DSCI 100 ANSWERS AND QUESTIONS SET A+
✔✔Boostrapping - ✔✔If your sample is big enough it will look like you population
Assume you sample is your population and take a bunch of samples from it
any approximations taken from this is not the true sampling distribution but the
bootstrap distribution
✔✔ Descriptive Question - ✔✔A question that asks about summarized characteristics of
a data set without interpretation (i.e., report a fact).
Example:
How many people live in each province and territory in Canada?
✔✔Exploratory Question - ✔✔A question that asks if there are patterns, trends, or
relationships within a single data set. Often used to propose hypotheses for future
study.
Example:
Does political party voting change with indicators of wealth in a set of data collected on
2,000 people living in Canada?
✔✔Predictive Question - ✔✔A question that asks about predicting measurements or
labels for individuals (people or things). The focus is on what things predict some
outcome, but not what causes the outcome.
Example:
What political party will someone vote for in the next Canadian election?
✔✔Inferential Question - ✔✔A question that looks for patterns, trends, or relationships
in a single data set and also asks for quantification of how applicable these findings are
to the wider population.
Example:
, Does political party voting change with indicators of wealth for all people living in
Canada?
✔✔Causal Question - ✔✔A question that asks about whether changing one factor will
lead to a change in another factor, on average, in the wider population.
Example:
Does wealth lead to voting for a certain political party in Canadian elections?
✔✔Mechanistic Question - ✔✔A question that asks about the underlying mechanism of
the observed patterns, trends, or relationships (i.e., how does it happen?)
Example:
How does wealth lead to voting for a certain political party in Canadian elections?
✔✔Summarization - ✔✔computing and reporting aggregated values pertaining to a data
set. Summarization is most often used to answer descriptive questions, and can
occasionally help with answering exploratory questions. For example, you might use
summarization to answer the following question: What is the average race time for
runners in this data set?
✔✔Visualization - ✔✔plotting data graphically. Visualization is typically used to answer
descriptive and exploratory questions, but plays a critical supporting role in answering
all of the types of question in Table 1.1. For example, you might use visualization to
answer the following question: Is there any relationship between race time and age for
runners in this data set?
✔✔Classification - ✔✔predicting a class or category for a new observation.
Classification is used to answer predictive questions. For example, you might use
classification to answer the following question: Given measurements of a tumor's
average cell area and perimeter, is the tumor benign or malignant?
✔✔Regression - ✔✔predicting a quantitative value for a new observation. Regression is
also used to answer predictive questions. For example, you might use regression to
answer the following question: What will be the race time for a 20-year-old runner who
weighs 50kg?
✔✔Clustering - ✔✔finding previously unknown/unlabeled subgroups in a data set.
Clustering is often used to answer exploratory questions. For example, you might use
clustering to answer the following question: What products are commonly bought
together on Amazon?
✔✔Estimation - ✔✔taking measurements for a small number of items from a large
group and making a good guess for the average or proportion for the large group.
Estimation is used to answer inferential questions. For example, you might use
✔✔Boostrapping - ✔✔If your sample is big enough it will look like you population
Assume you sample is your population and take a bunch of samples from it
any approximations taken from this is not the true sampling distribution but the
bootstrap distribution
✔✔ Descriptive Question - ✔✔A question that asks about summarized characteristics of
a data set without interpretation (i.e., report a fact).
Example:
How many people live in each province and territory in Canada?
✔✔Exploratory Question - ✔✔A question that asks if there are patterns, trends, or
relationships within a single data set. Often used to propose hypotheses for future
study.
Example:
Does political party voting change with indicators of wealth in a set of data collected on
2,000 people living in Canada?
✔✔Predictive Question - ✔✔A question that asks about predicting measurements or
labels for individuals (people or things). The focus is on what things predict some
outcome, but not what causes the outcome.
Example:
What political party will someone vote for in the next Canadian election?
✔✔Inferential Question - ✔✔A question that looks for patterns, trends, or relationships
in a single data set and also asks for quantification of how applicable these findings are
to the wider population.
Example:
, Does political party voting change with indicators of wealth for all people living in
Canada?
✔✔Causal Question - ✔✔A question that asks about whether changing one factor will
lead to a change in another factor, on average, in the wider population.
Example:
Does wealth lead to voting for a certain political party in Canadian elections?
✔✔Mechanistic Question - ✔✔A question that asks about the underlying mechanism of
the observed patterns, trends, or relationships (i.e., how does it happen?)
Example:
How does wealth lead to voting for a certain political party in Canadian elections?
✔✔Summarization - ✔✔computing and reporting aggregated values pertaining to a data
set. Summarization is most often used to answer descriptive questions, and can
occasionally help with answering exploratory questions. For example, you might use
summarization to answer the following question: What is the average race time for
runners in this data set?
✔✔Visualization - ✔✔plotting data graphically. Visualization is typically used to answer
descriptive and exploratory questions, but plays a critical supporting role in answering
all of the types of question in Table 1.1. For example, you might use visualization to
answer the following question: Is there any relationship between race time and age for
runners in this data set?
✔✔Classification - ✔✔predicting a class or category for a new observation.
Classification is used to answer predictive questions. For example, you might use
classification to answer the following question: Given measurements of a tumor's
average cell area and perimeter, is the tumor benign or malignant?
✔✔Regression - ✔✔predicting a quantitative value for a new observation. Regression is
also used to answer predictive questions. For example, you might use regression to
answer the following question: What will be the race time for a 20-year-old runner who
weighs 50kg?
✔✔Clustering - ✔✔finding previously unknown/unlabeled subgroups in a data set.
Clustering is often used to answer exploratory questions. For example, you might use
clustering to answer the following question: What products are commonly bought
together on Amazon?
✔✔Estimation - ✔✔taking measurements for a small number of items from a large
group and making a good guess for the average or proportion for the large group.
Estimation is used to answer inferential questions. For example, you might use
