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In statistical data analysis the total sum of squares (TSS or SST) is a quantity that appears as part of a standard way of presenting results of such analyses.For a set of observations, ,, it is defined as the sum over all squared differences between the observations and their overall mean ¯.: [1]
¯ = sample mean of differences d 0 {\displaystyle d_{0}} = hypothesized population mean difference s d {\displaystyle s_{d}} = standard deviation of differences
Random variables are usually written in upper case Roman letters, such as or and so on. Random variables, in this context, usually refer to something in words, such as "the height of a subject" for a continuous variable, or "the number of cars in the school car park" for a discrete variable, or "the colour of the next bicycle" for a categorical variable.
K – Is used as an abbreviation for 1,000. For example, $225K would be understood to mean $225,000, and $3.6K would be understood to mean $3,600. Multiple K's are not commonly used to represent larger numbers. In other words, it would look odd to use $1.2KK to represent $1,200,000. Ke – Is used as an
initialism = an abbreviation pronounced wholly or partly using the names of its constituent letters, e.g., CD = compact disc, pronounced cee dee; pseudo-blend = an abbreviation whose extra or omitted letters mean that it cannot stand as a true acronym, initialism, or portmanteau (a word formed by combining two or more words).
Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...
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The general regression model with n observations and k explanators, the first of which is a constant unit vector whose coefficient is the regression intercept, is = + where y is an n × 1 vector of dependent variable observations, each column of the n × k matrix X is a vector of observations on one of the k explanators, is a k × 1 vector of true coefficients, and e is an n× 1 vector of the ...