ch 1
an operational definition is a clear statement that provides a commonm understanding of meaning

no operational definition = errors

dcova uses business oritened data to establish data analysis

primary source - perosn collecting the data from survey/experiment/observation
secondary - not collector but uses census data or published data

**5 sources of data
-data distributed by organization
-outcomes of a designed experiment
-results of conducting observation
-data collected by ongoing business activities

*sampling frame is listing of items that make up the population (frames are data sources such as population, direcotires, or maps)
-usage of different frames to generate data can lead to dissimilar conclusions

frequency distribution ch 2*****
-to determine width of class interval, divide range (high val- low val = ) of data by number of class groupings desired

-when comparing two or more groups with different sample size, you must use relative frequency to compare*****

ch 3****//
coefficient of variation
-measures relative variation and variation to the mean ( always in %)
- can be used to ocmpare variability of two or more sets of data measured in different units
-formula is standard deviation/ mean

Z-SCORE of data value, SUBTRACT THE MEAN AND DIVIDE BY STANDARD DEVIATION (considered outlier if zscore less than -3 or greater than +3 (larger val of z score, farther away from mean

****

FIND QUARTILES POSITION!!! EACH UNIT IS 1
q1 =  (n+1)/4
q2 = (n+1)/2
q3 = (3(n+1)/4
N is number of observed values

find IQR by q3-q1 =IQR, that would be the range of the q3 - q1

5 number summary** related to boxplots
-smallest number
-first quartile
-median (second quartile)
-third quartile

ask teacher about chebyshev rule

scatter plot in depth
2 quantitivate measures of such relationships
-covariance
- coefficient of correlation

covariance = strength of linear relationship between two numerical values
coeff of corre = relative strength of linear relationship between two numerical val

they focus on coeff of correl

Excel function is =CORREL(AX1:AX2,BX1:BX2)

this compares the relationship between A and B and gives the Coeff of Correlation

basically, to interpret the numbers outputted, it ranges from -1 to 1, closer to -1 is negative linear relationship, closer to 0 is no linear relationship, and 1 is positive

flaw of covariance is inability to determine relative strength of relationship from size of covariance

empirical rule is 65% is witihin one standard dev , 95 2 standard dev, 99.7 3 standard dev

ch 4
mutually exclusive events, A = day in jan, B= day in feb, cannot occur simulataneously, so it's mutually exclusive event

collectively exhaustive events
-set of events cover all sample space, must occur, doesnt have to be mutually exclusive//

general addition rule is used as ( P(A or B) = p(a) +p(b) - p(a and b))

if a and b are mutually exclusive ( cannot happen same time) then p(a and b) = 0

can be rewrote as p(a or b) = p(a) +p(b)