1.4 Organizing And Summarizing Cat Data

Question 1

Two way frequency tables-> Correlational research

Answer 1

We are still just organizing cat data and deriving conclusions, but with a new method- MAKING DIRECT comparisons between CVs.
JUST observing the relationship between two CVs and seeing if this is evidence of discrimination FE
So we CANT say that these observed relationships are CAUSAL, just evidential of a pattern of TOI.
No manipulated CVs

Question 2

Analyzing two way tables for CONNECTIONS

Answer 2

1. The TOI always come first in question
   If not- derive TOI from context, then attach correlational CW or CV (mostly CW) as support to that TOI (demonstrate connection)
   EX: age discrimination- this phenomenon gives 2 variables -> over 50 and laid off (TOI) compared to under 50 and laid off
   But both have “laid off”, so age is the TOI (denom)
2. DENOM- Generalize the first CV asked about (go to CV label on table, then go straight to its total section)
   DENOM- We are trying to find how many laid off workers were female- so need the number of all the females there were in the FIRST (original) place- CW (regular division)
   EX: age is our MAIN TOI concern, so it is DENOM always because it will always be “over 50 TOTAL”
3. 2nd CV is automatically related to first CV, so connect the label on chart to FIRST CVs label (females- not general- SPECIFICALLY laid off females)
   EX: then “over 50 laid off”

• each numerator and denominator should have (trump) attached to it, IF total is not targeted
• position of label on chart determines its direction
• tip- TOI should give you two CVs interacting with each other- age discrimination: over 50 (laid off) to put up against what will prove your case (CW- over 50 total) LIKLIHOOD

Question 3

Frequency

Answer 3

They are the counts and aren’t enough- (raw data)
When there are more population in a CW, compared to its CV, this affects the chance of how they measure up (give them a chance bro they have 1 billion citizens)
Percents makes it objective

Question 4

Percent shows

Answer 4

ONE person’s probability of falling under TOI circumstance (NOT rate _ rate is just same implication through different means-> % form and “number per” form

Question 5

Key tip

Answer 5

Two way table questions, when you notice that you dont have to look to the table for information, you dont have to follow the rules anymore (proportion or rate context)

Question 6

Density population

Answer 6

Divide each population by the number of square miles