Marketing Analytics Final Exam

Marketing Analytics Final Exam

University

58 Qs

quiz-placeholder

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Marketing Analytics Final Exam

Marketing Analytics Final Exam

Assessment

Quiz

Business

University

Easy

Created by

Sarah-= Noel

Used 1+ times

FREE Resource

58 questions

Show all answers

1.

FILL IN THE BLANK QUESTION

1 min • 1 pt

Name 2 dimensions of data quality

2.

FILL IN THE BLANK QUESTION

1 min • 1 pt

Name 2 characteristics of big data

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Identify a valid difference between descriptive analytics and predictive analytics

Descriptive analytics uses data to explain the past, whereas predictive analytics uses data to explain the future.

Descriptive analytics can identify patterns in data whereas predictive analytics can recognize objects from an image.

Descriptive analytics predicts new needs and opportunities whereas predictive analysis reinforces existing beneficial practices.

Descriptive analyticss mimics human-like intelligence, whereas predictive analytics identifies the best optimal decision.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In the SMART analytics principle, the letter "A" refers to an _______ goal-setting technique.

accurate

achiveable

applicable

acceptable

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following types of data can be easily accessed and analyzed when using descriptive, predictive, and prescriptive data analytics techniques?

categorical data

unstructured data

nominal data

structured data

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

A marketing analyst at a gaming company is studying the effect of school holidays on sales of video games, In the study, what type of variable is school holidays?

a target variable

an outcome variable

a dependent variable

an independent variable

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following statement is true about supervvised learning?

The goal of supervised learning is to model underlying structure and distribution in the data.

Supervised learning has no previously defined target variable

In supervised learning, the target variable of interest is known.

Supervised learning is used to discover and confirm patterns in the data.

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