SlideShare a Scribd company logo
QNT 275 FINAL EXAM JULY 2017
VERSION
Buy Solutions: http://hwsoloutions.com/downloads/qnt-275-final-exam-july-2017-version/
QNT 275 FINAL EXAM JULY 2017
VERSION
QNT 275 FINAL EXAM JULY
2017 VERSION 90% SCORE
QNT 275 FINAL EXAM JULY
2017 VERSION 90% SCORE
We can use the analysis of variance procedure to test hypotheses about:
 the proportion of one population
 two or more population proportions
 two or more population means
 the mean of one population
For a one-tailed test the p-value is:
 twice the area under the curve to the same side of the value of the sample statistic
as is specified in the alternative hypothesis
 the area under the curve to the same side of the value of the sample statistic as is
specified in the alternative hypothesis
 twice the area under the curve between the mean and the observed value of the
sample statistic
 the area under the curve between the mean and the observed value of the sample
statistic
In a hypothesis test a Type I error occurs when:
 a false null hypothesis is not rejected
 a true null hypothesis is rejected
 a true null hypothesis is not rejected
 a false null hypothesis is rejected
You toss a coin nine times and observe 3 heads and 6 tails. This event is a:
 multiple outcome
 simple event
 multinomial sample point
 compound event
The graph of a cumulative frequency distribution is a(n):
 stem-and-leaf display
 frequency histogram
 ogive
 line graph
What is the critical value of t for the hypothesis test?
 2.441
 2.449
 2.733
 2.738
An error that occurs because of chance is called:
 mean error
 probability error
 sampling error
 nonsampling error
A researcher wants to test if elementary school children spend less than 30 minutes
per day on homework. The alternative hypothesis for this example will be that the
population mean is:
 equal to 30 minutes
 not equal to 30 minutes
 less than or equal to 30 minutes
 less than 30 minutes
A quantitative variable is the only type of variable that can:
 have no intermediate values
 be used to prepare tables
 assume numeric values for which arithmetic operations make sense
 be graphed
Which of the following pairs of events are mutually exclusive?
 Female and no
 Female and yes
 Female and male
 No and yes
In a hypothesis test a Type II error occurs when:
 a false null hypothesis is rejected
 a true null hypothesis is rejected
 a true null hypothesis is not rejected
 a false null hypothesis is not rejected
Two paired or matched samples would imply that:
 data are collected on two variables from the elements of two independent samples
 two data values are collected from the same source (elements) for two dependent
samples
 two data values are collected from the same source (elements) for two independent
samples
 data are collected on one variable from the elements of two independent samples
The p-value is the:
 largest significance level at which the alternative hypothesis can be rejected
 smallest significance level at which the null hypothesis can be rejected
 largest significance level at which the null hypothesis can be rejected
 smallest significance level at which the null hypothesis can be rejected
If you divide the number of elements in a sample with a specific characteristic by the
total number of elements in the sample, the dividend is the:
 sampling distribution
 sample distribution
 sample mean
 sample proportion
A linear regression:
 gives a relationship between two variables that can be described by a line
 gives a relationship between two variables that cannot be described by a line
 gives a relationship between three variables that can be described by a line
 contains only two variables
A continuous random variable x has a right-skewed distribution with a mean of 80
and a standard deviation of 12. The sampling distribution of the sample mean for a
sample of 50 elements taken from this population is:
 skewed to the left
 not normal
 approximately normal
 skewed to the right
Which of the following assumptions is not required to use ANOVA?
 All samples are of the same size.
 The samples drawn from different populations are random and independent.
 The populations from which the samples are drawn are (approximately) normally
distributed.
 The populations from which the samples are drawn have the same variance.
The model y = A + Bx is a:
 nonlinear model
 stochastic model
 probabilistic model
 deterministic model
The mean of a discrete random variable is its:
 second quartile
 box-and-whisker measure
 upper hinge
 expected value
In a one-way ANOVA, we analyze only one:
 population
 mean
 variable
 sample
The regression model y = A + Bx + e is:
 an exact relationship
 a probabilistic model
 a nonlinear model
 a deterministic model
For a goodness-of-fit test, the frequencies obtained from the performance of an
experiment are the:
 objective frequencies
 observed frequencies
 subjective frequencies
 expected frequencies
The mean of a discrete random variable is the mean of its:
 frequency distribution
 second and third quartiles
 percentage distribution
 probability distribution
A researcher wants to test if the mean annual salary of all lawyers in a city is
different than $110,000. The null hypothesis for this example will be that the
population mean is:
 greater than to $110,000
 not equal to $110,000
 equal to $110,000
 less than to $110,000
To make tests of hypotheses about more than two population means, we use the:
 t distribution
 normal distribution
 chi-square distribution
 analysis of variance distribution
You randomly select two households and observe whether or not they own a
telephone answering machine. Which of the following is a simple event?
 At most one of them owns a telephone answering machine.
 At least one of them owns a telephone answering machine.
 Neither of the two owns a telephone answering machine.
 Exactly one of them owns a telephone answering machine.
What is the critical value of z for the hypothesis test?
 -2.05
 -2.33
 -2.17
 -1.96
A qualitative variable is the only type of variable that:
 can assume an uncountable set of values
 cannot be measured numerically
 cannot be graphed
 can assume numerical values
The alternative hypothesis is a claim about a:
 statistic, where the claim is assumed to be false until it is declared true
 parameter, where the claim is assumed to be true until it is declared false
 statistic, where the claim is assumed to be true if the null hypothesis is declared
false
 parameter, where the claim is assumed to be true if the null hypothesis is declared
false
For small degrees of freedom, the chi-square distribution is:
 rectangular
 skewed to the left
 symmetric
 skewed to the right
QNT 275 FINAL EXAM JULY
2017 VERSION 90% SCORE

