Master Regression Analysis for Supply Chain Predictions

Question 1

Which regression model is suitable for predicting future demand for a product, given historical data on sales, prices, and marketing campaigns?

Accepted Answer

Demand = b0 + b1(Sales) + b2(Price) + b3(Marketing)

Answer

Demand = b0 + b1(Sales) * b2(Price) * b3(Marketing)

Answer

Demand = b0 + b1(Sales) + b2(Price)

Question 2

What is the primary objective of regression analysis in supply chain management?

Accepted Answer

To pinpoint the independent variables that most significantly impact a dependent variable

Answer

To forecast future supply chain outcomes using historical data

Answer

To optimize supply chain processes and enhance efficiency

Answer

To establish optimal inventory levels for a specific product

Question 3

What is the R-squared value used for in regression analysis?

Accepted Answer

To assess how well a regression model fits the data

Answer

To determine the significance of individual independent variables

Answer

To make predictions about future supply chain outcomes

Answer

To identify potential outliers in the data

Question 4

Which of the following is NOT a standard step in developing a regression model?

Accepted Answer

Hypothesis testing

Answer

Data collection

Answer

Model selection

Answer

Model interpretation

Question 5

In supply chain management, the central goal of regression analysis is to:

Accepted Answer

Determine the key drivers of supply chain performance.

Answer

Predict future demand and optimize inventory levels.

Answer

Reduce production costs and enhance service levels.

Answer

Evaluate the effectiveness of various supply chain strategies.

Question 6

Within a supply chain regression model, the variable being predicted is termed as:

Accepted Answer

Dependent variable

Answer

Independent variable

Answer

Control variable

Answer

Mediating variable

Question 7

The coefficient of determination (R-squared) in regression analysis serves to:

Accepted Answer

Quantify the proportion of variation in the dependent variable explained by the model.

Answer

Indicate the statistical significance of the model.

Answer

Identify the most influential independent variable.

Answer

Predict the exact value of the dependent variable.

Question 8

To transform a non-linear relationship into a linear one, which of the following techniques can be employed?

Accepted Answer

Any of the aforementioned techniques.

Answer

Logarithmic transformation

Answer

Exponential transformation

Answer

Polynomial regression

Question 9

The distinction between simple and multiple regression lies in the:

Accepted Answer

Number of independent variables used.

Answer

Purpose of the analysis (prediction versus explanation).

Answer

Complexity of the statistical model.

Answer

Type of dependent variable (continuous versus categorical).

Question 10

The primary objective of cross-validation in regression analysis is to:

Accepted Answer

Prevent overfitting and enhance generalization.

Answer

Assess the predictive accuracy of the model.

Answer

Identify the optimal combination of independent variables.

Answer

Increase the R-squared value.

Question 11

Categorical independent variables, such as product type, are often represented in regression analysis using:

Accepted Answer

Dummy variables

Answer

Continuous variables

Answer

Interaction terms

Answer

Polynomial terms

Question 12

The primary reason for incorporating dummy variables in regression analysis is to:

Accepted Answer

Represent categorical variables.

Answer

Mitigate multicollinearity.

Answer

Enhance the accuracy of the model.

Answer

Simplify the interpretation of the model.

Question 13

The primary purpose of regression analysis in supply chain management is to:

Accepted Answer

Predict future supply chain outcomes

Answer

Analyze trends in supply chain data

Answer

Identify inefficiencies in the supply chain

Answer

Optimize inventory levels

Question 14

Which of the following is NOT a common type of regression model used in supply chain analysis?

Accepted Answer

Monte Carlo simulation

Answer

Linear regression

Answer

Logistic regression

Answer

Decision tree regression

Question 15

The R-squared value in a regression model represents:

Accepted Answer

The strength of the relationship between variables

Answer

The accuracy of the model

Answer

The significance of the model

Answer

The number of observations in the model

Question 16

In regression analysis, a low p-value indicates:

Accepted Answer

The model is statistically significant

Answer

The relationship between variables is weak

Answer

The model is overfitting

Answer

The data is not normally distributed

Question 17

Which of the following is NOT a benefit of normalizing data in regression analysis?

Accepted Answer

Increases the accuracy of the model

Answer

Improves the stability of the model

Answer

Reduces the effect of outliers

Answer

Simplifies the interpretation of the model

Question 18

The purpose of cross-validation in regression model building is to:

Accepted Answer

Reduce overfitting and improve generalization

Answer

Identify the optimal number of independent variables

Answer

Select the best regression algorithm

Answer

Clean the data

Question 19

Which of the following is NOT a key assumption of linear regression analysis?

Accepted Answer

Extraneous variables are known and accounted for

Answer

Linear relationship between the dependent and independent variables

Answer

Homoscedasticity of residuals

Answer

No autocorrelation in the residuals

Question 20

What is the primary purpose of a scatterplot in regression analysis?

