu\) is not the best 'centre' of a distribution, and why we might want to take some other feature, such as the median, or some other quantile, instead. The target could be a person's 'true score' on some test - the value one would get if one (could, but not realistically) be tested on each of the (very large) number of test items in the test bank, or observed/measured continously over the period of interest. At a lower level, we might be interested in personal characters, such as the size of a person's vocabulary, or a person's mean (or minimum, or typical) reaction time. the depth of the earth's ocean or height of the land, or the height / BMI / blood pressure levels of a human population. \(\mu\) The mean level of a quantitative characteristic, e.g. The (statistical) parameters we will be concerned with Parameter – any quantitative aspect/dimension of the client’s (patient's) health, subject to measurement (by means of a test). Note that the term can mean other things in other contexts. A numerical characteristic of a population, as distinguished from a statistic obtained by sampling. ![]() Parameter – A constant (of unknown magnitude) in a (statistical) model. We begin by defining is meant by the term parameter in a statistical context See the unity (generality) in what we will be doing in the course, by seeing the big picture, i.e., the forests, not the trees. Understand the concept of a parameter relation or a parameter equationīe able to set up parameter equations that isolate and directly pinpoint parameter differences in both the absolute and relative scales, using a regression equation framework.ĭo so before fitting any such (regression) equations to data, so that we can focus on the research objects without having data get in the way. 18.3 Other Exercises (under construction)ĭefine what a parameter is in a statistical context.18.2.10 Correcting length-biased sampling.18.2.6 Height differences of random M-F pairs.18.2.4 Variable-length (parallel) parking spaces.17.3 Other Exercises (under construction).16.5.2 Statistical Concepts and Principles.15.7.2 Statistical Concepts and Principles.15.4 The p and q functions: an orientation.14.2 Powers, Logarithms and Anti–logarithms.13.3.10 weights of offspring (pups/twins).13.3.8 CI for proportion when observe 0/n or n/n.13.3.6 It's the 3rd week of the course: it must be Binomial.13.3.5 Can one influence the sex of a baby?.13.3.4 Binomial or Opportunistic? (Capitalization on chance.13.3.3 Automated Chemistries (from Ingelfinger et al).12.8 Linear combinations of RVs (regression slopes).12.6 Variance and SD of a FUNCTION of a random variable.12.5.4 Example of Variance-calculation using one-pass formula.12.5.2 Some (good) reasons for using variance, which averages the squares of the deviations from the mean.12.5 Variance (and thus, SD) of a random variable.12.4 Expected value of a FUNCTION of a random variable.12.3 Expectation (mean) of a Random Variable.11.5 Changing the Conditioning: the direction matters.11.4 Conditional probabilities, and (in)dependence.11.3 Basic rules for probability calculations.5.2 Fitting these to data / Estimating them from data.3.2.2 Ingredients and methods of procedure in a statistical test.3.2.1 (Frequentist) Test of a Null Hypothesis.3.1.3 Examples: parameter is a personal number or population mean. ![]() 3.1.2 Example: parameter is a proportion.3.1.1 Example: parameter is 2-valued: yes or no.2.2.2 Parameter relations in symbols, and with the help of an index-category indicator. ![]()
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