More Related Content

What's hot

Chi square test
Chi square testChi square test
Sign Test
Sign TestSign Test
Sign Test
AdrizaBera
 
Goodness of-fit
Goodness of-fit  Goodness of-fit
Goodness of-fit
Long Beach City College
 
Chi Square Worked Example
Chi Square Worked ExampleChi Square Worked Example
Chi Square Worked Example
John Barlow
 
Hyphotheses testing 6
Hyphotheses testing 6Hyphotheses testing 6
Hyphotheses testing 6
Sundar B N
 
Chi Squared Test
Chi Squared TestChi Squared Test
Chi Squared Test
Darren Barton
 
Chi square test
Chi square testChi square test
Chi square test
NidhiGossai
 
Machine learning session2
Machine learning   session2Machine learning   session2
Machine learning session2
Abhimanyu Dwivedi
 
Linearity cochran test
Linearity cochran testLinearity cochran test
Linearity cochran test
Chandra Prakash Singh
 
Decision between homoscedasticity or heteroscedasticity for linearity data (C...
Decision between homoscedasticity or heteroscedasticity for linearity data (C...Decision between homoscedasticity or heteroscedasticity for linearity data (C...
Decision between homoscedasticity or heteroscedasticity for linearity data (C...
Chandra Prakash Singh
 
Chapter 4 part1-Probability Model
Chapter 4 part1-Probability ModelChapter 4 part1-Probability Model
Chapter 4 part1-Probability Model
nszakir
 
Machine learning session5(logistic regression)
Machine learning   session5(logistic regression)Machine learning   session5(logistic regression)
Machine learning session5(logistic regression)
Abhimanyu Dwivedi
 
Data science
Data scienceData science
Data science
Rakibul Hasan Pranto
 
Chi squared test
Chi squared testChi squared test
Chi squared test
Ramakanth Gadepalli
 
Chi square and t tests, Neelam zafar & group
Chi square and t tests, Neelam zafar & groupChi square and t tests, Neelam zafar & group
Chi square and t tests, Neelam zafar & group
Neelam Zafar
 
wilcoxon signed rank test
wilcoxon signed rank testwilcoxon signed rank test
wilcoxon signed rank test
raj shekar
 
