Engineering Science Data Analysis Quiz

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