MK 370 Lecture Notes - Lecture 20: Recode, Frequency Distribution, Analysis Of Variance
22 Aug 2016
School
Department
Course
Professor
Document Summary
Every column represents a variable (aka a question). String = both alphabets and numbers: fine for names. Numeric = only numbers: fine for age. Data coding: assigning numbers to responses: for gender: male is 1, female is 2, write explanation in values. Data cleaning: making sure there aren"t any errors in data before you analyze: out of range. Ex: 3 on age when there"s only 2 variables. Delete the whole individual (row) from the dataset: finding outliers (mean 3sd, mean + 3sd) Not for gender and age or demographics. If we have an outlier, delete the individual from the dataset: missing value: blank. Do not delete the individual from the dataset. Replace the missing value with the mean. Get rid of the old variable column: normal curve: done for scaled variables only. Finding means: analyze descriptive stats descriptives. Finding percents: analyze descriptive stats frequencies, frequency distribution for class, mba program, sort performance in ascending order.