10 Nonparamatric statistics
10 Nonparamatric statistics10 Nonparamatric statistics
10 Nonparamatric statistics
Penny Jiang
 
Chapter 15
Chapter 15 Chapter 15
Chapter 15
Tuul Tuul
 

What's hot (20)

Chi square test
Chi square testChi square test
Chi square test
 
Sign Test
Sign TestSign Test
Sign Test
 
Goodness of-fit
Goodness of-fit  Goodness of-fit
Goodness of-fit
 
Chi Square Worked Example
Chi Square Worked ExampleChi Square Worked Example
Chi Square Worked Example
 
More tabs
More tabsMore tabs
More tabs
 
Regression
RegressionRegression
Regression
 
Hyphotheses testing 6
Hyphotheses testing 6Hyphotheses testing 6
Hyphotheses testing 6
 
Chi Squared Test
Chi Squared TestChi Squared Test
Chi Squared Test
 
Chi square test
Chi square testChi square test
Chi square test
 
Machine learning session2
Machine learning   session2Machine learning   session2
Machine learning session2
 
Linearity cochran test
Linearity cochran testLinearity cochran test
Linearity cochran test
 
Decision between homoscedasticity or heteroscedasticity for linearity data (C...
Decision between homoscedasticity or heteroscedasticity for linearity data (C...Decision between homoscedasticity or heteroscedasticity for linearity data (C...
Decision between homoscedasticity or heteroscedasticity for linearity data (C...
 
Chapter 4 part1-Probability Model
Chapter 4 part1-Probability ModelChapter 4 part1-Probability Model
Chapter 4 part1-Probability Model
 
Machine learning session5(logistic regression)
Machine learning   session5(logistic regression)Machine learning   session5(logistic regression)
Machine learning session5(logistic regression)
 
Data science
Data scienceData science
Data science
 
Chi squared test
Chi squared testChi squared test
Chi squared test
 
Chi square and t tests, Neelam zafar & group
Chi square and t tests, Neelam zafar & groupChi square and t tests, Neelam zafar & group
Chi square and t tests, Neelam zafar & group
 
wilcoxon signed rank test
wilcoxon signed rank testwilcoxon signed rank test
wilcoxon signed rank test
 
10 Nonparamatric statistics
10 Nonparamatric statistics10 Nonparamatric statistics
10 Nonparamatric statistics
 
Chapter 15
Chapter 15 Chapter 15
Chapter 15
 

Similar to Qnt 275 final exam july 2017 version

Non Parametric Test by Vikramjit Singh
Non Parametric Test by  Vikramjit SinghNon Parametric Test by  Vikramjit Singh
Non Parametric Test by Vikramjit Singh
Vikramjit Singh
 
Unit 3
Unit 3Unit 3
312320.pptx
312320.pptx312320.pptx
312320.pptx
YogeshPatel28169
 
STATISTIC ESTIMATION
STATISTIC ESTIMATIONSTATISTIC ESTIMATION
STATISTIC ESTIMATION
Smruti Ranjan Parida
 
Day-2_Presentation for SPSS parametric workshop.pptx
Day-2_Presentation for SPSS parametric workshop.pptxDay-2_Presentation for SPSS parametric workshop.pptx
Day-2_Presentation for SPSS parametric workshop.pptx
rjaisankar
 
ders 5 hypothesis testing.pptx
ders 5 hypothesis testing.pptxders 5 hypothesis testing.pptx
ders 5 hypothesis testing.pptx
Ergin Akalpler
 
Chi square test social research refer.ppt
Chi square test social research refer.pptChi square test social research refer.ppt
Chi square test social research refer.ppt
Snehamurali18
 
Qt notes by mj
Qt notes by mjQt notes by mj
Qt notes by mj
Mahesh Joshi
 
Ds vs Is discuss 3.1
Ds vs Is discuss 3.1Ds vs Is discuss 3.1
Ds vs Is discuss 3.1
Makati Science High School
 
