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1Labels or Names
Nonnumeric label or numeric code- AOrdinal
- BRatio
- CNominal
- DInterval
2Order or rank
Nonnumeric label or numeric code- ANominal
- BRatio
- COrdinal
- DInterval
3Properties of interval data and the ratio of two values is meaningful
Scale must contain zero value- AOrdinal
- BRatio
- CNominal
- DInterval
4Data have properties of ordinal data and interval between observations is express in terms of a fixed unit of measurement
- ANominal
- BRatio
- CInterval
- DOrdinal
5Business = 1
Humanities = 2
Education = 3- AOrdinal
- BNominal
- CInterval
- DRatio
6Freshman =1
Sophomore =2
Junior =3
Senior =4- ANominal
- BOrdinal
- CInterval
- DRatio
7Melissa's SAT score = 1885
Kevin's SAT score = 1780
150 pts. difference is the....- AOrdinal
- BRatio
- CInterval
- DNominal
8(Zero Value)
Melissa's Credit Hours = 36
Kevin's Credit Hours = 72
Kevin has twice as many as Melissa- AInterval
- BRatio
- COrdinal
- DNominal
9Labels or names
Nominal or ordinal
Numeric or nonnumeric- AQuantitative data
- BCategorical data
- CCross sectional data
- DTimes series data
10Indicates how many or much
always numeric- AQualitative data
- BCategorical data
- CQuantitative data
- DCross sectional data
11Collected at the same time or at the same point in time
- ATimes series data
- BCross-sectional data
- CCategorical data
- DQualitative data
12Collected over several times periods
Helps analysts understand what happened in the past, identify trends, and project future levels- ACross-sectional data
- BTime series data
- CCategorical data
- DQualitative data
13The process of using data obtained from a sample to make estimates and test hypotheses about the characteristics of a population
- ACross-sectional data
- BTime series data
- CStatistical inference
- DCategorical data
14Is the fraction or proportion of the total number of data items belonging to the class
- APercent frequency
- BFrequency
- CRelative frequency
- DCrosstabulation
15Is the relative frequency multiplied by 100
- ARelative frequency
- BPercent frequency
- CCross tabulation
- DFrequency
16Skewness to the right means
- ALonger tail to the right, skewness is -, mean is greater than the median
- BLonger tail to the left, skewness is +, mean is greater than the median
- CLonger tail to the left, skewness is -, mean is less than the median
- DLonger tail to the right, skewness is +, mean is greater than the median
17Skewness to the left means
- ALonger tail to the right, skewness is +, mean is greater than the median
- BLonger tail to the left, skewness is +, mean is greater than the median
- CLonger tail to the right, skewness is -, mean is greater than the median
- DLonger tail to the left, skewness is -, mean is less than the median
18a tabular summer of data for two variables
- APercent frequency
- BCross-sectional data
- CCrosstabulation
- DSimpon's Paradox
19Click all that apply to the following definition, Crosstabulation
- AOne variable is categorical and the other is quantitative
- BNeither variables are categorical
- CNeither variables are quantitative
- DBoth variables are categorical
- EBoth variables are quantitative
20Data in two or more crosstabulations are often aggregated to produce a summary crosstabulation.
In some cases the conclusion based upon an aggregated crosstabulation can be completely reversed if we look at the unaggregated data. The reversal of conclusions based on aggregate and unaggregated data is often called __________- ACross-sectional data
- BSkewness to the right
- CSimpon's Paradox
- DVariance
21If the measures are computed for data from a sample, they are called _____
- Asample statistics
- Bsample data
- Cpopulation parameters
- Dsample mean
22If the measures are computed for data from a population, they are called _____.
- Asample mean
- Bsample statistics
- Cpopulation parameters
- Dsample
23Is a measure of variability that utilizes all data. It is based on the difference between the value of each observations (xi) and the mean (x bar for a sample of u for a population). It is useful in comparing the variability of two or more variables.
It is the average of the squared difference between each data value and the man.- APercentiles
- BMedian
- CVariance
- DStandard deviation
24Is the positive square root of the variance. It is the measure in the same units as the data, making it more easily interpreted than the variance
- AVariance
- BMode
- CStandard Deviation
- DSimpson's Paradox
25denotes the number of standard deviations a data value xi is form the mean.
- APercentiles
- BEmpirical rule
- CStandard deviation
- DZ-score
26Data value < the sample mean with have a z-score [blank_start]<[blank_end] zero
Data value > the sample mean with have a z-score [blank_start]>[blank_end] zero
Data value = the sample mean with have a z-score [blank_start]of[blank_end] zero- A<
- B>
- Cof
27Can be used to determine the percentage of data values that must be withing a specified number of standard deviations of the mean. Based on normal distribution. (Bell-shaped curve)
- ASimpson's paradox
- BEmpirical Rule
- CZ-scores
- DStandard deviation
28[blank_start]68.26[blank_end]% values of a normal random variable are within +/- 1 standard deviation of its mean.
