Analyzing Outliers and Creating Scatterplots in Data

A teacher collected data on the number of hours students studied and their scores on a math test. After plotting the data on a scatterplot, she noticed one student who studied 30 hours but scored only 50. What can you conclude about this data point? Is it an outlier?

• No, it is a typical data point.

• Yes, it is the highest score.

• No, it is an average score.

• Yes, it is an outlier.

A local gym recorded the number of members and their average monthly spending. Create a scatterplot using the following data: (10, 100), (20, 200), (30, 300), (40, 400), (50, 500). What trend do you observe?

• The trend observed is a constant value relationship.

• The trend observed is a random scatter with no correlation.

• The trend observed is a positive linear relationship.

• The trend observed is a negative linear relationship.

In a survey, the heights (in inches) and weights (in pounds) of 10 individuals were recorded. After creating a scatterplot, one individual was significantly heavier than the others for their height. How would you identify this point as an outlier?

• Identify the point with the highest weight regardless of height.

• Use the average weight of all individuals to determine outliers.

• Plot the weights on a separate graph to find the outlier.

• Calculate the z-score for weights based on height; a z-score > 2 indicates an outlier.

A researcher is studying the relationship between the amount of rainfall and the number of flowers blooming in a park. If the data points are (1, 5), (2, 15), (3, 20), and (10, 50), create a scatterplot and describe the relationship. Are there any outliers?

• The relationship is negative with no outliers.

• The relationship is neutral with an outlier at (2, 15).

• The relationship is positive with an outlier at (10, 50).

• The relationship is positive with an outlier at (3, 20).

A car dealership wants to analyze the relationship between the age of a car and its resale value. They collected the following data: (1, 20000), (2, 15000), (3, 12000), (10, 5000). Create a scatterplot and identify any outliers. What does this tell you about older cars?

• Older cars generally have a much lower resale value, indicating significant depreciation.

• Older cars have a higher resale value due to demand.

• The age of a car has no impact on its resale value.

• Older cars are often sold for more than newer models.

During a science experiment, students measured the temperature of water at different times. The data collected was: (0, 10), (1, 15), (2, 20), (3, 25), (4, 100). Create a scatterplot and analyze the outlier. What might have caused this unusual temperature?

• The temperature of 100 degrees was due to a chemical reaction.

• The water was frozen at 0 degrees, causing a spike in temperature.

• The thermometer was broken and gave an inaccurate reading.

• The unusual temperature of 100 degrees may have been caused by an external heat source or a measurement error.

A city planner is examining the relationship between the number of parks in a neighborhood and the average property value. The data points are (1, 300000), (2, 350000), (3, 400000), (5, 600000). Create a scatterplot and discuss any outliers you find.

• The outlier is the point (5, 600000) as it deviates from the trend of the other data points.

• The outlier is the point (1, 300000) as it is significantly lower than the others.

• The outlier is the point (2, 350000) because it is the only point with a value below 400000.

• There are no outliers in the data set as all points follow a consistent trend.

A student recorded the number of books read and the time spent reading each week. The data points are (1, 2), (2, 4), (3, 6), (4, 8), (10, 50). Create a scatterplot and identify any outliers. What does this suggest about the reading habits of that student?

• The outlier is (3, 6), indicating consistent reading habits.

• The outlier is (2, 4), suggesting the student reads too few books.

• The outlier is (4, 8), showing the student reads at a steady pace.

• The outlier is (10, 50), suggesting variability in the student's reading habits.

In a study of the relationship between exercise hours and heart rate, the data collected was: (1, 80), (2, 75), (3, 70), (4, 90), (10, 150). Create a scatterplot and analyze the outlier. What could explain this high heart rate?

• The high heart rate is due to dehydration from lack of water.

• The high heart rate of 150 at 10 hours of exercise could be due to overexertion or a medical condition.

• The heart rate is normal for someone who is very fit.

• The high heart rate indicates a low level of fitness.

A farmer is tracking the yield of crops based on the amount of fertilizer used. The data points are (0, 0), (1, 10), (2, 20), (3, 30), (5, 100). Create a scatterplot and identify any outliers. What does this indicate about the use of fertilizer?

• The point (3, 30) suggests that yield decreases with more fertilizer.

• The point (5, 100) is an outlier, indicating that excessive fertilizer may not yield proportional increases in crop yield.

• The point (1, 10) is an outlier, indicating low fertilizer use is ineffective.

• The point (2, 20) shows that fertilizer has no impact on yield.