Textbook Notes (363,233)
Canada (158,276)
Statistics (133)
STAB22H3 (130)
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Chapter 1


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University of Toronto Scarborough

Examining a Distributions -in any graph of data, look at the overall pattern and for dramatic deviations from that pattern - describe pattern by its shape, center and spread -important kind of deviation is an outlier, an individual value that falls outside the overall pattern -describe center of distribution by its midpoint, the value with roughly half the observations taking smaller values and half taking larger values. We can describe the spread of distribution by giving the smallest and largest values -describe the spread of distribution by stating the smallest and largest values (Q1, Q3) Stemplots and histograms display this. Stemplots on its side with the larger value lies to the right. -Describing shape: Does the distribution have one or several major peaks called modes? Unimodal- one peak Is it symmetric or skewed? Symmetric- values smaller and larger than its midpoint are mirrored. Ex. heights of young women. Skewed- tails. Ex. money amounts, skewed to the right. -outliers: look for points that are clearly apart from the body of data, not just the most extreme observations in a distribution. Sometimes they point to errors made in recording the data. -time plots (pg. 19): of a variable plots each observation against the time at which it was measured. Always put the time on x axis (horizontal) scaled of your plot and the variable you are measuring on y axis. Connecting the points will show change over time. data collected over time, plot observations in time order. Displays of stemplots of histograms ignore time order, so it can be misleading when there is systematic change over time. -time series: measurements of a variable taken at regular intervals over time. Government, economic, and social data are often published as this. Ex. monthly unemployment rate and the quarterly gross domestic product. Time plots reveal the main features of a time series. -in a time series: Trend: is a persistent, long term rise or fall Seasonal variation: a pattern that repeats itself at known regular intervals of a time -many economic time series show strong seasonal variation. Government agencies adjust this variation before releasing economic data, its called seasonally adjusted (helps avoid misinterpretation. -residuals: removing trends and seasonal variation and what remains after the patterns are removed -exploratory data analysis: uses graphs and numerical summaries to describe the variables in a data set and the relations among them -distribution of a variable- what values and how often it takes these values 1.2- Describing Distributions with Numbers -numerical summaries make comparisons more specific www.notesolution.com
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