Wpc 300 Final Exam Questions With Correct Answers Latest Update 2026

WPC 300 FINAL EXAM QUESTIONS WITH CORRECT ANSWERS LATEST UPDATE 2026 Identifying a problem - Answers Who are our customers? How frequently do they shop at our location? What products do they buy from us? What is their share of wallet for our brand? Who is our most loyal customer? Who is our most valuable customer? How should we treat our most valuable customer? How do we learn more about our customers? What strategy should we implement to bring customers to our door more frequently? What should be the promotion strategy to get more attention from our customers? Principles of Problem Framing - Answers Tell an interesting and complete story Find an appropriate solution framework Routinize the procedure Analytics - Answers -is the process of developing actionable decisions or recommendations for actions based on insights generated from historical data -represents the combination of computer technology, management science techniques, and statistics to solve real problems. Primary Data - Answers Survey, Interviews (marketing firm's telephone interviews), Used a lot in marketing research Secondary Data - Answers Firm's proprietary database, Internet data (crawlers) [scarpy, beautifulsoup], Stock/capital market data [compustat, CRSP] Accounting disclosure data [ from 10K, 10Q] Simulated Data - Answers Data based on assumption and simulation Used a lot in scheduling, routing and queuing Data Extraction - Answers Extract data from primary/secondary source Data Transformation - Answers Transform / clean data into proper format or structure for the purpose of querying & analysis Data Load - Answers Load data into final target database, more specifically an operational data store, data mart or data warehouse ETL - Answers is time consuming and sometimes done in parallel. Several computation tools are used to develop robust ETL systems. Descriptive Analytics - Answers This is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis. Questions: (1) What happened? (2) What is happening? Methods: (1) Standard reporting (2) Dashboards (3) Visual analytics Outcome: Well defined business problems and opportunities Diagnostic/Explanatory Analytics - Answers this is about looking into the past and determining why a certain thing happened. This type of analytics usually revolves around working on a dashboard. Question: (1) Why did it happen? (2) How did it happen? Methods - Answers Inferential Statistics, Visual analytics Outcome - Answers Discover/Understand causal relationships of an outcome Predictive Analytics - Answers Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future. Question: (1) What will happen next? (2) Why will it happen next? Methods: (1) Data mining (2) Text mining (3) Forecasting Outcome: Accurate projections of future outcomes and events Prescriptive Analytics - Answers Prescriptive analytics answers the question of what to do by providing information on optimal decisions based on the predicted future scenarios. The key to prescriptive analytics is being able to use big data, contextual data and lots of computing power to produce answers in real time. Question: (1) What should be done about it? (2) Why should you do it? Methods: (1) Optimization (2) Simulation (3) Expert systems Outcome: Best possible business decision and outcome Data visualization - Answers is the graphical representation of information and data -charts, graphs, and maps provide access to see and understand trends, outliers, and patterns in data Basic Principles of Visualization- - Answers (Edward Tufte) The chart should tell a story The chart should have graphical integrity The chart should minimize graphical complexity Data Visualization Principle - Answers -The depictions should enable meaningful comparison -The chart should yield insight beyond the text -The chart should have graphical integrity. It should not "lie" or mislead readers. Tufte's "Lie Factor": Story Telling - Answers Stories have been told for millennia • To entertain • The moral of the story Analytics & Story Telling - Answers science & data μ, σ 2 , χ 2 , Σ, etc, etc... For content: - Answers create and weave threads to form the fabric of the story • Compelling narrative Beginning - Answers Context: set the scene Middle - Answers Execute analysis & report results End - Answers Review what was intended, interpret results - Conclusions naturally flow Problem - Answers More negative, Backwards looking, Needs to be fixed, Bad immediate, Engage solution mode Opportunity - Answers More positive, Forwards looking, Could be worth doing, Good, possibility, Engage evaluation mode Visualization - Answers is primarily descriptive Story telling - Answers weaves the narrative around visuals to bring out the explanation ("why") Qualitative - Answers -Is descriptive and conceptual and cannot be measured Quantitative - Answers -Can be counted, measured, and expressed using numbers Numerical - Answers -Interval, ratio Categorical - Answers -Nominal, Ordinal Nominal - Answers -In nominal measurement the numerical values just "name" the attribute uniquely Categorical:Ordinal - Answers -Categorical data can be on an ordinal scale. Numbers are used to