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Total questions: 50
Worksheet time: 50mins
Data analysis includes
data description
data inference
the search for relationships in data
all of these choices
Which statement is not true?
Dealing with uncertainty includes measuring uncertainty
Dealing with uncertainty includes modeling uncertainty explicitly into the analysis
Dealing with uncertainty includes eliminating uncertainty by using the normal probability distribution.
Dealing with uncertainty requires a basic understanding of probability.
A sample of a population taken at one particular point in time is categorized as
categorical
discrete
cross-sectional
time-series
Coding males as 1 and females as 0 in a data set illustrates the use of
nominal variables
dummy variables
numerical variables
ordinal variables
Gender and states of residence are examples of ____ data.
discrete
continuous
categorical
ordinal
The daily closing values of the Dow Jones Industrial Average over a period of 30 days are best described as _____ data
cross-sectional
discrete
time-series
nominal
How is the median defined if the number of observations is even?
the average of the two middle observations
the difference between the two middle observations
the most frequent observation
the difference between the highest and smallest observation
What measure of distribution relates to extreme events, such as a stock market crash?
asymmetric
kurtosis
negatively skewed
skewness
What is the most common type of chart for showing the distribution of a numerical variable?
time series graph
histogram
bin
box plot
The difference between the first and third quartile is called the
interquartile range
interdependent range
unimodal range
bimodal range
mid-range
If a value represents the 95th percentile, this means that
95% of all values are below this value
95% of all values are above this value
95% of the time you will observe this value
there is a 5% chance that this value is incorrect
there is a 95% chance that this value is correct
With symmetric, "bell-shaped" distributions, approximately what percent of the observations are within two standard deviations of the mean?
50%
68%
95%
99.7%
100%
The average score for a class of 30 students was 75. The 20 male students in the class averaged 70. The 10 female students in the class averaged:
the same as the males
higher than the males
significantly lower than the males
little lower than the males
To examine relationships between two categorical variables, we can use
counts and corresponding charts of the counts
scatter plots
histograms
none of these choices
Tables used to display counts of a categorical variable are called
crosstabs
contingency tables
either crosstabs or contingency tables
neither crosstabs nor contingency tables
Scatterplots are also referred to as
crosstabs
contingency charts
X-Y charts
all of these choices
none of these choices
Which correlation coefficient suggests the strongest relationship?
+1
-1
0
+0.5
A line or curve superimposed on a scatterplot to quantify an apparent relationship is known as a(n):
average
trend line
data point
positive variable
slope
A sample in which the sampling units are chosen from the population by means of a random mechanism is a
probability sample
judgmental sample
stratified sample
systematic sample
Potential sample members, called sampling units, are:
people
companies
households
all these choices
In sampling, a population is
the set of all humans
the set of all members about which a study intends to make inferences
any group of test subjects
a random group of individuals, households, cities, or countries
Identifiable subpopulations within a population are called
clusters
samples
blocks
strata
none of the above choices
Selecting a random sample from each identifiable subgroup within a population is called:
demographic sampling
systematic sampling
stratified sampling
cluster sampling
none of these choices
Regression analysis asks
if there are differences between distinct populations
if the sample is representative of the population
how a single variable depends on other relevant variables
how several variables depend on each other
In regression analysis, the variable we are trying to explain or predict is called the
independent variable
dependent variable
regression variable
statistical variable
residual variable
In regression analysis, if there are several explanatory variables, it is called:
simple regression
multiple regression
compound regression
composite regression
Correlation is a summary measure that indicates:
a curved relationship among the variables
the rate of change in Y for a one unit change in X
the strength of the linear relationship between pairs of variables
the magnitude of difference between two variables
The term autocorrelation refers to
the analyzed data refers to itself
the sample is related too closely to the population
the data are in a loop (values repeat themselves)
time series variables are usually related to their own past values
In multiple regression, the coefficients reflect the expected change in
Y when the associated X value increases by one unit
X when the associated Y value increases by one unit
Y when the associated X value decreases by one unit
X when the associated Y value decreases by one unit
In regression analysis, multicollinearity refers to the
response variables being highly correlated
explanatory variables being highly correlated
response variable(s) and the explanatory variable(s) being highly correlated with one another
response variables being highly correlated over time
Simulation models are particularly useful for:
forecasting
obtaining deterministic outputs
evaluating constraints
asking what-if questions
Forecasting models can be divided into three groups. They are
time series, optimization, and simulation methods
judgmental, extrapolation, and econometric methods
judgmental, random, and linear methods
linear, non-linear, and extrapolation methods
Extrapolation methods attempt to:
use non-quantitative methods to predict future values
search for patterns in the data and then use those to predict future values
find variables that are correlated with the data being predicted
predict the next period’s value by using the latest period’s value
Econometric models can also be called:
judgmental models
time series models
causal models
environmetric models
What is a component of a time series?
base series
trend
seasonal component
cyclic component
all of these choices
The forecast error is the difference between:
this period’s value and the next period’s value
the average value and the expected value of the response variable
the explanatory variable value and the response variable value
the actual value and the forecast value
Which of the following is not one of the commonly used summary measures for forecast errors?
MAE (mean absolute error)
MFE (mean forecast error)
RMSE (root mean square error)
MAPE (mean absolute percentage error)
Which summary measure for forecast errors does not depend on the units of the forecast variable?
MAE (mean absolute error)
MFE (mean forecast error)
RMSE (root mean square error)
MAPE (mean absolute percentage error)
A manager at Gampco Inc. wishes to know the company's revenue and profit in its previous quarter. Which of the following business analytics will help the manager?
prescriptive analytics
normative analytics
descriptive analytics
predictive analytics
A trader who wants to predict short-term movements in stock prices is likely to use ________ analytics
predictive
descriptive
normative
prescriptive
(a) refers to how well a model represents reality.
(a) data is continuous and have a natural zero
A (a) is a collection of related files containing records on people, place or things
The term business intelligence was coined by (a) .
(a) helps to determine how specific combinations of inputs that reflect key assumptions affects model outputs
An (a) diagram describes how various elements of the model influence or relate to others
(a) analytics summarizes data into meaningful charts and reports.
(a) analytics uses optimization to identify the best alternatives to minimize or maximize some objective
(a) analytics seeks to predict future by examining historical data, detecting patterns or relationships in these data and then extrapolating these relationships forward in time.
Which of the following questions will prescriptive analytics help a company address?
How many and what types of complaints did they resolve?
What is the best way of shipping goods from their factories to minimize costs?
What do they expect to pay for fuel over the next several months?
What will happen if demand falls by 10% or if supplier prices go up 5%?
