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Introduction to Business Analytics and Descriptive Analytics ( Updated 2024 ) 1 100 Complete Questions & Answers (Solved) 100% Correct
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What is the primary focus of descriptive analytics in business? Descriptive analytics focuses on summarizing historical data to understand what has happened in the past. What are some common sources of data in business analytics?
Common sources include company databases, government reporting, market research, administrative records, and social media. How often is the volume of data doubling due to electronic data capture? The volume of data is doubling every two years. What is a significant challenge when linking data from different sources? The challenge is ensuring that data about one subject or person can be accurately linked across different sources. Why are loyalty programs and memberships valuable in data collection? They help in linking data about individuals from various sources, enhancing data accuracy. What are two methods of data collection mentioned in the notes? Surveys and observational studies. What type of data do surveys typically collect? Surveys collect qualitative data about preferences and opinions. What is a key challenge in designing survey questions? Questions must extract the right information without ambiguity and should be impartial. What is selection bias in survey sampling?
Nominal values represent separate units used to label variables that have no quantitative value. What are ordinal values in data? Ordinal values represent separate and ordered units where the order matters. What is the significance of representative samples in surveys? Representative samples ensure that survey results can be generalized to the wider population. What is the impact of biased statistical results? Biased statistical results can lead to incorrect conclusions and poor decision-making. What is the role of observational studies in data collection? Observational studies allow researchers to collect data on behaviors and characteristics in natural settings. Why is it important to relate information together in surveys? Relating information helps identify patterns and correlations, such as demographic support for political parties. What is the importance of wording in survey questions? Wording must avoid leading or ambiguous language to ensure accurate and honest responses. What are the three ways to report age?
Continuous (e.g., 50.75 years), Discrete (e.g., 50 years), Categorical (ordinal, e.g., 50 to 64 age group). What is the first step in the Analysis and Visualization components of the data analytics pipeline? Understand the quality of your data: missing values, veracity, outliers. What should you always start with when analyzing data? A Question of Interest. Why is it important to ensure you are measuring the correct thing in data analysis? To make sure the data is relevant for the question and to allow for meaningful descriptions. What is an example of scaling data in the context of Covid-19? Comparing Covid-19 cases per capita. How can the rate of return affect perceptions of an investment? A higher rate of return (e.g., 60%) may elicit sympathy for a loss compared to a lower rate (e.g., 0.60%). What are nominal prices? The dollar value of a series as actually measured at each point in time. What are real prices?
Counts, % of Total, % of Column, % of Row. What does interpreting % of row and % of column in a pivot table help with? Understanding the relationships between different categorical variables. How can you describe bivariate categorical data? Using a pivot table or contingency table to show the relationship between two categorical variables. What is the purpose of visualizing categorical data? To make the data clearer and easier to interpret. What is the role of descriptive statistics in evaluating an actress's performance? To summarize her movie roles using metrics like average movie takings or Rotten Tomato scores. What is the importance of scaling data in financial analysis? It allows for comparisons that account for different investment sizes and inflation effects. What is the relationship between qualifications and income in categorical data analysis? It can be explored using pivot tables to show counts or percentages across different qualifications and income brackets. What is a cumulative distribution table?
A table constructed from a frequency table that shows the cumulative frequency of data. How can numerical data be summarized? By categorizing it and constructing a frequency distribution. What does a histogram tell us about numerical data? It reveals the shape of the distribution of the data. What are the three types of skewness in data distribution? Positively skewed, negatively skewed, and symmetric. What is the median? The middle value in a data set where 50% of values are equal to or higher and 50% are equal to or lower. How is the median calculated in Excel? Using the formula =Median(A1:A21). What is the significance of skewness in relation to mean and median? Mean = Median for symmetric distribution; Mean > Median for positive skewness; Mean < Median for negative skewness. What indicates a highly skewed distribution? Skewness less than - 1 or greater than 1. What indicates a moderately skewed distribution? Skewness between - 1 and - 0.5 or between 0.5 and 1.
What does a symmetric distribution imply about mean and median? In a symmetric distribution, the mean and median are equal. What is an example of a skewed distribution scenario involving weights? In a bus scenario, if the mean and median weight of passengers are both 80kg, it may not reflect the presence of extreme weights like babies or sumo wrestlers. What is the purpose of descriptive analytics in numerical data? To summarize and describe the main features of a data set. What is the significance of the average in descriptive analytics? The average helps to understand the central tendency of the data. What does it mean when the median is significantly smaller than the mean? It indicates that the data is positively skewed. What are the implications of a distribution being highly skewed? It suggests that the data may not be normally distributed and could affect statistical analysis. What are the methods to describe the relationship between two numerical variables? Correlation coefficient, line graph (when time is involved), and scatterplot.
