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Team 2
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Teams
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banana
Go to last place!
Oops!
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fairy
Take points!
5
10
15
20
25
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gift
Win 25 points!
Okay!
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lifesaver
Give 15 points!
Oops!
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seesaw
Swap points!
Oops!
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star
Double points!
Okay!
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shark
Other team loses 20 points!
Okay!
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baam
Lose 15 points!
Oops!
15
×
Why is the interquartile range often preferred over the range?
It is not affected by extreme values or outliers.
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15
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Why is it inappropriate to use a regression line to make predictions outside the range of the data?
It assumes the same trend continues, which may not be valid (extrapolation risk).
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15
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Give one assumption necessary for modelling data with a normal distribution.
The data must be symmetrically distributed around the mean.
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banana
Go to last place!
Oops!
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seesaw
Swap points!
Okay!
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fairy
Take points!
5
10
15
20
25
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banana
Go to last place!
Oops!
15
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Name two conditions for using a binomial distribution model.
Fixed number of trials and constant probability of success; outcomes are independent.
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15
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Why is the area under the normal curve equal to 1?
Because it represents the total probability of all outcomes.
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15
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What does a p-value represent in hypothesis testing?
he probability of obtaining the observed result (or more extreme) assuming the null hypothesis is true.
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thief
Give points!
5
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25
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rocket
Go to first place!
Okay!
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gift
Win 15 points!
Okay!
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baam
Lose 25 points!
Oops!
15
×
What is the difference between qualitative and quantitative data?
Qualitative data is descriptive (e.g. colours), while quantitative data is numerical (e.g. heights).
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15
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What does it mean to “reject the null hypothesis”?
here is sufficient evidence to support the alternative hypothesis.
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15
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Why does a strong correlation not imply causation?
There may be lurking variables or the relationship could be coincidental.
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15
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Why might a random sample be preferred over a convenience sample in statistical analysis?
A random sample reduces bias and is more representative of the population.
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15
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What is meant by the critical region in hypothesis testing?
The set of values of the test statistic that would lead to rejecting the null hypothesis.
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15
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Why is it important to evaluate whether a model’s assumptions hold in real-life data?
Because if the assumptions are invalid, the model may give misleading results.
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