UCF, QMB 3200, Prof. Brook, Midterm Exam

Data

the facts and figures collected, analyzed, and summarized for presentation and interpretation

Dataset

all data collected for analysis

Element

entity on which datais collected

Variable

a characteristic of interest for the elements

Observation

variables associated with an individual element

Categorical Data

measures categories

Quantitative Data

numerical data

Cross-Sectional Data

data collected at the same or approximately the same point in time

Time Series Data

data collected over several time periods

Panel Data

combonation of Cross-Sectional and Time Series data

Descriptive Statistics

describe data or variables

Population

set of all data

Sample

a subset of the population

Stat Inference

uses data from sample to make estimate and test hypothesis about characteristics of a population

Predictive Analytics

predict future or cause and effect

Prespective Analytics

using models to find optimal solution

Volume

number of observations

Velocity

speed that data is collected

Variety

forms of data are different types

Veracity

reliability of data

Data Mining

Focuses on extracting predicitive info from big data

Frequency Distribution

tabular summary of data showing number of observations in each of several non-overlapping categories

Relative Frequency

the frequency divided by the total number of observations

Percent Frequency

relative frequency multiplied by 100

Frequency Formula in excel

=countif(Data Sheet! Range, Frequency Sheet! Value)

width of class formula

range in one class divided by total number of classes

Histogram

bar chart with no space between bars

Cumulative Distributions

Frequency of values less than or equal to the upper limit for each class (Ex. <=8, <=24, etc.)

Crosstabulation

table of two variables

Scatter Plot

graph of 2 quantitative variables

Trendline

correlation line in scatter plot

Mean

Average(Sample mean=x̄, population mean=μ)

Median

middle of data

Mode

value that appears the most

bi-modal

data with 2 modes

multi-modal

data with 3 or more modes

Weighted Mean

mean that weighs certain values more heavily than others (ex. classwork 30%, Exam 60%, Attendance 10%)

Geometric Mean

(x1x2...xn)^1/n

Percentile

A point on a ranking scale of 0 to 100. The 50th percentile is the midpoint; half the people in the population being studied rank higher and half rank lower

Quartiles

how data spread over four quarters

Quartile 1= 25th percentile

Quartile 2= 50th percentile

Quartile 3= 75th percentile

Range

max-min

Interquartile Range

Quartile 3 - Quartile 1; middle 50% of the data

Variance

standard deviation squared

standard deviation for sample equation

((sum of x-x̄)^(2)/(n-1))^1/2

standard deviation for population equation

((sum of x-x̄)^(2)/n)^1/2

Standard Deviation (SD)

measures the average difference between each score and the mean of the data set

coefficient of variation

how large the standard deviation is relative to the mean

distribution shape

skewness (if left=-, right=+, symmetrical=0)

z score

the number of standard deviations a particular score is from the mean

z score formula

(x-x̄)/s

Chebyshev's Theorem

(1-(1/z^2)) of data must be within z standard deviations of the mean

Outlier

when z score is greater than 3 or less than -3

sample correlation coefficient formula

sample covariance between variable x and y / ((StDev of x)(StDev of y))

population correlation coefficient formula

population covariance between variable x and y/ ((StDev of x)(StDev of y))

compliment of an event

all of the outcomes NOT in an event

Union of two events

the event containing all sample points that are in A or B or both

Intersection of two events

event containing only sample points in both events

Addition Law

P(A or B) = P(A) + P(B) - P(A and B)

Mutually Exclusive Events

Events with no overlap

Conditional Probability

the probability of an event ( A ), given that another ( B ) has already occurred.

Uniform Probability Distribution

all possible values have the same possibility

Binomial Probability Distribution Function

P(x:n,p) = (nCx)(p^x)(1-p)^(n-x)

Expected Value (Mean) Formula

sum of number of trials times success rate of each trial

Cumulative Distribution Function Formula Excel

=binom.dist(x,n,p,TRUE)

Probability Mass Function Formula Excel

=binom.dist(x,n,p,FALSE)

variance formula binomial distribution

=npq, p(x-μ)^2

Covarience formula binomial distribution

(var(x+y)-var(x)-var(y))/2

Standard Normal Distribution

a bell curve where the mean is set to 0 and the standard deviation is set to 1 (use z scores)

Standard Normal Distribution Probability Function Excel

=Norm.S.Dist(z,TRUE)

Uniform Probability Distribution

Where everything in a certain range is equally likely to happen (ex. Rolling a Dice once)

Normal Probability Distribution

bell curve

Exponential Probability Distribution Function

f(x)=(1/μ)e^(-x/μ)