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  2. How to Calculate Your One-Rep Max (Without Actually ... - AOL

    www.aol.com/lifestyle/calculate-one-rep-max...

    Finding your one rep max is a good gage of strength. Here's how to calculate your one rep max, how to use it, how to improve it, and what a good one rep max is.

  3. One-repetition maximum - Wikipedia

    en.wikipedia.org/wiki/One-repetition_maximum

    For example, if a person can lift 100 pounds on a given exercise for 10 reps, the estimated one rep max would be 133 pounds for both formulae. However, if the person were to complete only 6 reps, then Epley would estimate a one rep maximum of approximately 120 pounds, while Brzycki would return an estimate of approximately 116 pounds.

  4. One in ten rule - Wikipedia

    en.wikipedia.org/wiki/One_in_ten_rule

    In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis (in particular proportional hazards models in survival analysis and logistic regression) while keeping the risk of overfitting and finding spurious correlations low. The rule states that one ...

  5. Best linear unbiased prediction - Wikipedia

    en.wikipedia.org/.../Best_linear_unbiased_prediction

    In statistics, best linear unbiased prediction (BLUP) is used in linear mixed models for the estimation of random effects. BLUP was derived by Charles Roy Henderson in 1950 but the term "best linear unbiased predictor" (or "prediction") seems not to have been used until 1962. [ 1 ] "

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  7. Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Bayes_classifier

    Suppose a pair (,) takes values in {,, …,}, where is the class label of an element whose features are given by .Assume that the conditional distribution of X, given that the label Y takes the value r is given by (=) =,, …, where "" means "is distributed as", and where denotes a probability distribution.

  8. Linear predictor function - Wikipedia

    en.wikipedia.org/wiki/Linear_predictor_function

    The basic form of a linear predictor function () for data point i (consisting of p explanatory variables), for i = 1, ..., n, is = + + +,where , for k = 1, ..., p, is the value of the k-th explanatory variable for data point i, and , …, are the coefficients (regression coefficients, weights, etc.) indicating the relative effect of a particular explanatory variable on the outcome.

  9. Remove Banner Ads with Ad-Free AOL Mail | AOL Products

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    SYSTEM REQUIREMENTS. Mobile and desktop browsers: Works best with the latest version of Chrome, Edge, FireFox and Safari. Windows: Windows 7 and newer Mac: MacOS X and newer Note: Ad-Free AOL Mail ...