Question 1

Explain how data mining differs from statistical inference.

Accepted Answer

size of the databases is much

Question 2

According to the regression analysis output below, how much of the variability in tourism revenue is accounted for by the number of foreign visitors? Regression Analysis: Tourism (\$bill) versus visitors (mill) The regression equation is: Tourism (\$ bill) $= 21.5 + 0.295$ visitors (mill)
$\begin{array} { l r r } \text { Predictor } & \text { Coef } & \text { SE Coef } \ \text { Constant } & 21.464 & 3.462 \ \text { Visitors (mill) } & 0.29497 & 0.07917 \end{array}$
$$S = 2.58307 \quad \mathrm { R } - \mathrm { Sq } = 63.4 \%$$

Accepted Answer

A)  63.4 % 
B)  13.8 % 
C)  2.58 billion $ 
D)  21.464 % 
E)  3.73 billion $ 
A)  63.4 % 
B)  13.8 % 
C)  2.58 billion $ 
D)  21.464 % 
E)  3.73 billion $

Question 3

Suppose data mining is used to determine how important it is for a customer to purchase a vehicle with a very low carbon footprint. In data mining this is referred to
As what type of problem?

Accepted Answer

A)  Regression. 
B)  Transactional. 
C)  Unsupervised. 
D)  Classification. 
E)  None of the above. 
A)  Regression. 
B)  Transactional. 
C)  Unsupervised. 
D)  Classification. 
E)  None of the above.

Question 4

A farmer has increased his wheat production by about the same amount each year. His most useful predictive model is most probably

Accepted Answer

A)  exponential. 
B)  linear. 
C)  logarithmic. 
D)  power. 
E)  quadratic. 
A)  exponential. 
B)  linear. 
C)  logarithmic. 
D)  power. 
E)  quadratic.

Question 5

Suppose the goal of data mining using in a data warehouse is to predict whether a customer's expenditures on international specialty food items would increase,
Decrease or stay the same in the next year. What technique might be most appropriate
For achieving this goal?

Accepted Answer

A)  Neural network. 
B)  Unsupervised problem. 
C)  Tree model. 
D)  Nodal network. 
E)  None of the above. 
A)  Neural network. 
B)  Unsupervised problem. 
C)  Tree model. 
D)  Nodal network. 
E)  None of the above.

Question 6

According to the output below, which of the following statement is true about the correlation between stock price and earnings per share (EPS)? The regression equation is: Stock Price $= - 0.49 + 14.8 \mathrm { EPS }$
$\begin{array} { l r r r r } \text { Predictor } & \text { Coef } & \text { SE Coef } & T & \mathrm {~P} \ \text { Constant } & - 0.486 & 4.032 & - 0.12 & 0.906 \ \text { EPS } & 14.8129 & 0.9437 & 15.70 & 0.000 \end{array}$
$$S = 7.63235 \quad \mathrm { R } - \mathrm { Sq } = 95.0 \%$$

Accepted Answer

A)  The correlation is negative. 
B)  The correlation is not significantly different from zero. 
C)  The correlation is positive and significantly different from zero. 
D)  The correlation is positive but not significantly different from zero. 
E)  Cannot be determined from the information given. 
A)  The correlation is negative. 
B)  The correlation is not significantly different from zero. 
C)  The correlation is positive and significantly different from zero. 
D)  The correlation is positive but not significantly different from zero. 
E)  Cannot be determined from the information given.

Question 7

Use the following information 
Suppose data mining is employed on telecommunication company data warehouse in order to answer the following questions. On the line to the right of each, indicate whether these involve a classification (C) or regression (R) problem.
-Whether or not a customer would be interested in flexible cable TV plans (subscribe to different channels on different days/times)? ____

Accepted Answer

The answer of Use the following information 
Suppose data mining...

Question 8

Data are collected on the number of foreign visitors to a country (million) and total tourism revenue ($billion) for a sample of 10 countries. According to the following
Output, what is standard error of the slope for this estimated regression equation? Regression Analysis: Tourism (\$bill) versus Visitors (mill) The regression equation is: Tourism (\$bil1) = $21.5 + 0.295$ visitors (mill)
$\begin{array} { l r r } \text { Predictor } & \text { Coef } & \text { SE Coef } \ \text { Constant } & 21.464 & 3.462 \ \text { Visitors (mill) } & 0.29497 & 0.07917 \ S = 2.58307 & \mathrm { R } - \mathrm { Sq } = 63.4 \div \end{array}$ S = 2.58307 R-Sq = 63.4%

Accepted Answer

A)  2.58307 
B)  3.462 
C)  0.07917 
D)  6.672 
E)  0.29497 
A)  2.58307 
B)  3.462 
C)  0.07917 
D)  6.672 
E)  0.29497

Explain how data mining differs from statistical inference.

Suppose data mining is used to determine how important it is for a customer to purchase a vehicle with a very low carbon footprint. In data mining this is referred to As what type of problem?

A farmer has increased his wheat production by about the same amount each year. His most useful predictive model is most probably

Suppose the goal of data mining using in a data warehouse is to predict whether a customer's expenditures on international specialty food items would increase, Decrease or stay the same in the next year. What technique might be most appropriate For achieving this goal?

Data and Decisions

Displaying and Describing Categorical Data

Displaying and Describing Quantitative Data

Correlation and Linear Regression

Randomness and Probability

Random Variables and Probability Models

The Normal and Other Continuous Distributions

Surveys and Sampling

Sampling Distributions and Confidence Intervals for Proportions

Testing Hypotheses About Proportions

Confidence Intervals and Hypothesis Tests for Means

Comparing Two Means

Inference for Counts: Chi-Square Tests

Inference for Regression

Multiple Regression

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Exam 16: Introduction to Data Mining

Explain how data mining differs from statistical inference.

Suppose data mining is used to determine how important it is for a customer to purchase a vehicle with a very low carbon footprint. In data mining this is referred to As what type of problem?

A farmer has increased his wheat production by about the same amount each year. His most useful predictive model is most probably

Suppose the goal of data mining using in a data warehouse is to predict whether a customer's expenditures on international specialty food items would increase, Decrease or stay the same in the next year. What technique might be most appropriate For achieving this goal?

Data and Decisions

Displaying and Describing Categorical Data

Displaying and Describing Quantitative Data

Correlation and Linear Regression

Randomness and Probability

Random Variables and Probability Models

The Normal and Other Continuous Distributions

Surveys and Sampling

Sampling Distributions and Confidence Intervals for Proportions

Testing Hypotheses About Proportions

Confidence Intervals and Hypothesis Tests for Means

Comparing Two Means

Inference for Counts: Chi-Square Tests

Inference for Regression

Multiple Regression

Filters