MISY 262 FINAL EXAM QUESTIONS WITH VERIFIED SOLUTIONS LATEST UPDATE 2026

MISY 262 FINAL EXAM QUESTIONS WITH VERIFIED SOLUTIONS LATEST UPDATE 2026 Business Analytics - Answers The process of gathering, inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information to suggest conclusions and support decision making data cleaning - Answers The process of detecting and correcting coding errors in a machine-readable dataset EDA Process - Answers the process of reorganizing the data and make it easy to read and understand. (exploratory data) Descriptive Statistics - Answers uses data to provide descriptions of the population either through charts, tables, or numerical calculations; describes the KNOWN data; draws insights from past data -- more meaningful measures of centre - Answers Mean Median Mode Geometric Mean Midrange Trimmed Mean measures of spread - Answers Range Variance Standard Deviation Coefficient of Variation Mean Absolute Deviation Interquartile Range- IQR Skewness vs Kurtosis - Answers skewness: extent (typically absence) of symmetry in a distribution kurtosis: the STEEPNESS of a distribution in its center -platykurtic: relatively flat -leptokurtic: relatively peaked -mesokurtic: somewhere in the middle Examples of Descriptive Statistics - Answers mean, median, mode, range, standard deviation Inferential Statistics - Answers making inferences by comparing and predicting future outcomes (in the form of probability scores) -- make conclusions beyond the available data Data Visualization - Answers The representation of data in the form of charts, dashboards, etc Descriptive BA - Answers "What happened?"; understanding underlying trends and causes Predictive BA - Answers "What will happen and when?"; accurate projections of future events and outcomes by looking at past data Prescriptive BA - Answers "What should I do?"; makes decisions to achieve the best performance possible; uses descriptive and predictive analytics to create alternatives -- find the best one EX: netflix creates personalized movie recommendations Script - Answers Top Left Corner; where the code is written Console - Answers bottom left corner; shows the output of the code that has been run Environment - Answers top right corner; displays the set of external elements that have been added EX: x=3 Graphical Output - Answers bottom right corner; displays the graphs created during exploratory data analysis - OR = - Answers symbols to assign values to R Numeric - Answers real numbers Integer - Answers whole numbers Logical - Answers true/false Vectors - Answers OBJECTS which represent one-dimensional arrays that can hold NUMERIC data, character data, or logical data c() - Answers what is the function to create a vector class() - Answers what is the function for the class of an object vector() - Answers what is the function for a vector of a numeric type er() - Answers what is the function for an integer variable in R "as." command - Answers what is the command to convert the class of a vector Data Frame - Answers important object types used to store tabular data () - Answers what is the function for a data frame Factors - Answers uses the range to categorize values as a vector of integers; each integer type has a label -- they are assigned these values Data - Answers raw facts, figures, measurements, and amounts that we gather for analysis or reference + descriptive information + collects and stores data typically through observations Categorical Data - Answers can be put into groups/categories using names/labels; can be assigned to only one category based on its qualities (each is mutually exclusive) EX: hair color, smoking status, rank, major Nominal Data - Answers type of categorical data in which objects fall into unordered categories (order is NOT important) Ordinal Data - Answers type of categorical data in which order is important EX: year in school, level of illness Binary Data - Answers only two categories exist (there is no in between) EX: attendance (present OR absent) Measurement - Answers numerical data "measured" based on some quantitative trait Discrete Measurement - Answers only certain values are possible; no gaps between the values EX: SAT scores Continuous Measurement - Answers any value within an interval EX: height, age, GPA Dataset - Answers data obtained through observations, measurements, study, analysis; it is an ordered collection of data usually presented in a tabular pattern Attributes/Variables (in a dataset_ - Answers "the category names"; last name, age, rank Observations/Data Records (in a dataset) - Answers "the actual input for the category" EX: people, age Dataset Columns - Answers each has its own characteristic, name, unit, and format Dataset Rows - Answers "observation or record"; each has its own (and one only) observation ex: topic, subject, person Numerical Data - Answers age, gpa Textual Data - Answers Last and first name data(datasetname) - Answers