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DMD 14 MOD I Review

DMD 14 MOD I Review. David Kopcso and Richard Cleary. Data Analysis: Descriptive. Basic Idea: To explore a data set in search of useful information. Data Analysis: Hypothesis Testing. Basic Idea: To test whether observed differences are due to random chance or substantive changes.

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DMD 14 MOD I Review

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  1. DMD 14MOD I Review David Kopcso and Richard Cleary

  2. Data Analysis: Descriptive Basic Idea: To explore a data set in search of useful information

  3. Data Analysis: Hypothesis Testing • Basic Idea: To test whether observed differences are due to random chance or substantive changes. • Know how to translate a business proposition into null and alternative hypotheses. • Know how to state the conclusions of a hypothesis test in managerial terms.

  4. Hypothesis Statement Translation E.g.: A new drug developed by Biotech, Inc. does or does not make an improvement; i.e., the cure rate (percentage cured) for the new drug is higher than the cure rate for the major competitor which is 52%. The parameter would be the cure rate for the new drug and if we were Biotech, Inc., the manufacturer of the new drug, we want to show that our cure rate exceeds 52%. E.g.: Spiffy Lube is interested in improving service. Currently the average number of minutes to change an auto’s oil and filter (i.e., service a customer) is at least 20. Spiffy Lube will adopt a new procedure and incur the cost of retaining staff if Spiffy Lube is convinced that the new procedure reduces service time. The parameter would be the average service time of the new procedure and if we were to adopt the new procedure, we would want to show that average service time of the new procedure is significantly less than 20 minutes.

  5. If the p-value < a, it is safe to conclude that the st.devs. are NOT equal, i.e., use p-value in Unequal. Here, the Equality of Variances Test p-value is large, thus it is safe to assume equal variances.

  6. Data Analysis: Regression • Basic Idea: To quantify relationships between variables and make predictions. • Know the steps in a regression analysis. • Know why it is important to look at scatterplots of the data and of the residuals.

  7. Sample Regression Output

  8. Scatter Plots and Residual Assumptions

  9. Is the relationship between the Residuals and Real DPI quadratic?

  10. Decision Analysis • Basic Idea: Use decision trees to structures and analyze decisions. • Know how to structure a decision using decision trees, i.e., construct tree from word problem. • Know how to interpret decision trees. • Know how to calculate expected values, sometimes referred to as EMV (expected monetary value). • Sensitivity Analysis • Basic Idea: Determine which variables are influential and which are not in the decision. • Know how to interpret tornado diagrams and strategy charts. • Know the advantages and disadvantages of sensitivity analysis.

  11. Example: Tokay Wine

  12. Risk Profile

  13. Strategy Graph: Value Tokay • Strategy graphs show the input variable on the x-axis and the output variable on the y-axis. They also show the output variable for each decision alternative.

  14. Tornado Graphs • Tornado graphs are horizontal bar charts. The length of each bar indicates the amount the output variable changes as the corresponding input variable (on the vertical axis) varies.

  15. Simulation Basic Idea: Model the inherent uncertainty using probability distribution functions and thereby more fully understand associated risks. • Know how to replace static values with probability distributions. • Understand what probability distributions represent, and know some common types covered in applications discussed in class (normal, uniform, discrete, beta, triangular) • Know how to run a simulation analysis and interpret the results. • Be able to explain the difference between a simulation analysis and a worst-case/best-case analysis. Sensitivity Analysis Basic idea: Determine the influence of the uncertainties.

  16. Contingency Analysis: Pivot Tables Basic Idea: Test if there is a relationship between categorical variables. • How to interpret counts and row/column percentages in the table. • What is the null hypothesis for the Chi-Square Test? • What is a Type I/Type II error in the context of contingency analysis? • How to use to make business/marketing recommendations? Common Misconceptions • Using categorical variables to obtain correlations • Predicting a categorical variable using least-square regression

  17. Pivot Table of Counts

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