Two Proportions
Two Proportions  Two Proportions
Two Proportions
Long Beach City College
 
1​A linear regression· ​gives a relationship between thre.docx
1​A linear regression· ​gives a relationship between thre.docx1​A linear regression· ​gives a relationship between thre.docx
1​A linear regression· ​gives a relationship between thre.docx
felicidaddinwoodie
 
TEST OF SIGNIFICANCE.pptx
TEST OF SIGNIFICANCE.pptxTEST OF SIGNIFICANCE.pptx
TEST OF SIGNIFICANCE.pptx
muthukrishnaveni anand
 
Chi – square test
Chi – square testChi – square test
Chi – square test
Dr.M.Prasad Naidu
 
Emil Pulido on Quantitative Research: Inferential Statistics
Emil Pulido on Quantitative Research: Inferential StatisticsEmil Pulido on Quantitative Research: Inferential Statistics
Emil Pulido on Quantitative Research: Inferential Statistics
EmilEJP
 
Chi square
Chi square Chi square
Chi square
HemamaliniSakthivel
 
Descriptive And Inferential Statistics for Nursing Research
Descriptive And Inferential Statistics for Nursing ResearchDescriptive And Inferential Statistics for Nursing Research
Descriptive And Inferential Statistics for Nursing Research
enamprofessor
 
250Lec5INFERENTIAL STATISTICS FOR RESEARC
250Lec5INFERENTIAL STATISTICS FOR RESEARC250Lec5INFERENTIAL STATISTICS FOR RESEARC
250Lec5INFERENTIAL STATISTICS FOR RESEARC
LeaCamillePacle
 

Similar to Qnt 275 final exam july 2017 version (20)

Non Parametric Test by Vikramjit Singh
Non Parametric Test by  Vikramjit SinghNon Parametric Test by  Vikramjit Singh
Non Parametric Test by Vikramjit Singh
 
Unit 3
Unit 3Unit 3
Unit 3
 
312320.pptx
312320.pptx312320.pptx
312320.pptx
 
STATISTIC ESTIMATION
STATISTIC ESTIMATIONSTATISTIC ESTIMATION
STATISTIC ESTIMATION
 
Day-2_Presentation for SPSS parametric workshop.pptx
Day-2_Presentation for SPSS parametric workshop.pptxDay-2_Presentation for SPSS parametric workshop.pptx
Day-2_Presentation for SPSS parametric workshop.pptx
 
Sampling theory
Sampling theorySampling theory
Sampling theory
 
Chapter12
Chapter12Chapter12
Chapter12
 
ders 5 hypothesis testing.pptx
ders 5 hypothesis testing.pptxders 5 hypothesis testing.pptx
ders 5 hypothesis testing.pptx
 
Chi square test social research refer.ppt
Chi square test social research refer.pptChi square test social research refer.ppt
Chi square test social research refer.ppt
 
Chi2 Anova
Chi2 AnovaChi2 Anova
Chi2 Anova
 
Qt notes by mj
Qt notes by mjQt notes by mj
Qt notes by mj
 
Ds vs Is discuss 3.1
Ds vs Is discuss 3.1Ds vs Is discuss 3.1
Ds vs Is discuss 3.1
 
Two Proportions
Two Proportions  Two Proportions
Two Proportions
 
1​A linear regression· ​gives a relationship between thre.docx
1​A linear regression· ​gives a relationship between thre.docx1​A linear regression· ​gives a relationship between thre.docx
1​A linear regression· ​gives a relationship between thre.docx
 
TEST OF SIGNIFICANCE.pptx
TEST OF SIGNIFICANCE.pptxTEST OF SIGNIFICANCE.pptx
TEST OF SIGNIFICANCE.pptx
 
Chi – square test
Chi – square testChi – square test
Chi – square test
 
Emil Pulido on Quantitative Research: Inferential Statistics
Emil Pulido on Quantitative Research: Inferential StatisticsEmil Pulido on Quantitative Research: Inferential Statistics
Emil Pulido on Quantitative Research: Inferential Statistics
 
