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Lecture 4

PSYC 202 Lecture 4: STATS 202 (week 4)
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15 Pages
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Department
Psychology
Course Code
PSYC 202
Professor
Ronald R Holden

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October 3rd 2017
WEEK 4
Categorical Data
- best way to organize categorical data is through contingency tables
- with these tables we can look at subsets of the data
- purpose is the help display and organize categorical data
- dimensionality of a contingency table depends on how many categorical variables can
be measured on the observational unit
o one-way: measuring one variable, looking at it with columns
total at the bottom of the chart is marginal distribution
o two-way: measuring 2- look at columns in rows
rows distinguish the different variables
we have 2 types of totals, one for the columns and total for each row
row and column are both marginal distributions
- soeties the data is peseted as popotios, thats doe  dividing the number of
individuals in each cell by the total amount of people
- in the total column and total row are marginal proportions, all of those are less than
oe, the ol thig thats  is the gad total aout of people used i sue
conditional distributions
- look at the effects of just one category while controlling for the effects of the other,
rather than looking at them at the same time
- you start with your raw data and you convert everything to percentages
- the way you convert it depends on if your more interested in looking at the columns or
rows
- olus: ell alulate the total fo the olus as a oditioal distiutio
- rows: row total becomes conditional distribution
- since conditional distribution is only looking at column or a row it will always sum to
100%
- marginal distribution will not sum to 100% because your comparing it to the total
number of people surveyed
- will be on the exam*****
find more resources at oneclass.com
find more resources at oneclass.com
Bar Charts
- y-axis shows the number of cases
- x-axis shows each of the categories
- gap between the bars on the x-axis because it shows the first major categorical division-
gap etee shos thees a atego thats diffeet
- pie charts are also used to display categorical data
o ou at ead the raw counts off of a pie chart
o they contain much less information then we have form a bar chart
Grouped bar charts with 2-way information
- take one category and make it the primary category
- then within each grouping show the data for the 2 observation units ie) male and female
- gap between the primary groups but the secondary grouping are touching and do not
have a group
- emphasis the compounds first then look at the gender ratios second
- grouped bar chart still contains all the info from the table
- the secondary groups can have many variables ie) 4 age groups
Histograms
- how to visualize continuous and discrete numerical data
- plot of your sorted and binned data
- first step is to sort data from smallest to largest
- second step is to break that data down into different bins
- ex) breaking each bin into units of 5, there will be varying measures within each
grouping of the range of 5 units
- histogram plots the number of observations we have within each bin ranges
- no gaps between the bars- the x-axis is a binning of numerical variable so they supposed
to be continuous from one bin to the next
- gives us more richness about the data that we have, beyond measuring central tendency
and variation
- to compare 2 sets of units (male and female, US and Canada) you can plot the 2
histograms right onto of the other
- plot fo the frequency for each bin
find more resources at oneclass.com
find more resources at oneclass.com
Cumulative histograms
- plot the cumulative frequencies as you will cross the different bins in the x-axis
- instead of plotting the frequency you sum the frequency going from the left to the right
on the x-axis
- first value in cumulative histogram is the first value form your raw histogram
- the second value of the histogram is the sum of the first and second values from your
original histogram
- 3rd level is the sum of the first 3 bins
- the left hand side is always o and the right hand side is the total number of data
- easy to compare multiple groups- draw as different line son the same figure
Box plots
- a way to visual numerical data
- not quite as informative as a histogram
- but good visual description when you have large groups
- box plots visualize the quartiles of your data set
- y-axis is your numerical variable
- draw a black line to distinguish the median then do your first and 3rd quartile and draw a
line-that box has 50% of your data by definition and spans the IQR
- then draw on whiskers, they represent most of the remaining data but not all of it
find more resources at oneclass.com
find more resources at oneclass.com

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Description
October 3 2017 WEEK 4 Categorical Data best way to organize categorical data is through contingency tables with these tables we can look at subsets of the data purpose is the help display and organize categorical data dimensionality of a contingency table depends on how many categorical variables can be measured on the observational unit o oneway: measuring one variable, looking at it with columns total at the bottom of the chart is marginal distribution o twoway: measuring 2 look at columns in rows rows distinguish the different variables we have 2 types of totals, one for the columns and total for each row row and column are both marginal distributions sometimes the data is presented as proportions, thats done by dividing the number of individuals in each cell by the total amount of people in the total column and total row are marginal proportions, all of those are less than one, the only thing thats 1 is the grand total amount of people used in survey conditional distributions look at the effects of just one category while controlling for the effects of the other, rather than looking at them at the same time you start with your raw data and you convert everything to percentages the way you convert it depends on if your more interested in looking at the columns or rows columns: well calculate the total for the columns as a conditional distribution rows: row total becomes conditional distribution since conditional distribution is only looking at column or a row it will always sum to 100 marginal distribution will not sum to 100 because your comparing it to the total number of people surveyed will be on the exam*****
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