Accepted Answer

To visualize the relationship between variables and identify potential outliers

Answer

To determine the slope of the regression line

Answer

To predict future outcomes

Answer

To test the significance of the regression model

Question 21

Which statistical measure indicates the strength of the linear relationship between variables?

Accepted Answer

Correlation coefficient

Answer

Slope of the regression line

Answer

Intercept of the regression line

Answer

Coefficient of determination

Question 22

In a regression model, the slope coefficient represents:

Accepted Answer

The change in the dependent variable for a one-unit increase in the independent variable

Answer

The y-intercept of the regression line

Answer

The correlation coefficient between variables

Answer

The mean value of the dependent variable

Question 23

What is the purpose of a confidence interval in regression analysis?

Accepted Answer

To estimate the range of values that the population parameter may fall within

Answer

To determine the significance of the regression model

Answer

To predict future outcomes

Answer

To test the assumptions of regression

Question 24

Which of the following is NOT a variable selection method used in regression analysis?

Accepted Answer

Random selection

Answer

Forward selection

Answer

Backward selection

Answer

Principal component analysis

Question 25

Distinguish between goodness-of-fit and predictive accuracy in regression analysis.

Accepted Answer

Goodness-of-fit measures the model's performance on the training data, while predictive accuracy measures its performance on new data.

Answer

Predictive accuracy is more important than goodness-of-fit.

Answer

Goodness-of-fit and predictive accuracy are equivalent concepts.

Answer

Goodness-of-fit measures the strength of the relationship between variables, while predictive accuracy measures the precision of predictions.

Question 26

In supply chain management, how can regression analysis be combined with other analytical techniques, such as machine learning or simulation, to enhance decision-making?

Accepted Answer

Regression analysis can uncover patterns and relationships in data, which can then be used to improve the accuracy of predictive models developed using machine learning or simulation techniques.

Answer

Regression analysis provides a structured framework for organizing and understanding complex supply chain data.

Answer

Regression analysis automates the process of building and interpreting regression models.

Answer

Regression analysis eliminates the need for other analytical techniques in supply chain decision-making.

Question 27

Which of the following is NOT a recognized type of regression model?

Accepted Answer

Cluster regression

Answer

Linear regression

Answer

Logistic regression

Answer

Polynomial regression

Question 28

In a regression model, the intercept term indicates:

Accepted Answer

The predicted output value when all input variables are zero

Answer

The adjustment factor for the slope of the regression line

Answer

The estimation of the error term

Answer

The determination of the model's goodness of fit

Question 29

The coefficient of determination (R-squared) in a regression model primarily measures:

Accepted Answer

The goodness of fit of the model

Answer

The estimation of the error term's magnitude

Answer

The adjustment of the regression line's slope

Answer

The significance of the independent variables

Question 30

Which of the following measures the strength and direction of the linear relationship?

Accepted Answer

Correlation coefficient

Answer

Slope

Answer

Intercept

Answer

Residuals

Question 31

The coefficient of determination (R-squared) primarily indicates the:

Accepted Answer

Proportion of variability in the dependent variable explained by the model

Answer

Statistical significance of the model

Answer

Predicted value of the dependent variable

Answer

Magnitude of the residuals

Question 32

Which of the following is an example of a time series regression model that uses both past values and random error terms to predict future values?

Accepted Answer

Autoregressive integrated moving average (ARIMA) model

Answer

Exponential smoothing model

Answer

Linear regression model

Answer

Logistic regression model

Question 33

What is the primary function of the intercept in a regression model?

Accepted Answer

To represent the predicted value of the dependent variable when the independent variable is zero.

Answer

To indicate the slope of the regression line.

Answer

To adjust for the units of measurement of the variables.

Answer

To determine the direction of the relationship between the variables.

Question 34

Which measure assesses the proportion of variance in the dependent variable that is explained by the regression model?

Accepted Answer

R-squared

Answer

Mean absolute error

Answer

Adjusted R-squared

Answer

Root mean squared error

Question 35

What is the process of identifying the most relevant and informative independent variables for a regression model called?

Accepted Answer

Variable selection

Answer

Data transformation

Answer

Model validation

Answer

Outlier detection

Question 36

What is the primary purpose of a residual plot in regression analysis?

Accepted Answer

To identify patterns in the residuals and assess the validity of the model's assumptions.

Answer

To predict future values of the dependent variable.

Answer

To select the best regression model.

Answer

To calculate the R-squared value.

Question 37

What is the term for the metric that quantifies the proportion of variance in the dependent variable that is explained by the regression model?

Accepted Answer

R-squared

Answer

Coefficient of determination

Answer

Mean squared error

Answer

Standard deviation

Question 38

What is a key advantage of using regression analysis in supply chain management?

Accepted Answer

It enables accurate predictions of future supply chain performance based on historical data.

