WorksheetsEngineering Science Data Analysis Quiz
Total questions: 30
Worksheet time: 20mins
What does a scatter plot help you visualize when comparing two data sets?
How big the data sets are
Whether a correlation exists between the two variables
The average of the data
The percentages of each data set
What does the R² value (R-squared) tell you?
How random your data is
How many data points were used
The strength of the relationship between two variables
The exponential equation that fits the data
Which of the following best describes a weak positive correlation?
The variables are unrelated
As one variable increases, the other tends to increase slightly
As one variable increases, the other decreases
The variables are exactly proportional
What might an exponential trend line suggest in a scatter plot?
The relationship grows at a constant rate
The relationship is random
The rate of increase or decrease gets faster over time
The variables are unrelated
Which of the following is NOT a typical step in investigating a hypothesis using data?
Remove data to fit a line that has a strong correlation
Collect relevant data
Make a scatter plot to compare variables
Draw a conclusion based on your analysis
If you find no correlation between two variables, what is the most reasonable conclusion?
The hypothesis is proven true
The hypothesis needs to be rewritten
The two variables may not influence each other directly
There was an error in the graph
Why are outliers important to analyze in data projects?
They always fit the data trendline and strengthen it
They are wrong data points so they should be removed
They often reveal interesting exceptions that help explain the data
They don't affect your analysis
Which of the following would be a meaningful “so what?” conclusion to a project that compared number of bike lanes vs. number of cars during rush hour?
The data is really interesting for marketing reasons.
The hypothesis must be correct and bike lanes need to be created.
This information could help cities make better transportation policy decisions.
The data will help us make better educational programs about texting while driving.
What is one possible next step after completing a project with an unexpected result?
Find more data until hypothesis is proven.
Repeat the same process with a total new set of data.
Identify other variables that might influence the outcome.
Assume the hypothesis is wrong.
What kind of chart is most useful to compare values across different categories (like states)?
Pie chart
Line graph
Bar chart
Scatter plot
Which tool did you use to calculate R² and fit a trend line to your data?
Google Docs
Google Maps
Google Sheets
Google Slides
Which of the following is a sign of a strong linear correlation?
R² is close to 0
R² is close to 1
R² is exactly 0.5
The data forms a circle
Why is it important to create multiple types of charts (bar, pie, geo, scatter) in a project?
To obtain at least 3 R^2 values that confirm a strong correlation.
To make sure a bar chart is included.
To show different aspects of the data and compare perspectives.
To make sure we understand the proportional split.
Which of the following best describes the role of a hypothesis in your project?
An uninformed guess that will show a correlation.
A fact that needs to be proven
A prediction that can be tested using data
A conclusion you reach at the end.
Luisa's project presented a hypothesis that more universities in a state would lead to more licensed engineers.
What kind of relationship did this project find?
No correlation
Weak linear correlation
Strong exponential correlation
Negative correlation
Which state stood out as having the most # of licensed engineers in the university vs. licensed engineers data (Luisa's project)?
California
Michigan
Texas
New York
Ohio stood out as an outlier in the data for Layla's project examining university vs. architectural engineering data.
What factor made it unusual compared to its population?
It had very few universities for its size
It had a large population but few engineers
It had a surprisingly high number of universities
It had a low number of architectural firms
What best explains why the number of architectural engineers didn't correlate strongly with the number of universities?
Architectural engineering is too small a field to analyze.
Universities no longer offer architectural programs
Other factors, like architectural firms or state industry demand, may influence it more
Data on architectural engineers was limited.
Agustina's project tested whether states with more oil would invest less in renewable energy.
What did the data actually show?
A strong negative correlation
No pattern at all
A strong positive correlation
A weak positive correlation
Why might a state like Texas lead in both oil production and renewable energy development?
It imports renewable energy
Oil makes it rich enough to afford renewable energy.
It is transforming its economy to prepare for a renewable future
It has no regulations on energy
Kylin looked at EV ownership per state and found what kind of correlation with income?
Strong negative linear
No correlation
Strong positive exponential
Weak positive linear
What is a meaningful conclusion from the EV project?
People with high income prefer diesel cars, not EVs
Electric vehicle adoption may increase with policies that raise income, such as grants and rebates.
EVs should only be sold to a high income sector of people with good credit.
Higher income leads to fewer emissions
What was the hypothesis tested about sunlight and solar energy production across states, as per Jayden's investigation?
More sunlight in a state equals less solar energy
Less sunlight in a state equals more solar energy
More sunlight in the state equals more solar energy
Solar energy and amount of sunlight in the state are unrelated. Other factors are at play.
Why did California stand out in the sunlight vs. solar energy project?
It had a moderate amount of sunlight but a lot of solar energy production due to strong policy and infrastructure
It had the most sunlight and most energy.
It banned fossil fuels entirely
It had very low solar production despite strong renewable energy policies.
Alex's project tested whether higher pollution led to higher asthma rates. What did the data show?
A strong positive correlation
No correlation
Strong negative correlation
Only correlation in states with large cities.
What is a key takeaway from the asthma vs. pollution project?
Pollution has no effects on respiratory conditions.
Asthma is affected by multiple factors beyond pollution
Particulate pollution is only a concern in cities
States with clean air have less asthma so we should invest in air filtration.
Evan tested whether busier airports had higher cancellation rates. What did the results show?
Strong negative correlation
Weak positive correlation
No correlation
Strong exponential correlation
What regional pattern stood out in the airport cancellation geo chart?
Western airports had higher cancellations due to mountain ranges.
Southern airports were the worst due to humidity.
East Coast airports had more cancellations, possibly due to rain and visibility concerns.
Midwest airports canceled fewer flights due to cold weather infrastructure.
What is the difference between a correlation between two sets of data and an outlier in the data? How can use either to help us prove or disprove a hypothesis and explain the data? Give an example of each based on your project or another project that was presented.
In the conclusions section, we draw recommendations based on the analysis. For example, if bike lanes are proven to reduce rush hour traffic, cities with traffic congestion could invest in building bike lanes, give grants for people to buy bikes, etc.
Provide one recommendation based on your project and one based on another project given the correlations shown by the data.
