Q: A data professional is working for a large corporation. The marketing team asks them to predict the success of a new ad campaign. To make an informed prediction, they use statistics to analyze data on past ad campaigns. What type of probability are they using?
- Dependent
- Independent
- Objective
- Subjective
Q: The probability of an event is close to 1. Which of the following
statements best describes the likelihood that the event will occur?
- The event is unlikely to occur.
- The event is certain to occur.
- The event is certain not to occur.
- The event is likely to occur.
Q: The probability of rain tomorrow is 40%. What is the probability of
the complement of this event?
- The probability of no rain tomorrow is 80%.
- The probability of no rain tomorrow is 20%.
- The probability of no rain tomorrow is 60%.
- The probability of no rain tomorrow is 40%.
Q: Fill in the blank: Two events are _____ if the occurrence of one
event does not change the probability of the other event.
- continuous
- independent
- discrete
- dependent
Q: Fill in the blank: To calculate posterior probability, a data
professional can use _____ to update the prior probability based on the data.
- the normal distribution
- Bayes’s theorem
- the binomial distribution
- the complement rule
Q: Which of the following statements accurately describes a key
difference between discrete and continuous random variables?
- Discrete random variables are typically decimal values that can be measured; continuous random variables are typically whole numbers that can be counted.
- Discrete random variables are typically whole numbers that can be counted; continuous random variables are typically decimal values that can be measured.
- Discrete random variables are positive numbers; continuous random variables are negative numbers.
- Discrete random variables are negative numbers; continuous random variables are positive numbers.*
Q: The Poisson distribution can model which of the following kinds of
data? Select all that apply.
- The number of heads in 10 fair coin tosses
- The number of calls per hour at a call center
- The number of visitors per day on a website
- The number of customers per week at a retail store
Q: A data professional working for a smartphone manufacturer is
analyzing sample data on the weight of a specific smartphone. The data follows
a normal distribution, with a mean weight of 150g and a standard deviation of
10g. According to the empirical rule, approximately what percentage of the data
values lie between 140g and 160g?
- 95%
- 50%
- 68%
- 99.7%
Q: A data value has a z-score of 2.5. Where is it located?
- 2.5 standard deviations below the median
- 2.5 standard deviations above the median
- 2.5 standard deviations below the mean
- 2.5 standard deviations above the mean
Q: A data analytics team at a water utility works with a dataset that
contains information about local reservoirs. They determine that the data
follows a normal distribution. What Python function can they use to compute
z-scores for the data?
- mean.zscore()
- describe()
- median.zscore()
- stats.zscore()
Q: Fill in the blank: The _____ distribution best models the number of
heads in 10 fair coin flips.
- Bernoulli
- Poisson
- Binomial
- Normal
Q: If all outcomes of an event are equally likely, how is its
probability calculated?
- Divide the number of desired outcomes by the total number of possible outcomes.
- Divide the total number of possible outcomes by the number of desired outcomes.
- Divide the total number of certain outcomes by the number of possible outcomes.
- Divide the total number of possible outcomes by the number of certain outcomes.
Q: A coin is tossed twice. To calculate the probability of getting two
heads in a row, which of the following equations should be used?
- ½ ÷ ½
- ½ * ½
- ½ + ½
- ½ – ½
Q: Which of the following events are mutually exclusive? Select all
that apply.
- Getting heads on a first coin toss and tails on a second coin toss
- Getting a 4 on a first die roll and a 6 on a second die roll
- Getting heads and tails on the same coin toss
- Getting a 4 and a 6 on the same die roll
Q: What concept refers to the probability of an event before new data
is collected?
- Prior probability
- Subjective probability
- Conditional probability
- Posterior probability
Q: Which of the following are examples of continuous random variables?
Select all that apply.
- The number of students in a math class
- The height of a redwood tree
- The time it takes for a person to run a race
- The weight of a polar bear
Q: A data professional working for a smartphone manufacturer is
analyzing sample data on the weight of a specific smartphone. The data follows
a normal distribution, with a mean weight of 150g and a standard deviation of
10g. What data value lies 3 standard deviations below the mean?
- 160g
- 120g
- 130g
- 180g
Q: The mean and the standard deviation of a standard normal
distribution always equal what values?
- Mean = 2; standard deviation = 1
- Mean = 0; standard deviation = 2
- Mean = 1; standard deviation = 2
- Mean = 0; standard deviation = 1
Q: A data professional is analyzing sales data for a retail store. The
data follows a normal distribution. What Python function can they use to
compute z-scores for the data?
- stats.zscore()
- median.zscore()
- mean.zscore()
- normal.zscore()
Q: A first coin toss results in tails, and a second coin toss results
in heads. What concept best describes these two events?
- Subjective
- Non-random
- Independent
- Dependent
Q: What concept refers to the probability of an event occurring given
that another event has already occurred?
- Classical probability
- Conditional probability
- Subjective probability
- Empirical probability
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where the probability of both events A and B happening together is denoted by the symbol P(A∩B), and the likelihood of event B occurring is denoted by the symbol P(B) in the case of event A's occurrence.