Business Analytics
2nd Edition
ST
UV
TEST BANK
IA
_A
Sanjiv Jaggia
PP
Alison Kelly
Kevin Lertwachara
RO
Leida Chen
────────────────────────────────────────────────────
VE
Comprehensive Test Bank for Instructors
and Students
D ?
© Sanjiv Jaggia, Alison Kelly, Kevin Lertwachara & Leida Chen.
All rights reserved. Reproduction or distribution without permission is prohibited.
© BooksbyMed
, Business Analytics – 2nd Edition
(ISBN 9781264302802)
Sanjiv Jaggia, Alison Kelly, Kevin Lertwachara, and Leida Chen
TABLE OF CONTENTS
Chapter 1. Introduction to Business Analytics
ST
Chapter 2. Data Management and Wrangling
Chapter 3. Summary Measures
Chapter 4. Data Visualization
UV
Chapter 5. Probability and Probability Distributions
Chapter 6. Statistical Inference
Chapter 7. Regression Analysis
IA
Chapter 8. More Topics in Regression Analysis
Chapter 9. Logistic Regression
Chapter 10. Forecasting with Time Series Data
_A
Chapter 11. Introduction to Data Mining
Chapter 12. Supervised Data Mining: k-Nearest Neighbors and Naive Bayes
Chapter 13. Supervised Data Mining: Decision Trees
Chapter 14. Unsupervised Data Mining
PP
Chapter 15. Spreadsheet Modeling
Chapter 16. Risk Analysis and Simulation
Chapter 17. Optimization: Linear Programming
RO
Chapter 18. More Applications in Optimization
VE
D ?
© BooksbyMed
,Student name:__________
1) The ability to use qualitative reasoning with quantitative tools allows management to make
decisions to improve business performance.
⊚ true
⊚ false
ST
2) The use of historical information to predict what could happen in the future describes
prescriptive analytics.
⊚ true
UV
⊚ false
3) Data are the compilation of facts, figures, or other contents, both numerical and non-
IA
numerical.
⊚ true
⊚ false
_A
4) Generally speaking, it is not feasible to obtain complete population data due to expense and
near impossibility to examine every member of the population.
⊚ true
PP
⊚ false
RO
5) Collecting height data annually on the sample set of participants is an example of time series
data.
⊚ true
⊚ false
VE
6) Structured and unstructured data are only machine generated.
⊚ true
⊚ false
D?
Version 1 1
, 7) Numerical variables are either discrete or continuous.
⊚ true
⊚ false
8) When each piece of data in a file is separated by a comma, it is called delimiter and the file is
called a comma-spliced file.
ST
⊚ true
⊚ false
UV
9) When coding in HTML, <table> is a tag used to provide structure for textual data.
⊚ true
⊚ false
IA
10) In XML, tags are not case-sensitive and are interchangeable. For example, <City>and
<city>represent the same pieces of information.
_A
⊚ true
⊚ false
PP
11) Sally created a table that summarizes the dollar amount of last year’s sales for each store.
This is an example of descriptive analytics.
⊚ true
RO
⊚ false
12) Social media data, such as Twitter, Facebook, and TicTok are examples of structured data.
⊚ true
VE
⊚ false
13) The characteristics marital status and income are examples of observations.
D?
⊚ true
⊚ false
Version 1 2
2nd Edition
ST
UV
TEST BANK
IA
_A
Sanjiv Jaggia
PP
Alison Kelly
Kevin Lertwachara
RO
Leida Chen
────────────────────────────────────────────────────
VE
Comprehensive Test Bank for Instructors
and Students
D ?
© Sanjiv Jaggia, Alison Kelly, Kevin Lertwachara & Leida Chen.
All rights reserved. Reproduction or distribution without permission is prohibited.
© BooksbyMed
, Business Analytics – 2nd Edition
(ISBN 9781264302802)
Sanjiv Jaggia, Alison Kelly, Kevin Lertwachara, and Leida Chen
TABLE OF CONTENTS
Chapter 1. Introduction to Business Analytics
ST
Chapter 2. Data Management and Wrangling
Chapter 3. Summary Measures
Chapter 4. Data Visualization
UV
Chapter 5. Probability and Probability Distributions
Chapter 6. Statistical Inference
Chapter 7. Regression Analysis
IA
Chapter 8. More Topics in Regression Analysis
Chapter 9. Logistic Regression
Chapter 10. Forecasting with Time Series Data
_A
Chapter 11. Introduction to Data Mining
Chapter 12. Supervised Data Mining: k-Nearest Neighbors and Naive Bayes
Chapter 13. Supervised Data Mining: Decision Trees
Chapter 14. Unsupervised Data Mining
PP
Chapter 15. Spreadsheet Modeling
Chapter 16. Risk Analysis and Simulation
Chapter 17. Optimization: Linear Programming
RO
Chapter 18. More Applications in Optimization
VE
D ?
© BooksbyMed
,Student name:__________
1) The ability to use qualitative reasoning with quantitative tools allows management to make
decisions to improve business performance.
⊚ true
⊚ false
ST
2) The use of historical information to predict what could happen in the future describes
prescriptive analytics.
⊚ true
UV
⊚ false
3) Data are the compilation of facts, figures, or other contents, both numerical and non-
IA
numerical.
⊚ true
⊚ false
_A
4) Generally speaking, it is not feasible to obtain complete population data due to expense and
near impossibility to examine every member of the population.
⊚ true
PP
⊚ false
RO
5) Collecting height data annually on the sample set of participants is an example of time series
data.
⊚ true
⊚ false
VE
6) Structured and unstructured data are only machine generated.
⊚ true
⊚ false
D?
Version 1 1
, 7) Numerical variables are either discrete or continuous.
⊚ true
⊚ false
8) When each piece of data in a file is separated by a comma, it is called delimiter and the file is
called a comma-spliced file.
ST
⊚ true
⊚ false
UV
9) When coding in HTML, <table> is a tag used to provide structure for textual data.
⊚ true
⊚ false
IA
10) In XML, tags are not case-sensitive and are interchangeable. For example, <City>and
<city>represent the same pieces of information.
_A
⊚ true
⊚ false
PP
11) Sally created a table that summarizes the dollar amount of last year’s sales for each store.
This is an example of descriptive analytics.
⊚ true
RO
⊚ false
12) Social media data, such as Twitter, Facebook, and TicTok are examples of structured data.
⊚ true
VE
⊚ false
13) The characteristics marital status and income are examples of observations.
D?
⊚ true
⊚ false
Version 1 2