[blank_start]95.44[blank_end]% values of a normal random variable are within +/- 2 standard deviation of its mean.
[blank_start]99.72[blank_end]% values of a normal random variable are within +/- 3 standard deviation of its mean.- A68.26
- B95.44
- C99.72
29is a pure measure of linear association, which adjusts for differences and variability. Is a measure of linear association and not causation.
- ACross-tabulation
- BCorrelation
- CEmpirical Rule
- DZ-scores
30If an experiment consists of a sequence of k steps in which there are n1 possible results for the first step, n2 possible results for the second step, and so on, then the total number of experimental outcomes is given by (n1)(n2)...(nk)
- AVenn Diagram
- BCounting rule for combinations
- CCounting rule for multiple-step experiments
- DSample space
31enables us to count the number of experimental outcomes when n objects are to be selected from a set of N objects
- ACounting rule for multiple-step experiments
- BCounting rule for combinations
- CCR1 and CR2
- DCross-tabulation
32Basic requirements for assigning probabilities
- AThe probability assigned to each experimental outcomes must be between 0 and 2
- BThe sum of probabilities for all experimental outcomes must equal 0
- CThe sum of probabilities for all experimental outcomes must equal 1
- DThe probability assigned to each experimental outcomes must be between -3 and +3
- EThe probability assigned to each experimental outcomes must be between 0 and 1, inclusively
33The probability assigned to each experimental outcomes must be between 0 and 1, inclusively
- A0 < or equal to P(Ei) > or equal to 1 for all i
- B0 < or equal to P(Ei) < or equal to 1 for all i
- C0 < or equal to P(Ei) < or equal to 0 for all i
- D0 > or equal to P(Ei) > or equal to 1 for all i
34The probability assigned to each experimental outcomes must be between 0 and 1, inclusively
- AP(E1)+P(E2)...P(En) = 0
- BP(E1)+P(E2)...P(En) = -1
- CP(E1)+P(E2)...P(En) = 1
- DP(E1)+P(E2)...P(En) = 2
35All experimental outcomes
- AVariable
- BSample space
- CEvent
- DProbability
36A collection of sample points
- ASample space
- BVenn Diagram
- CEvent
- DProbability
37The probability of any [blank_start]event[blank_end] is equal to the sum of the probabilities of the sample points in the event.
- Avariable
- Bsample space
- Cevent
- Dsample points
38collection of sample points that have something in common
- ASample space
- BEvent
- CVenn Diagram
- DIntersection
39Union of Two Events
- AA & B is the set of all sample points that are both in A & B
- BA & B is the containing all sample points that are in A or B or Both, denoted by A n B
- CA & B is the containing all sample points that are in A or B or Both, denoted by A u B
- DA & B is the set of all sample point that are in both A & B, denoted by A n B
40Intersection
- AA & B is the containing all sample point that are in A or B, or both, denoted by A u B
- BA & B is the containing all sample point that are in A or B, or both, denoted by A n B
- CA & B is the set of all sample point that are in both A & B, denoted by A u B
- DA & B is the set of all sample point that are in both A & B, denoted by A n B
41for a random variable describes how probabilities are distributed over the values of a random variable
- ADiscrete Probability distribution
- BBinomial Probability distribution
- CProbability distribution
- DProbability
42Four properties of Binomial Experiment
- AProbability of an occurrence is the same for any 2 intervals of equal length
- BThe experiment consists of a sequence of n identical trials
- CThe occurrence or nonoccurrence in any interval is independent of the occurrence or nonoccurrence in any other interval
- DTwo outcomes, success and failure, are possible on each trial
- EThe probability of success, denoted by p, does not change from trial to trial
- FThe trials are independent
- GOnly one outcome is possible on each trial
43Required conditions for a Discrete Probability function are
- A0 > or equal to f(x) > or equal to 1
E f(x)=1 - B1 < or equal to f(x) > or equal to 0
E f(x)=1 - C1 > or equal to f(x) > or equal to 0
E f(x)=1 - D1 > or equal to f(x) < or equal to 1
E f(x)=1
- A0 > or equal to f(x) > or equal to 1
44Two probabilities of ______
1. Probability of an occurrence is the same for any 2 intervals of equal length
2. The occurrence or nonoccurrence in any interval is independent of the occurrence or nonoccurrence in any other interval- ABinomial probability distribution
- BContinuous Probability distribution
- CPoisson probability disitribution
- DBinomial experiment
45Is often useful in estimating the number of occurrences over a specified interval of time or space
- ABinomial Probability Distribution
- BNormal Probability Distribution
- CDiscrete Probability Distribution
- DPoisson Probability Distribution
46the probability of a random variable assuming a value within some given interval from x1 to x2 is defined to be the area under the graph of the probability density function between x1 and x2 (uniform vs normal)
- ANormal Probability Distribution
- BBinomial Probability Distribution
- CContinuous Probability Distribution
- DStandard normal Probability Distribution
47Is the most important distribution for describing a continuous random variable. Used in statistical inference
- ABinomial Probability Distribution
- BNormal Probability Distribution
- CProbability Distribution
- DStandard Normal Probability Distribution
48a random variable having a normal distribution with a mean of 0 and a standard deviation of 1 is said to have a _________.