what is the function to load a built-in dataset data(iris) - Answers what is the function to load the iris dataset head() - Answers returns the first few rows of the dataset tail() - Answers returns the last few rows of a dataset str() - Answers returns the structure of the dataset () - Answers identifies any missing values in the dataset summary() - Answers returns the basic structure of the dataset Exploratory Data Analysis - Answers The critical process of performing initial investigations on data to... with the help of graphical representations and summary statistics EDA Step 1 - Answers Load the Data (import, load, read) EDA Step 2 - Answers Data Visualization; data is represented in a chart, graph, etc EDA Step 3 - Answers Data Imputation; deal with missing data EDA Step 4 - Answers Statistical Analysis; provide descriptions of the data characteristics through some numerical calculations Comma - Separated - Values - Answers what does CSV stand for head(dataset_name, 10) - Answers returns the first 10 rows of a dataset tail(dataset_name, 15) - Answers returns the last 15 rows of a dataset View() - Answers returns the entire dataset ?() - Answers returns the specification of the variables in the dataset ncol() - Answers returns the number of columns in the dataset nrow() - Answers returns the number of rows in the dataset Redir package - Answers used for importing external datasets ggplot2 package - Answers used for data visualization fBasics package - Answers used for statistical analysis mice package - Answers used for data imputation ges() - Answers function used to install a package library() - Answers function used to load a package getwd() - Answers used to check the path of the data file you are using spec() - Answers used to retrieve the full column specification for the dataset glimpse() - Answers functions that makes it possible to see every column in a dataset and tries to show as much as possible (is in the dplyr package) scatter plot - Answers Shows the relationship between two numerical variables plotted simultaneously along both the horizontal and vertical axis histogram - Answers a graphical representation of a frequency distribution; can make it easier to identify outliers Line Chart - Answers best used to show how a value has changed over time Bar Chart - Answers common chart type used for graphing categorical data stacked bar chart - Answers shows two categorical variables; one along the whole bar and the other is stacks within each bar box plot - Answers a dense display that aims to provide lots of context for any given dataset Faceting - Answers small multiples or conditioning to break a plot into subsets geom_point() - Answers function used to create a scatterplot geom_point(shape=1, color="red") - Answers where does shape and color go in a scatterplot function labs() - Answers function used to add titles theme_classic() - Answers function used to change the theme to a classic view cord_flip() - Answers function used to flip the x and y axis ggsave() - Answers function used to save a plot geom-point() - Answers function used to differentiate each category by the shape of the point geom-point(color=categorical_variable_name) - Answers function used to differntiate each category by the color of the point geom-point(color=categorical_variable_name, shape=categorical_variable_nme) - Answers function used to differentiate each category by the color and shape facet_wrap() - Answers function used when there is multiple plots in one geom_point(aes(color=categorical_variable_name, shae=categorical_variable_name)) - Answers function used to visualize each category by the color and shape of the point ggsave("") - Answers function used to save the created plot (, aes(x=, y= ) - Answers function used to define the x and y axis group= - Answers function used to define geometry geom_point() - Answers function used to add data points to a graph linetype() - Answers function used to edit the geom_line() linetype geom_boxplot() - Answers function used to create a boxplot factor() - Answers function used to convert the numerical month into a factor categorical variable stat_summary() - Answers function used to add the mean inside the box geom_boxplot(width=[#]) - Answers function used to change the width of a boxplot data("weather") - Answers function used to load a dataset EX: weather glimpse(weather) - Answers function used to glimpse the weather dataset ggplot(data=weather, mapping=aes(x=temp)) + geom_histogram() - Answers function used to count the observations in each bin that gets computed automatically geom_histogram(color="white") - Answers function used to add a white vertical border defining the bins geom_histogram(fill="steelblue") - Answers function used to set the bin colors geom_histogram(bins=40) - Answers function used to override the number of bins geom_historam(binwidth=10) - Answers function used to specify the width of the bins