C207 Data-Driven Decision Making Practice Questions - Set 1 - Part 2

Correct Answer: The line of best fit

The regression line minimizes the errors between predicted values and actual values, making it the line that best fits the data.

Correct Answer: Cleaning and storing collected data

Data management refers to the process of organizing, cleaning, and storing data to ensure it is accurate and ready for analysis.

Correct Answer: Irregular and unpredictable fluctuations

Random variation is variability in data caused by unpredictable factors, making it impossible to anticipate or eliminate through process changes.

Correct Answer: Regular patterns that occur within a year

Seasonality is the presence of regular, predictable fluctuations in data that occur at the same time each year.

Correct Answer: Equal variance across all levels of the independent variable

Homoscedasticity means that the spread of residuals is consistent across all levels of the independent variable, which is an assumption of linear regression.

Correct Answer: One-time deviations due to unforeseen events

Irregularities are unpredictable, one-time deviations from expected patterns, often caused by events such as natural disasters or economic shocks.

Correct Answer: Examine the distribution of categorical data

The Chi-squared Test is used to determine whether the distribution of categorical data fits expected patterns or differs significantly.

Correct Answer: Summing all possible payoffs, weighted by their probabilities

The Expected Value is the weighted average of all potential outcomes, with each outcome’s value multiplied by its probability.

Correct Answer: Model the relationship between one dependent variable and multiple independent variables

Multiple Linear Regression allows for the analysis of how two or more independent variables affect a dependent variable.

Correct Answer: Visualize possible alternatives and their consequences

A Decision Tree helps decision-makers visualize different options and the potential outcomes for each, aiding in complex decision-making.

Correct Answer: Using time as the independent variable in regression

Time series analysis examines how a variable changes over time, using time itself as the independent variable.

Correct Answer: The sum of all payoffs, weighted by their probabilities

The Expected Value represents the average expected outcome, calculated by summing the possible outcomes, each weighted by its likelihood.

Correct Answer: Measure the strength of the relationship between variables

R-squared indicates the proportion of variance in the dependent variable that is explained by the independent variable(s), reflecting the strength of their relationship.

Correct Answer: A variable is correlated with itself over time

Autocorrelation occurs when the value of a variable at one point in time is related to its values at previous points in time.

Correct Answer: Group data points into "natural" clusters based on similarity

Cluster analysis helps in identifying natural groupings within a dataset based on similar characteristics, often used in market segmentation.

Correct Answer: A data point that is significantly distant from other observations

An outlier is a data point that differs significantly from the rest of the dataset and can often distort statistical analyses.

Correct Answer: The "average" deviation of data points from the regression line

The Standard Error of Estimate measures how well the regression line fits the data by calculating the average distance of data points from the line.

Correct Answer: Shows how process elements fit together

A SIPOC diagram outlines the Suppliers, Inputs, Processes, Outputs, and Customers involved in a process, helping to visualize how the elements of a process interconnect.

Correct Answer: Both the products and the processes used

Quality management ensures that both the products and the processes used to make them meet the necessary standards, leading to better overall quality.

Correct Answer: Metrics to analyze results

Statistical process control uses metrics to monitor and control processes, allowing for the identification of variations that may affect quality.