MISY 262 Final Exam Questions with Correct Answers Latest Update 2026/2027

MISY 262 Final Exam Questions with Correct Answers Latest Update 2026/2027 Descriptive statistics - Answers Popular for its ability to generate accessible insights from otherwise uninterpreted data, does not attempt to make predictions about the future, draws insights from past data mean - Answers sum of data values divided by the number of data items median - Answers 50th percentile or midpoint of a sample dataset Range - Answers Difference between the largest and smallest observations Variance - Answers sum of squared deviations from the mean divided by the population size Downside of Variance - Answers differences are squared so the numbers that represent deviation/variance become somewhat meaningless Standard deviation - Answers the square root of the variance, a single number that helps us understand how individual values, in a dataset vary from the mean Normal Distribution - Answers distribution that is perfectly symmetric on both sides of the mean Skewness - Answers measure of asymmetry in a distribution of the data that portrays a unimodal structure Kurtosis - Answers The relative length of the tails and the degree of concentration in the center, measures the degree to which a distribution is more or less peaked than a normal distribution Mesokurtic - Answers Distribution with medium kurtosis (medium tails) Platykurtic - Answers Distributions with low kurtosis (thin tails) Leptokurtic - Answers Distributions with high kurtosis (fat tails) (more outliers) Lower Quartile (Q1) - Answers the median of the lower half of the data Upper Quartile (Q3) - Answers the median of the upper half of the data Second Quartile (Q2) - Answers 50th percentile (median) Interquartile Range (IQR) - Answers Q3-Q1 Outlier - Answers affects the mean, range, variance and SD Box Plot - Answers graphical representation of the concentration of the data; shows how far the extreme values are from most of the Data Converting numerical variables to factors - Answers dataset$variable = r( dataset$variable) options(scipen = 999) - Answers turns off scientific notation Missing Completely at Random (MCAR) - Answers probability of data being missing is the same for all observations, missingness is independent of both observed and unobserved data, doesn't relate to the values of any variables Missing at Random (MAR) - Answers likely is related to other observed variables, but not the value of the missing data itself Missing Not At Random (MNAR) - Answers related to the value of the missing data itself, can bias analysis if not properly handled Deletion - Answers remove features (columns) with missing values, remove observations (rows) with missing values Imputation - Answers fill in the missing data; mean/median/mode, regression, random forest () - Answers Returns TRUE if the value is missing, otherwise FALSE colSums(()) - Answers returns the number of missing values in each column of a dataset which(()) - Answers returns the indicies of the missing values