Lesson L2
L2 Scatter diagrams, regression lines and correlation Quiz: AQA Maths, Unit 11
20 questions
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Lesson L2, Scatter diagrams, regression lines and correlation: 20 multiple choice questions for the AQA Maths (7357), Unit 11: Statistics, written with Revision Ninja.
Host it live on the board and students join with a game code on their own devices, or revise alone with Free Play. The answers are revealed in the game.
The 20 questions
-
What does the statement correlation does not imply causation mean?
- A strong correlation proves a causal link
- Correlation is always zero for real data
- Correlation applies only to categorical data
- A strong correlation can arise from a third factor or coincidence, so it does not prove one variable causes the other
-
What does positive correlation mean?
- As one variable increases, the other tends to increase
- The variables are unrelated
- As one variable increases, the other decreases
- The two variables are always equal
-
For strong negative correlation, the points lie close to which kind of line?
- A horizontal line
- A circle
- A straight line with positive gradient
- A straight line with negative gradient
-
Within what range must the product moment correlation coefficient r lie?
- -1 to 1 inclusive
- 0 to infinity
- 0 to 1 inclusive
- -10 to 10 inclusive
-
A scatter diagram shows two distinct sections of points. What does this suggest?
- That a regression line is not needed
- An error in the data
- The data may come from two or more sub-populations
- That the variables are independent
-
A correlation coefficient of r = -0.9 is found for two variables. Which is the best interpretation?
- A strong negative linear correlation
- No correlation
- A perfect positive correlation
- A weak positive correlation
-
A regression line of exam score on hours of revision has gradient 3.5. What is the correct interpretation?
- On average, each extra hour of revision is associated with 3.5 more marks
- Each hour of revision causes exactly 3.5 marks
- There is no relationship between revision and score
- A student with no revision scores 3.5 marks
-
A point lies far away from the rest of a scatter diagram and pulls the line of best fit towards it. What is it called?
- A correlation coefficient
- A residual of zero
- An outlier
- A cluster
-
A correlation coefficient of 0.05 is calculated. Which is the best interpretation?
- The variables are perfectly negatively correlated
- The variables are strongly positively correlated
- There is almost no linear correlation between the variables
- Every point lies on a straight line
-
A regression line is used to predict a value for an x-value far outside the range of the data. What is this called and how reliable is it?
- Extrapolation, which may be unreliable
- Interpolation, which is always reliable
- A correlation of zero
- A sampling error that cannot be fixed
-
Ice cream sales and drowning incidents have a correlation of 0.8. What is the best conclusion?
- The variables are strongly positively correlated, but a common cause such as hot weather may explain both, so causation cannot be concluded
- Drowning causes ice cream sales to rise
- Ice cream causes drowning
- The correlation is negative
-
All points lie exactly on a straight line with negative gradient. What is the value of r?
- -1
- 0
- -0.5
- 1
-
A scatter diagram has two groups of points, each showing little correlation within itself, but a strong overall pattern. What should be done?
- Report only the overall correlation
- Remove the second group as errors
- Analyse the groups separately, since the overall correlation may be misleading
- Ignore the scatter diagram
-
A correlation coefficient of 0.6 is found between two variables. Which statement is best?
- There is no association between the variables
- The variables have a moderate positive linear association
- One variable causes 60% of the other
- The variables have a perfect positive linear relationship
-
Shoe size and reading score are positively correlated among children. What is the best explanation?
- Bigger feet cause better reading
- Age is a lurking variable that increases both
- Reading makes feet grow
- The correlation must be zero
-
A scatter diagram shows points forming a clear U-shape. What is likely to be true of Pearson's r?
- r equals the gradient of the points
- r is close to zero, despite a clear relationship, because the relationship is non-linear
- r is exactly -1
- r is close to 1 because the points rise
-
A model gives value y = 50 - 2x, with x the age of a car in years and y the value in thousands of pounds. What does the intercept 50 represent?
- The depreciation per year
- The average age of the cars
- The age at which the value is zero
- The predicted value of a new car, in thousands of pounds, when age is zero
-
Using y = 50 - 2x for car value y in thousands of pounds and age x in years, what does the gradient -2 mean?
- The value falls by 50 thousand pounds each year
- The age increases by 2 years each time
- The value rises by 2 thousand pounds each year
- The value falls by 2 thousand pounds per extra year on average
-
If every x-value in a dataset is multiplied by 2, what happens to the correlation coefficient r?
- It doubles
- It stays the same
- It halves
- It becomes negative
-
A correlation coefficient is calculated from only 5 data points. What caution applies?
- Small samples can give large apparent correlations by chance, so the result is unreliable
- Five points always give r equal to zero
- Larger samples are never needed
- Five points always give a perfect correlation
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