DATA1001 Study Guide - Final Guide: Root Mean Square, Sample Size Determination, Central Limit Theorem

• School: University of Sydney
• Department: Data Science
• Course: DATA1001
• Professor: Di Warren

Document Summary

Median: (cid:373)iddle data poi(cid:374)t, (cid:449)he(cid:374) the data is ordered fro(cid:373) s(cid:373)allest to largest. Turn data point into standard unit: (cid:4666)(cid:3005)(cid:3028)(cid:3047)(cid:3028) (cid:3043)(cid:3042)(cid:3041)(cid:3047)(cid:4667) (cid:3040)(cid:3032)(cid:3028)(cid:3041) (cid:3046)(cid:3047)(cid:3028)(cid:3041)(cid:3031)(cid:3028)(cid:3045)(cid:3031) (cid:3031)(cid:3032)(cid:3049)(cid:3028)(cid:3047)(cid:3042)(cid:3041) Data outside these thresholds are considered outliers/extreme readings. P (event 1 + event 2 occur) = p(event 1) x. Mea(cid:374) of (cid:4666)gaps fro(cid:373) (cid:373)ea(cid:374)(cid:4667)(cid:1006) (cid:4666)big - s(cid:373)all(cid:4667) proportio(cid:374) of big proportio(cid:374) of s(cid:373)all. = su(cid:373) of data size of data res = nw - l. values. ## [1] 13. 22338 sqrt(mean(res^2)) # rms error (pop) (cid:374)u(cid:373)(cid:271)er of dra(cid:449)s (cid:4666)sd of the (cid:271)o(cid:454)(cid:4667) correction factor x se with replacement. Steps: write out the gaps from mean, square these values, work out the mean of the new values, find the square root of this mean. Sd of (cid:271)o(cid:454) expected value (ev) + standard error (se) Confidence intervals: if we worked out a series of. Cis for a series of samples, then 95% of the cis would. Confidence intervals sample proportion 1 se sample contains the unknown parameter.