Answer

It can handle large and complex datasets efficiently.

Answer

It provides a graphical representation of relationships between variables.

Answer

It is a straightforward technique that requires minimal expertise to apply.

Question 39

Define correlation and linearity, and explain their significance in regression analysis.

Accepted Answer

Correlation measures the strength of the relationship between variables, while linearity describes the form of that relationship.

Answer

Correlation alone determines a regression model's accuracy, regardless of linearity.

Answer

Linearity is only relevant in simple regression models, not multiple regression models.

Question 40

What does the R-squared measure represent?

Accepted Answer

The proportion of variability in the dependent variable explained by the independent variables.

Answer

The accuracy of the regression model.

Answer

The strength of the relationship between the independent and dependent variables.

Question 41

Which technique is used to identify influential observations in a regression model?

Accepted Answer

Cook's distance

Answer

F-test

Answer

Adjusted R-squared

Answer

Multicollinearity test

Question 42

What distinguishes a scatterplot from a residual plot?

Accepted Answer

A scatterplot displays the relationship between independent and dependent variables, while a residual plot shows the difference between predicted and actual dependent variable values.

Answer

A scatterplot uses continuous variables, while a residual plot uses categorical variables.

Question 43

What is the purpose of a dummy (indicator) variable?

Accepted Answer

To represent a categorical independent variable in a regression model.

Answer

To control for the effects of outliers.

Answer

To transform non-linear relationships into linear ones.

Question 44

What advantage does regression analysis offer?

Accepted Answer

It aids in identifying independent variables with significant impact on the dependent variable.

Answer

It is consistently the most accurate method for dependent variable prediction.

Question 45

How does multiple regression differ from simple regression?

Accepted Answer

Multiple regression predicts a dependent variable from multiple independent variables, whereas simple regression uses a single independent variable.

Answer

Multiple regression is used with continuous variables, while simple regression is used with categorical variables.

Question 46

What is the purpose of a confidence interval in regression analysis?

Accepted Answer

To estimate the range within which the true value of a parameter is likely to fall.

Answer

To test the significance of an independent variable.

Answer

To predict the value of a dependent variable.

Question 47

Which of the following is NOT a key assumption of linear regression?

Accepted Answer

The independent variables must be normally distributed.

Answer

The error terms must have constant variance.

Answer

The relationship between the dependent and independent variables must be linear.

Question 48

A regression model has an R-squared value of 0.85. What does this indicate?

Accepted Answer

85% of the variation in the dependent variable is explained by the independent variable(s).

Answer

The model is 85% accurate in predicting the dependent variable.

Answer

The model has a strong positive correlation between the variables.

Question 49

A supply chain manager uses regression analysis to predict the demand for a product based on historical sales data. The model shows a statistically significant negative coefficient for the price of the product. What does this imply?

Accepted Answer

As the price of the product increases, demand for the product decreases.

Answer

The price of the product has no impact on demand.

Answer

The model is not accurate because price shouldn't affect demand.

Question 50

Which of the following is a common method for handling multicollinearity in regression analysis?

Accepted Answer

Removing one or more of the correlated independent variables.

Answer

Ignoring the problem, as it doesn't affect the model's accuracy.

Answer

Increasing the sample size to reduce the impact of multicollinearity.

Question 51

A supply chain manager is using regression analysis to predict lead time for a component. The model includes factors such as distance to supplier, supplier reliability, and order quantity. Which of the following is likely to be the dependent variable in this model?

Accepted Answer

Lead time

Answer

Supplier reliability

Answer

Order quantity

Answer

Distance to supplier

Question 52

What is the purpose of a residual plot in regression analysis?

Accepted Answer

To assess the assumptions of linearity and constant variance of the error terms.

Answer

To determine the R-squared value of the model.

Answer

To identify the most important independent variables.

Question 53

A company is using regression analysis to predict the cost of transportation for different routes. Which of the following variables is LEAST likely to be a useful independent variable in this model?

Accepted Answer

The color of the delivery truck.

Answer

Distance of the route.

Answer

Fuel prices.

Answer

Average weight of the shipment.

Question 54

Which statistical test is used to assess the overall significance of a regression model?

Accepted Answer

F-test

Answer

T-test

Answer

Chi-square test

Answer

ANOVA test

Question 55

A supply chain manager wants to predict the number of customer orders received each month. Which of the following is MOST likely to be a relevant independent variable in this regression model?

Accepted Answer

Number of marketing campaigns launched in the previous month.

Answer

Average temperature in the previous month.

Answer

Number of employees in the company.

Question 56

What is the main difference between simple linear regression and multiple linear regression?

Accepted Answer

Simple linear regression uses one independent variable, while multiple linear regression uses two or more independent variables.

Answer

Simple linear regression is used for categorical data, while multiple linear regression is used for numerical data.