MGSC Exam 1

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111 Terms

1

statistics

the science of using an assortment of methods to systematically collect, organize, summarize, analyze, and interpret information

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2

descriptive statistics

tabular, graphical, or numerical summaries of data for a particular group

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3

statistical inference

using data collected from a sample in order to make estimates and test hypotheses about the characteristics of a larger inference

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4

population

the set of all elements of interest in a particular study

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5

census

collecting data for the entire population

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6

sample

subset of the population

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7

sample survey

collecting data for a sample

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8

data

information that we collect and analyze

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9

data set

all of the data that is collected for a study

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10

elements

the subjects of a study; entities on which data is collected

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11

variable

attribute of the subjects/elements we are interested in studying

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12

observation

set of all measurements collected for one subject/element

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13

total data values

elements x variables

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14

measurement scale

the nature of the values that are assigned to variables

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15

nominal

categorical variables; does not indicate ranking (ex. gender, zip code)

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16

ordinal

ranked data; distance between not equal/known (ex. socioeconomic class, TRACE scale)

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17

interval

always numeric; distance between integers are equal; no absolute zero (0 doesn’t mean “absence of”) (ex. temperature, dress size)

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18

ratio

lowest value is always zero; has an absolute zero (0 means “absence of”) (ex. age)

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19

qualitative

nominal or ordinal; use words (or rank number) to describe subjects/elements

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20

quantitative

interval or ratio; use numbers to describe subjects/elements

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21

continuous

variables can take on values between whole numbers (fractions/decimals)

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22

discrete

usually only take on whole number values (except shoe size)

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23

experiment

a variable is specifically manipulated by the researcher

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24

constant

a characteristic of elements/subjects that does not vary from one subject to the next

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25

control variable

held constant in a research study by observing only one of its levels

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26

observational research

levels of independent variable already exist (ex. gender, age); cannot make causal statements; looks for relationships between some set of variables

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27

cross-sectional studies

provide a “snapshot” of different groups at one point in time

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28

time series studies

longitudinal; use data that are collected on the same subjects/elements over several points in time; observe changes over time

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29

effects

changes in data patterns

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30

cyclical effect

any usual/consistent variation in daily, weekly, monthly, or annual data not related to change in season

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31

seasonal effect

change in data that can be explained by/attributed to annual calendar-related events

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32

irregular effect

any change in the data is not related to a regular cycle or season; caused by unusual events

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33

business analytics

the use of data, tehcnology and statistical analysis to answer questions

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34

descriptive analytics

use of data to understand past and current business performance

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35

predictive analytics

use of historical data to identify patterns or relationships and to make predictions about what will happen in the future

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36

prescriptive analytics

identify the best alternatives to minimize or maximize some objective

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37

parameters

greek symbols representing descriptive measure of a population

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38

sample statistics

roman letters representing descriptive measures of a sample

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39

random sample

each member of population has an equal and independent chance of being included in the sample

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40

sampling bias

when a sample is collected in a way that results in some members of the population being more or less likely to be included than others

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41

raw data

data that has not been organized or summarized in any way

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42

frequency distribution

the list of all frequencies for all categoreis

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43

relative frequency distribution

the proportion of the observations that belong to a category f/n

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44

percent frequency distribution

the percent of the observations that belong to a category f/n x 100

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45

class intervals

data divided into sets with equal widths

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46

class midpoint

the value half way between the upper and lower limit of an interval

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47

cumulative frequency distribution

total number of items that have values less than or equal to the upper limit of each class

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48

data vizualization

the process of displaying data meaningully in order to improve decision-making

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49

dashboard

visually summarizes key business information

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50

line charts

display data over time

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51

pie chart

used to display relative frequency or percentage distributions

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52

bar chart

used to visually present qualitative data; separated bars

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53

histograms

display frequency distributions of quantitative variables; no spaces between bars

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54

frequency polygon

points used to depict frequency for each class interval

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55

scatter plot

displays relationship between two quantitative variables

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56

trendline

depicts general direction of the relationship between variables

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57

positive relationship

as x increases, y increases

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58

negative relationship

as x increases, y decreases

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59

symmetrical distribution

similar on both sides

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60

negatively skewed distribution

skewed left; most data fall at upper end

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61

positively skewed distribution

skewed right; most data fall at lower end

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62

skewness

the measure of the symmetry of a data distribution

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63

kurtosis

the measure of how peaked or flat a data distribution is relative to a normal distribution

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64

excess kurtosis

kurtosis - 3

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65

mesokurtic

kurtosis = 3, excess = 0

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66

leptokurtic

kurtosis > 3, excess > 0

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67

platykurtic

kurtosis < 3, excess <0

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68

mildly skewed rule of thumb

skewness between -.5 and +.5

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69

moderately skewed rule of thumb

skewness between -.5 and -1 or between +.5 and =1

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70

highly skewed rule of thumb

skewness less than -1 or greater than +1

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71

standard error calculation

plus or minus 3 times the standard error of skewness

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72

mode

value with highest frequency of occurance; unaffected by outliers

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73

median

the middlemost value when arranged in ascending order; not affected by outliers

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74

median index

(n+1)/2

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75

mean

best measure for normal data; can be pulled in direction of outliers

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76

positive skew central tendencies

mode < Median < mean

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77

negative skew central tendencies

mean < median < mode

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78

mean of the means

if groups are the same size use ___

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79

weighted mean

used to calculate the mean of two or more groups when their sample sizes are not equal

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80

percentiles

divide rank-ordered data into 100 equal parts

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81

quartiles

divide rank-ordered data into 4 equal parts

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82

measures of variability

describe the spread of the data around the center

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83

range

largest value - smallest value; sensitive to outliers

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84

Interquartile Range

Q3-Q1; middle half of data

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85

box and whisker plot

shows the center, the spread, and outliers of a data distribution

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86

five number summary

smallest value(within inner fences), Q1, Q2, Q3, largest value(within inner fences)

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87

inner fences

Q1 - (1.5 x IQR) Q3 + (1.5 x IQR)

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88

outer fences

Q1 - (3 x IQR) Q3 + (3 x IQR)

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89

middle 50% of data positively skewed

if median in box is on the left side; the ___

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90

outer 50% of data positively skewed

if longest whisker is to the right of the box; the ___

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91

deviation score

the distance from any score in teh data to the mean of the distribution

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92

zero

deviation scores add up to ___

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93

sum of squares

sum of squared deviation scores ; total variation in the data set

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94

population variance

sum of squares divided by N; average variability

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95

sample variance

sum of squares divided by n-1; average variability

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96

standard deviation

square root of variance; average deviation around the mean of a distribution

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97

degrees of freedom

the number of scores that are free to vary

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98

empirical rule

applies to normal data; 68% between 1 standard deviation, 95% between 2 standard deviation, 99.7% between 3 standard deviation

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99

chebyshev’s theorem

any data set (skewed); what percentage of data lie within k standard deviations of the mean (k>1)

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100

coefficient of variation

how large the standard deviation is relative to the mean

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