indicate rank or order Numerical: Interval - Answers Also known as "Scale", "Quantitative" or "Parametric" data -Distances between consecutive integers are equal Numerical: Ratio - Answers Ratio is very similar to the interval scale, with the difference that it has a true zero point. This scale is commonly used for values that are measured in numbers, such as length, height, weight, or monetary values like cost and revenue. Statistics - Answers is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data to assist in making effective decisions. Three principles of describing data - Answers -Center, Spread and Shape Inferential statistics (study sample data) - Answers Estimate uncertainty (using probability) some member of the data to infer about population data Population: - Answers -the whole -a collection of persons, objects, or items under study Census: - Answers -gathering data from the entire population Sample: - Answers - a portion of the whole/population a subset of the population; must be large enough to represent the whole Measures of central tendency - Answers -yield information about the center, or middle part, of a group of numbers. -Mean Median Mode Percentiles Quartiles Mean - Answers Is the average of a group of numbers Median - Answers Middle value in an ordered array of numbers Mode: - Answers The most frequently occurring value in a data set Measures of variability - Answers describe the spread or the dispersion of a set of data. Common Measures of Variability: - Answers Range Interquartile Range Mean Absolute Deviation Variance Standard Deviation Range - Answers The difference between the largest and the smallest values in a set of data Mean Absolute Deviation - Answers Average of the absolute deviations from the mean: Measures of Shape - Answers describe the skewness of a set of data. Common Measures of skewness is called Kurtosis - Answers a measure of whether the data are peaked or flat relative to a normal distribution. Covariance - Answers is a measure of the linear association between two variables, X and Y. Like the variance, different formulas are used for populations and samples. Correlation - Answers is a measure of the linear relationship between two variables, X and Y, which does not depend on the units of measurement. -The correlation coefficient is scaled between -1 and 1 Inferential statistics - Answers use a random sample of data taken from a population to describe and make inferences about the population How reliable is conclusion - Answers You can infer/conclude about the population with a certain confidence level. (You will be able to find out if the claim is really correct or not with certain confidence level - 95%) Random sampling - Answers is a part of the sampling technique in which each sample has an equal probability of being chosen -meant to be an unbiased representation of the total population. Typical sampling mistake - Answers -Unrepresentative sample -Biased respondents -Low response rate (non-response bias) or lower sample size -Biased questions Online Survey - Answers Polls are completed only by visitors to the site -Those with an interest in the website's mission are the only ones who will participate Non-response - Answers -Some individuals are less likely to respond to a survey, e.g., want their opinion about smoking weed Social Desirability - Answers -Want to study what factors lead to academic dishonesty -Who will be participating? Observation: - Answers Expected mean of the sample means is the true population mean -The standard deviation of sample mean distribution (SE) decreases as the sample size "n" increases -Increasing sample size "n" reduces the average sampling error confidence interval - Answers -is a range of values that is likely to contain the population mean confidence level - Answers - is the proportion of samples that will yield a confidence interval that actually contains the population means. Z-statistics - Answers is good for larger sample (n30) with underlying distribution of the population may or may not be normal T-statistics - Answers - is used when population SD is unknown Testing the hypothesis - Answers -When p-value is lower than α, then reject the null hypothesis Confidence Level - Answers If CL = 98%, the confidence interval gets wider than when CL = 95%. -You do so to be more confident in your results. Sample Size - Answers -If you increase the sample size, the CI becomes narrower -You get more precise measurement Standard Deviation - Answers -If you increase your SD, the confidence interval gets wider -Your have more uncertainty and less precision in your measurement Anova - Answers Useful when you are comparing more than two sample means -Observations are independent Equal variances for each population -This process investigates variances to establish if the means are significantly different Bonferroni - Answers extremely general and simple, but often not powerful Tukey's - Answers the best for all-possible pairwise comparisons when sample sizes are unequal or confidence intervals are needed; very good even with equal samples sizes without confidence intervals -Stepdown - Answers -the most powerful for all possible pairwise comparisons when sample sizes are