Chi square
Chi square Chi square
Chi square
 
Descriptive And Inferential Statistics for Nursing Research
Descriptive And Inferential Statistics for Nursing ResearchDescriptive And Inferential Statistics for Nursing Research
Descriptive And Inferential Statistics for Nursing Research
 
250Lec5INFERENTIAL STATISTICS FOR RESEARC
250Lec5INFERENTIAL STATISTICS FOR RESEARC250Lec5INFERENTIAL STATISTICS FOR RESEARC
250Lec5INFERENTIAL STATISTICS FOR RESEARC
 

Recently uploaded

Transkredit Finance Company Products Presentation (1).pptx
Transkredit Finance Company Products Presentation (1).pptxTranskredit Finance Company Products Presentation (1).pptx
Transkredit Finance Company Products Presentation (1).pptx
jenomjaneh
 
how can I sell pi coins after successfully completing KYC
how can I sell pi coins after successfully completing KYChow can I sell pi coins after successfully completing KYC
how can I sell pi coins after successfully completing KYC
DOT TECH
 
Intro_Economics_ GPresentation Week 4.pptx
Intro_Economics_ GPresentation Week 4.pptxIntro_Economics_ GPresentation Week 4.pptx
Intro_Economics_ GPresentation Week 4.pptx
shetivia
 
2. Elemental Economics - Mineral demand.pdf
2. Elemental Economics - Mineral demand.pdf2. Elemental Economics - Mineral demand.pdf
2. Elemental Economics - Mineral demand.pdf
Neal Brewster
 
SWAIAP Fraud Risk Mitigation Prof Oyedokun.pptx
SWAIAP Fraud Risk Mitigation   Prof Oyedokun.pptxSWAIAP Fraud Risk Mitigation   Prof Oyedokun.pptx
SWAIAP Fraud Risk Mitigation Prof Oyedokun.pptx
Godwin Emmanuel Oyedokun MBA MSc ACA ACIB FCTI FCFIP CFE
 
Pensions and housing - Pensions PlayPen - 4 June 2024 v3 (1).pdf
Pensions and housing - Pensions PlayPen - 4 June 2024 v3 (1).pdfPensions and housing - Pensions PlayPen - 4 June 2024 v3 (1).pdf
Pensions and housing - Pensions PlayPen - 4 June 2024 v3 (1).pdf
Henry Tapper
 
Webinar Exploring DORA for Fintechs - Simont Braun
Webinar Exploring DORA for Fintechs - Simont BraunWebinar Exploring DORA for Fintechs - Simont Braun
Webinar Exploring DORA for Fintechs - Simont Braun
FinTech Belgium
 
Seminar: Gender Board Diversity through Ownership Networks
Seminar: Gender Board Diversity through Ownership NetworksSeminar: Gender Board Diversity through Ownership Networks
Seminar: Gender Board Diversity through Ownership Networks
GRAPE
 
An Overview of the Prosocial dHEDGE Vault works
An Overview of the Prosocial dHEDGE Vault worksAn Overview of the Prosocial dHEDGE Vault works
An Overview of the Prosocial dHEDGE Vault works
Colin R. Turner
 
Patronage and Good Governance 5.pptx pptc
Patronage and Good Governance 5.pptx pptcPatronage and Good Governance 5.pptx pptc
Patronage and Good Governance 5.pptx pptc
AbdulNasirNichari
 
BYD SWOT Analysis and In-Depth Insights 2024.pptx
BYD SWOT Analysis and In-Depth Insights 2024.pptxBYD SWOT Analysis and In-Depth Insights 2024.pptx
BYD SWOT Analysis and In-Depth Insights 2024.pptx
mikemetalprod
 
The secret way to sell pi coins effortlessly.
The secret way to sell pi coins effortlessly.The secret way to sell pi coins effortlessly.
The secret way to sell pi coins effortlessly.
DOT TECH
 
