WPC 300 Final Latest Update Graded A+

WPC 300 Final Latest Update Graded A+ analytics the process of developing actionable decisions or recommendations for actions based upon insights generated from historical data primary data data collected specifically for the research problem at hand (ex: survey, interviews) secondary data data collected for some purpose other than the problem at hand (ex: firm's proprietary data, internet data, stock/capital market data) stimulated data data based on assumption and simulation importance of data visualization 1. visual elements allow us to see and understand trends, outliers, & patterns in data 2. can comprehend difficult concepts or identify new patterns more easily 3. humans LOVE visuals 3 main principles of data visualization 1. chart should tell a story / yield insight beyond text 2. chart should have graphical integrity (Tufte's "Lie" factor) 3. chart should min graphical complexity (Tufte's "data ink" ratio) statistics science concerned with developing and studying methods for collecting, analyzing, interpreting, and presenting empirical data to assist in making effective decisions descriptive statistic study data in entirety 3 principles of describing data: center, spread, shape inferential statistics utilize random sample of data taken from population to describe and make INFERENCES about the population - reliability of conclusion dependent on CL 3 principles of descriptive statistics 1. Data centrality (mean, median, mode) 2Data spread / variability (range, MAD, variance, stdv) 3. Data shape (kurtosis) kurtosis measure of whether the data are peaked or flat relative to a normal distribution High kurtosis = data is peaked near mean, declines rather rapidly, and has heavy tails Low kurtosis = data is flat near mean covariance measure of the DIRECTION of linear association between two variables scaled between negative infinity and positive infinity aka how variables vary from each other correlation measure of linear relationship between two variables which does NOT depend on units of measurement Scaled between -1 (perfect negative correlation) and 1 (perfect positive correlation) w/ 0 = no correlation margin of error the percentage in which the sample will DIFFER from the population sampling error an error that occurs when a sample somehow does not represent the target population Central Limit Theorem (CLT) Says that when n is large (n = 30), the sampling distribution of the sample mean is approximately Normal standard error the standard deviation of a sampling distribution Confidence Interval (CI) roughly speaking, the RANGEEE of scores (that is, the scores between an upper and lower value) that