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LFS 252 (5)
Lecture

# Chapter3&4.docx

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School
University of British Columbia
Department
Land & Food Systems
Course
LFS 252
Professor
Erin Friesen
Semester
Winter

Description
Lecture 3 Center and Spread of Distributions for Numerical Variables - Shape- symmetry & modes - Center- typical value - Spread-spread out Mean: describes the center  For skewed right histogram, the means is to the right of the typical value SD: describes the spread  Large SD: the distribution ( bell shape) is wide and short in the center; small SD: the distribution is narrow and high in the center  N( 50,8): Means- 50, SD- 8, sample is normally distributed Define Majority? Use Empirical Rule (approximate) 1SD: 68 % of samples fell in between 1SD away from Mean 2SD : 95 % 3SD: 99 % Z- score: standardize the observations  How many SD away from mean  The resulting units are called Standard units Skewed distribution: samples fell in certain group of intervals, mean will be pulled to the tail of the graphic (NOT IDEAL TO DESCRIBE THE CENTER)  Median will be better representation Median: middle number of the average of the two middle numbers if the sample size is even - Symmetric distribution: medium and mean are similar - Skewed distribution: medium and mean are not similar Quartiles: used to measure spread of a skewed numeric distribution below 1Q: 25% below 3Q 75% IQR( Interquartile range)=Q3-Q1 = 50%  An outlier affect Mean, SD, and Range but not Median and IQR Boxplots: less details (median, Q1, Q3)-> useful for comparing different distributions and potential to find outliers Potential outliers: data value that is a distance of more than 1.5 interquartile ranges ( below 1Q or above 3Q) Boxplots show: - Typical range of values - Possible Q - variation No show - Mode: cant tell which number has more - Mean - Anything for small data sets, especially <5 Lesson 4 Regression analysis ( when we have two numerical variables) - Exam the relationship ( association) between two variabl
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