一比一原版(UCSB毕业证)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB毕业证)圣芭芭拉分校毕业证如何办理一比一原版(UCSB毕业证)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB毕业证)圣芭芭拉分校毕业证如何办理
bbeucd
 
Which Crypto to Buy Today for Short-Term in May-June 2024.pdf
Which Crypto to Buy Today for Short-Term in May-June 2024.pdfWhich Crypto to Buy Today for Short-Term in May-June 2024.pdf
Which Crypto to Buy Today for Short-Term in May-June 2024.pdf
Kezex (KZX)
 
一比一原版(IC毕业证)帝国理工大学毕业证如何办理
一比一原版(IC毕业证)帝国理工大学毕业证如何办理一比一原版(IC毕业证)帝国理工大学毕业证如何办理
一比一原版(IC毕业证)帝国理工大学毕业证如何办理
conose1
 
USDA Loans in California: A Comprehensive Overview.pptx
USDA Loans in California: A Comprehensive Overview.pptxUSDA Loans in California: A Comprehensive Overview.pptx
USDA Loans in California: A Comprehensive Overview.pptx
marketing367770
 
The European Unemployment Puzzle: implications from population aging
The European Unemployment Puzzle: implications from population agingThe European Unemployment Puzzle: implications from population aging
The European Unemployment Puzzle: implications from population aging
GRAPE
 
how to sell pi coins effectively (from 50 - 100k pi)
how to sell pi coins effectively (from 50 - 100k  pi)how to sell pi coins effectively (from 50 - 100k  pi)
how to sell pi coins effectively (from 50 - 100k pi)
DOT TECH
 
What price will pi network be listed on exchanges
What price will pi network be listed on exchangesWhat price will pi network be listed on exchanges
What price will pi network be listed on exchanges
DOT TECH
 
Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...
Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...
Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...
Vighnesh Shashtri
 

Recently uploaded (20)

Transkredit Finance Company Products Presentation (1).pptx
Transkredit Finance Company Products Presentation (1).pptxTranskredit Finance Company Products Presentation (1).pptx
Transkredit Finance Company Products Presentation (1).pptx
 
how can I sell pi coins after successfully completing KYC
how can I sell pi coins after successfully completing KYChow can I sell pi coins after successfully completing KYC
how can I sell pi coins after successfully completing KYC
 
Intro_Economics_ GPresentation Week 4.pptx
Intro_Economics_ GPresentation Week 4.pptxIntro_Economics_ GPresentation Week 4.pptx
Intro_Economics_ GPresentation Week 4.pptx
 
2. Elemental Economics - Mineral demand.pdf
2. Elemental Economics - Mineral demand.pdf2. Elemental Economics - Mineral demand.pdf
2. Elemental Economics - Mineral demand.pdf
 
SWAIAP Fraud Risk Mitigation Prof Oyedokun.pptx
SWAIAP Fraud Risk Mitigation   Prof Oyedokun.pptxSWAIAP Fraud Risk Mitigation   Prof Oyedokun.pptx
SWAIAP Fraud Risk Mitigation Prof Oyedokun.pptx
 
Pensions and housing - Pensions PlayPen - 4 June 2024 v3 (1).pdf
Pensions and housing - Pensions PlayPen - 4 June 2024 v3 (1).pdfPensions and housing - Pensions PlayPen - 4 June 2024 v3 (1).pdf
Pensions and housing - Pensions PlayPen - 4 June 2024 v3 (1).pdf
 
Webinar Exploring DORA for Fintechs - Simont Braun
Webinar Exploring DORA for Fintechs - Simont BraunWebinar Exploring DORA for Fintechs - Simont Braun
Webinar Exploring DORA for Fintechs - Simont Braun
 
Seminar: Gender Board Diversity through Ownership Networks
Seminar: Gender Board Diversity through Ownership NetworksSeminar: Gender Board Diversity through Ownership Networks
Seminar: Gender Board Diversity through Ownership Networks
 
