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BSNS112 - Final Exam prep Questions and
Answers (100% Correct Answers) Already
Graded A+
What does quantitative data use? Ans: means
What is discrete data? Ans: Quantitative. Measured in specific values
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What is continuous data? Ans: Quantitative. Measure in infinite values
What does qualitative data use? Ans: Proportions
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What is ordinal data? Ans: Qualitative. Conveys a ranking
What is nominal data? Ans: Qualitative. Uses labels (no ranking)
confidence interval for one proportion Ans: q-hat = (1-p-hat)
sample size for estimating mean Ans: E = B (on formula sheet)
standard normal transformation formula Ans: calculating when Z is
unknown (or x or sd but most likely z)
right skewed distribution Ans: mean > median, also known as a positive
skew
left skewed distribution Ans: mean < median, also known as a negative
skew
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90% confidence interval Ans: 1.645
95% confidence interval Ans: 1.96
99% confidence interval Ans: 2.575
90% confidence interval of the proportion of all market goers who are
students would be: Ans: Narrower than the 95% confidence interval
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When do you reject the null hypothesis? Ans: When the p value is less
than 0.05 (p-value low, reject that SHO!!!) 5% level of significance
What does the t-value represent? Ans: the sample means is (x) amount
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standard errors more/less (depending if it is positive or minus) than the
hypothesised mean
What is a type one error? Ans: Rejecting the null hypothesis when it is
true
What is a type two error? Ans: failing to reject a false null hypothesis
p-value Ans: -is calculated based on the assumption that the null
hypothesis is true
-the p-value is sample specific, meaning if you collected another
random sample of the same size from the same population, the p-value
would likely be different.
Stratifed random sample Ans: For the same sample size, parameter
estimates are usually more accurate than for simple random sampling
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Categorical variables (qualitative variables) Ans: those that divide
subjects into groups, but do not allow any sort of mathematical
operations to be performed on the data
Numerical Variables (Quantitative) Ans: numbers
Ordinal Ans: rank, order
nominal variables Ans: variables measured in monetary units....currency
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input variable Ans: xi
- explanatory variable (independent) variables these are also called
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features
output variable Ans: Yk
- the response (dependent) variables
issues with data Ans: - data you tend to play with (number you're given
in a sample)
- features and examples (more of one less of the other)
- large number of example of features
- very large number of examples
data quality Ans: - most often comes as a table - but this isn't the case
(in the real world)
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- data can have holes and look and it won't look clean (complex
format)
poor data quality Ans: -missing column variables
-missing values
- errors in the data entry
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- mixed numeric and test
- inconsistent values
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Transformations (scaling) Ans: - reduce the scale (range) of the data
- transform the mean, 0, and SD, 1
Log transform Ans: - some data is highly skewed and is better if you
change it to look more normally distributed
- square root helps
Feature selection reduction Ans: worst case, more explanatory variables
than examples, so a multiple linear regression cannot be constructed
- simple method
1. select the feature F that is most correlated with the response
1
BSNS112 - Final Exam prep Questions and
Answers (100% Correct Answers) Already
Graded A+
What does quantitative data use? Ans: means
What is discrete data? Ans: Quantitative. Measured in specific values
© 2026 Assignment Expert
What is continuous data? Ans: Quantitative. Measure in infinite values
What does qualitative data use? Ans: Proportions
Guru01 - Stuvia
What is ordinal data? Ans: Qualitative. Conveys a ranking
What is nominal data? Ans: Qualitative. Uses labels (no ranking)
confidence interval for one proportion Ans: q-hat = (1-p-hat)
sample size for estimating mean Ans: E = B (on formula sheet)
standard normal transformation formula Ans: calculating when Z is
unknown (or x or sd but most likely z)
right skewed distribution Ans: mean > median, also known as a positive
skew
left skewed distribution Ans: mean < median, also known as a negative
skew
,For Expert help and assignment handling,
2
90% confidence interval Ans: 1.645
95% confidence interval Ans: 1.96
99% confidence interval Ans: 2.575
90% confidence interval of the proportion of all market goers who are
students would be: Ans: Narrower than the 95% confidence interval
© 2026 Assignment Expert
When do you reject the null hypothesis? Ans: When the p value is less
than 0.05 (p-value low, reject that SHO!!!) 5% level of significance
What does the t-value represent? Ans: the sample means is (x) amount
Guru01 - Stuvia
standard errors more/less (depending if it is positive or minus) than the
hypothesised mean
What is a type one error? Ans: Rejecting the null hypothesis when it is
true
What is a type two error? Ans: failing to reject a false null hypothesis
p-value Ans: -is calculated based on the assumption that the null
hypothesis is true
-the p-value is sample specific, meaning if you collected another
random sample of the same size from the same population, the p-value
would likely be different.
Stratifed random sample Ans: For the same sample size, parameter
estimates are usually more accurate than for simple random sampling
,For Expert help and assignment handling,
3
Categorical variables (qualitative variables) Ans: those that divide
subjects into groups, but do not allow any sort of mathematical
operations to be performed on the data
Numerical Variables (Quantitative) Ans: numbers
Ordinal Ans: rank, order
nominal variables Ans: variables measured in monetary units....currency
© 2026 Assignment Expert
input variable Ans: xi
- explanatory variable (independent) variables these are also called
Guru01 - Stuvia
features
output variable Ans: Yk
- the response (dependent) variables
issues with data Ans: - data you tend to play with (number you're given
in a sample)
- features and examples (more of one less of the other)
- large number of example of features
- very large number of examples
data quality Ans: - most often comes as a table - but this isn't the case
(in the real world)
, For Expert help and assignment handling,
4
- data can have holes and look and it won't look clean (complex
format)
poor data quality Ans: -missing column variables
-missing values
- errors in the data entry
© 2026 Assignment Expert
- mixed numeric and test
- inconsistent values
Guru01 - Stuvia
Transformations (scaling) Ans: - reduce the scale (range) of the data
- transform the mean, 0, and SD, 1
Log transform Ans: - some data is highly skewed and is better if you
change it to look more normally distributed
- square root helps
Feature selection reduction Ans: worst case, more explanatory variables
than examples, so a multiple linear regression cannot be constructed
- simple method
1. select the feature F that is most correlated with the response