- ANormal Probability Distribution
- BBinomial Probability Distribution
- CStandard Normal Probability Distribution
- DContinuous Probability Distribution
49Normal Probability Distribution
- ASkewed to the right, defined by its mean u and its standard deviation o, highest point on the curve is at the mean, also the median and mode.
- BSkewed to the left, defined by its mean u and its standard deviation o, highest point on the curve is at the mean, also the median and mode.
- CSymmetric (skewness is zero), defined by its mean u and its standard deviation o, highest point on the curve is at the mean, also the median and mode.
- DHighly skewed to the right, defined by its standard deviation o, highest point on the curve is at the mean, also the median and mode.
50List of elements that the sample with be selected from
- ASimple random sample
- BSampling Distribution
- CSample frame
- DCentral Limit Theorem
51of size n from a finite population of size N is sample selected such that each possible same of size n has the same probability of being selected. _______ is also often defined as a sample in which each element in population has an equal chance of being selected for the sample (n/N)
- ASampling distribution
- BSample Frame
- CPoint estimation
- DSimple random sample
52_________ is a form of statistical inference. We use data from the sample to compute a value of sample statistic that serve as an estimate of a population parameter.
- ASampling distribution
- BPoint estimation
- CSimple random sample
- DCentral Limit Theorem
53______ of x bar is the probability distribution of all possible values of the sample mean x bar. Can be used to provide the probability information about how close the sample mean x bar is the population mean.
- ACentral limit theorem
- BPoint estimation
- CSampling distribution
- DSimple random sample
54_______ is helpful in identifying the shape of the sampling distribution of x bar. In selecting random samples of size n from a population, the sampling distribution of the sample mean x bar can be approximated by a normal distribution as the sample size becomes large.
- ASampling Distribution
- BSimpson's Paradox
- CSimple Random Sample
- DCentral Limit Theorem
55______, can be computed by adding and subtracting a margin of error to the point estimate. Can also provide info about how close the pint estimate is to the value of the parameter.
- AInterval estimate
- BPoint estimation
- CT distribution
- DDegrees of freedom
56In order to develop an interval estimate of a population mean, the Margin of Error must be computed using:
- AEither the standard deviation or the sample standard deviation
- BThe population standard deviation
- CEither the population standard deviation or the sample standard deviation
- DProbability distribution
57depends on a parameter known as the degrees of freedom
- AZ-score
- BInterval estimate
- CProbability distribution
- Dt distribution
58Degrees of freedom
- AAs it decreases the difference between the t distribution and the standard normal probability distribution becomes smaller and smaller
- BAs it increases the difference between the t distribution and the standard normal probability distribution becomes bigger and bigger
- CAs it increases the difference between the t distribution and the standard normal probability distribution becomes smaller and smaller
- DAs it decreases the difference between the t distribution and the standard normal probability distribution becomes bigger and bigger
59What happened, Two possibilities
- A1. Extreme event
2. Starting assumption is wrong - B1. Starting assumption is wrong
2. Population sample is too big - C1. Extreme event
2. Population sample is too small
- A1. Extreme event
60Once beta is set we can determine _____ before data is collected
- Aalternative hypotheses
- Bsample data
- Cp-value
- Dcritical values
61For beta = .10
- ACritical value (z) = +, - 1.65
- BCritical value (z) = +, - 1.96
- CCritical value (z) = +, - 2.58
62For beta = .05
- ACritical value (z) = +, - 1.65
- BCritical value (z) = +, - 1.96
- CCritical value (z) = +, - 2.58
63For beta = .01
- ACritical value (z) = +, - 1.65
- BCritical value (z) = +, - 1.96
- CCritical value (z) = +, - 2.58
64can be used to determine whether a statement about a value of a population parameter should or should not be rejected
- ANull hypothesis
- BAlternative hypothesis
- CHypothesis testing
- DP-value
65denoted by Ho, is tentative assumption about a population parameter
- AAlternative hypothesis
- BNull hypothesis
- CHypothesis testing
- DP-value
66denoted by Ha, is the opposite of what is stated in null hypothsis
- AHypothesis testing
- BDegrees of freedom
- CNull hypothesis
- DAlternative hypothesis
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- Quiz on Untitled, created by Angela S on 16/12/2015.
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- Angela S
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