An Overview of the Prosocial dHEDGE Vault works
An Overview of the Prosocial dHEDGE Vault worksAn Overview of the Prosocial dHEDGE Vault works
An Overview of the Prosocial dHEDGE Vault works
 
Patronage and Good Governance 5.pptx pptc
Patronage and Good Governance 5.pptx pptcPatronage and Good Governance 5.pptx pptc
Patronage and Good Governance 5.pptx pptc
 
BYD SWOT Analysis and In-Depth Insights 2024.pptx
BYD SWOT Analysis and In-Depth Insights 2024.pptxBYD SWOT Analysis and In-Depth Insights 2024.pptx
BYD SWOT Analysis and In-Depth Insights 2024.pptx
 
The secret way to sell pi coins effortlessly.
The secret way to sell pi coins effortlessly.The secret way to sell pi coins effortlessly.
The secret way to sell pi coins effortlessly.
 
一比一原版(UCSB毕业证)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB毕业证)圣芭芭拉分校毕业证如何办理一比一原版(UCSB毕业证)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB毕业证)圣芭芭拉分校毕业证如何办理
 
Which Crypto to Buy Today for Short-Term in May-June 2024.pdf
Which Crypto to Buy Today for Short-Term in May-June 2024.pdfWhich Crypto to Buy Today for Short-Term in May-June 2024.pdf
Which Crypto to Buy Today for Short-Term in May-June 2024.pdf
 
一比一原版(IC毕业证)帝国理工大学毕业证如何办理
一比一原版(IC毕业证)帝国理工大学毕业证如何办理一比一原版(IC毕业证)帝国理工大学毕业证如何办理
一比一原版(IC毕业证)帝国理工大学毕业证如何办理
 
USDA Loans in California: A Comprehensive Overview.pptx
USDA Loans in California: A Comprehensive Overview.pptxUSDA Loans in California: A Comprehensive Overview.pptx
USDA Loans in California: A Comprehensive Overview.pptx
 
The European Unemployment Puzzle: implications from population aging
The European Unemployment Puzzle: implications from population agingThe European Unemployment Puzzle: implications from population aging
The European Unemployment Puzzle: implications from population aging
 
how to sell pi coins effectively (from 50 - 100k pi)
how to sell pi coins effectively (from 50 - 100k  pi)how to sell pi coins effectively (from 50 - 100k  pi)
how to sell pi coins effectively (from 50 - 100k pi)
 
What price will pi network be listed on exchanges
What price will pi network be listed on exchangesWhat price will pi network be listed on exchanges
What price will pi network be listed on exchanges
 
Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...
Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...
Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...
 