What type of graph is used to describe the relationship between a numerical and a categorical variable? Bar graph and pivot table with means for each category. What does the correlation coefficient measure? The relationship between two variables. How can you calculate the correlation coefficient in Excel? Using the =CORREL( ) function. What are the properties of the correlation coefficient (r)? r measures a linear relationship only, lies between - 1.0 and +1.0, sign indicates direction, and value indicates strength of relation. What does a positive correlation indicate? As X increases, Y also increases. What does a negative correlation indicate? As X increases, Y decreases. What does it mean if r = 0? It could mean no relationship at all between X and Y or that the relationship is non-linear. What is the symmetry property of the correlation coefficient? rxy = ryx, meaning the correlation between any variable and itself is 1. What is the significance of graphing data in descriptive analytics?
It involves presenting data in a way that tells a clear story while minimizing clutter. What is the effect of clutter in data visualization? Clutter can obscure the data and make it harder to understand. What is the 'slideshow effect' in research reports? When text elements narrate the visuals instead of integrating them effectively. What is the importance of rounding in data presentation? It conveys uncertainty and avoids undue precision. What does it mean to integrate text and graphs in reports? Visualizations should complement the text and contain enough information to stand alone. What is the recommendation for the number of decimal points in data presentation? Use fewer decimal points to convey uncertainty and avoid overwhelming precision. What are horizontal lines used for in tables? Horizontal lines are generally necessary only for separating column titles from data values or indicating that a calculation has taken place. How can shading be useful in large tables? Shading can be useful to break up rows or columns.
What is an outlier in data analysis? An outlier is an observation with an unusually low or high value that may be recorded incorrectly or may be real. How can outliers be identified? Outliers can be identified by looking for observations that are +/- three standard deviations from the mean. What should you consider when dealing with outliers? You need to decide whether to keep, remove, adjust, or treat them as missing data. What is the definition of Missing Completely at Random (MCAR)? MCAR means that the values of data that are missing do not depend on either the value of the missing data or the value of any other variable in the data. What is Missing at Random (MAR)? MAR means that the values of data that are missing do depend on the value of other variables in the data but not on the missing data itself. Provide an example of MAR. Survey respondents with income from investments may be less inclined to report income than those with income from wages. What does Missing Not at Random (MNAR) mean? MNAR means that the tendency for the value of a variable to be missing depends on the value of the variable itself.
What is imputation in the context of missing data? Imputation is the systematic replacement of missing values with values that seem reasonable. What is a potential downside of ignoring missing values? Ignoring missing values can lead to loss of information. What is the effect of using the mean or median for imputation? Using the mean or median for imputation can affect the standard deviation of the data. What is the first step in dealing with missing data? Deciding on a strategy requires understanding the reasons for the missing data. Why is it important to consider the amount of missing data? The amount of missing data can influence whether to ignore, impute, or remove variables. What are the key parts for a distributor's success today? Inventory, People, Technology, Success. What is the purpose of business analytics? To identify problems and provide tools to help decision makers solve them. Why is understanding current status important in business analytics?
If you don't know where you are and how you got there, you won't be able to determine where you want to go and how to get there. How is business analytics defined? The practice of iterative, methodical exploration of an organization's data with emphasis on statistical analysis. What analogy is used to describe business analytics? It's like GPS and MapQuest rolled into one. What is the significance of data in business analytics? Using all items of data to tell a story and make a plan is the value in business. What are some sources of business analytics? Data mining, order processing, pricing, inventory management, purchasing, inventory control, sales analysis, ERP systems, general ledger, accounts payable, accounts receivable, and truck routing. What are the benefits of business analytics? Gaining insights that inform business decisions and automating and optimizing business processes. How do data-driven companies treat their data? As a corporate asset and leverage it for competitive advantage. What factors contribute to successful business analytics?
What does it mean to automate business processes through analytics? It means using data insights to streamline and improve efficiency in business operations. What is the impact of data quality on business analytics? High data quality is essential for accurate insights and effective decision making. In what ways can business analytics provide a competitive advantage? By leveraging data to inform decisions and optimize processes, companies can outperform competitors. What is the significance of organizational commitment in business analytics? An organizational commitment to data-driven decision making is necessary for successful implementation of business analytics. How does business analytics help in problem-solving? It identifies gaps between current situations and desired outcomes, guiding decision makers to effective solutions. What does the phrase 'the gap between the two is the problem' refer to in business analytics? It refers to the difference between what is currently happening and what is desired, which business analytics aims to address. What is data mining?
Exploring data to find new patterns and relationships. What does statistical analysis explain? It explains why a certain result occurred. What is A/B testing used for? Experimenting to test previous decisions. What is predictive modeling? Forecasting future results. Why is analytics necessary for distributors? It helps gain a competitive edge. How does analytics assist decision makers? It allows them to make better informed decisions. What is a key challenge in making business analytics work? Not focusing on what's really important, such as customers and products. What is a recommended tool for presenting data? A dashboard. What is a common issue with ERP systems in relation to analytics? They are often not set up to make information easily accessible. What are the usual analytics areas included in a business dashboard?