Qnt 275 final exam july 2017 version

  • 1. QNT 275 FINAL EXAM JULY 2017 VERSION Buy Solutions: http://hwsoloutions.com/downloads/qnt-275-final-exam-july-2017-version/ QNT 275 FINAL EXAM JULY 2017 VERSION QNT 275 FINAL EXAM JULY 2017 VERSION 90% SCORE QNT 275 FINAL EXAM JULY 2017 VERSION 90% SCORE We can use the analysis of variance procedure to test hypotheses about:  the proportion of one population  two or more population proportions  two or more population means  the mean of one population For a one-tailed test the p-value is:  twice the area under the curve to the same side of the value of the sample statistic as is specified in the alternative hypothesis  the area under the curve to the same side of the value of the sample statistic as is specified in the alternative hypothesis  twice the area under the curve between the mean and the observed value of the sample statistic  the area under the curve between the mean and the observed value of the sample statistic In a hypothesis test a Type I error occurs when:  a false null hypothesis is not rejected  a true null hypothesis is rejected  a true null hypothesis is not rejected  a false null hypothesis is rejected You toss a coin nine times and observe 3 heads and 6 tails. This event is a:  multiple outcome  simple event  multinomial sample point  compound event The graph of a cumulative frequency distribution is a(n):  stem-and-leaf display
  • 2.  frequency histogram  ogive  line graph What is the critical value of t for the hypothesis test?  2.441  2.449  2.733  2.738 An error that occurs because of chance is called:  mean error  probability error  sampling error  nonsampling error A researcher wants to test if elementary school children spend less than 30 minutes per day on homework. The alternative hypothesis for this example will be that the population mean is:  equal to 30 minutes  not equal to 30 minutes  less than or equal to 30 minutes  less than 30 minutes A quantitative variable is the only type of variable that can:  have no intermediate values  be used to prepare tables  assume numeric values for which arithmetic operations make sense  be graphed Which of the following pairs of events are mutually exclusive?  Female and no  Female and yes  Female and male  No and yes In a hypothesis test a Type II error occurs when:  a false null hypothesis is rejected  a true null hypothesis is rejected  a true null hypothesis is not rejected  a false null hypothesis is not rejected Two paired or matched samples would imply that:  data are collected on two variables from the elements of two independent samples  two data values are collected from the same source (elements) for two dependent samples  two data values are collected from the same source (elements) for two independent samples  data are collected on one variable from the elements of two independent samples The p-value is the:  largest significance level at which the alternative hypothesis can be rejected  smallest significance level at which the null hypothesis can be rejected  largest significance level at which the null hypothesis can be rejected  smallest significance level at which the null hypothesis can be rejected If you divide the number of elements in a sample with a specific characteristic by the total number of elements in the sample, the dividend is the:  sampling distribution  sample distribution  sample mean  sample proportion A linear regression:  gives a relationship between two variables that can be described by a line
  • 3.  gives a relationship between two variables that cannot be described by a line  gives a relationship between three variables that can be described by a line  contains only two variables A continuous random variable x has a right-skewed distribution with a mean of 80 and a standard deviation of 12. The sampling distribution of the sample mean for a sample of 50 elements taken from this population is:  skewed to the left  not normal  approximately normal  skewed to the right Which of the following assumptions is not required to use ANOVA?  All samples are of the same size.  The samples drawn from different populations are random and independent.  The populations from which the samples are drawn are (approximately) normally distributed.  The populations from which the samples are drawn have the same variance. The model y = A + Bx is a:  nonlinear model  stochastic model  probabilistic model  deterministic model The mean of a discrete random variable is its:  second quartile  box-and-whisker measure  upper hinge  expected value In a one-way ANOVA, we analyze only one:  population  mean  variable  sample The regression model y = A + Bx + e is:  an exact relationship  a probabilistic model  a nonlinear model  a deterministic model For a goodness-of-fit test, the frequencies obtained from the performance of an experiment are the:  objective frequencies  observed frequencies  subjective frequencies  expected frequencies The mean of a discrete random variable is the mean of its:  frequency distribution  second and third quartiles  percentage distribution  probability distribution A researcher wants to test if the mean annual salary of all lawyers in a city is different than $110,000. The null hypothesis for this example will be that the population mean is:  greater than to $110,000  not equal to $110,000  equal to $110,000  less than to $110,000 To make tests of hypotheses about more than two population means, we use the:
  • 4.  t distribution  normal distribution  chi-square distribution  analysis of variance distribution You randomly select two households and observe whether or not they own a telephone answering machine. Which of the following is a simple event?  At most one of them owns a telephone answering machine.  At least one of them owns a telephone answering machine.  Neither of the two owns a telephone answering machine.  Exactly one of them owns a telephone answering machine. What is the critical value of z for the hypothesis test?  -2.05  -2.33  -2.17  -1.96 A qualitative variable is the only type of variable that:  can assume an uncountable set of values  cannot be measured numerically  cannot be graphed  can assume numerical values The alternative hypothesis is a claim about a:  statistic, where the claim is assumed to be false until it is declared true  parameter, where the claim is assumed to be true until it is declared false  statistic, where the claim is assumed to be true if the null hypothesis is declared false  parameter, where the claim is assumed to be true if the null hypothesis is declared false For small degrees of freedom, the chi-square distribution is:  rectangular  skewed to the left  symmetric  skewed to the right QNT 275 FINAL EXAM JULY 2017 VERSION 90% SCORE