Statistical Business Analysis Quiz! Hardest Trivia Questions

An acceptable division between training, validation, and testing data is to allocate 50% of the data for training the predictive model, 50% for validating the model's performance, and 0% for testing. This division allows for a balanced evaluation of the model's accuracy and generalization ability. The training data is used to train the model, the validation data is used to fine-tune the model and select the best hyperparameters, and the testing data is used to assess the final performance of the model on unseen data.

The missing option to complete the syntax is "hovtest". This option is used to perform the homogeneity of variance test in the GLM procedure. The hovtest function calculates a test statistic to determine if the variances of the groups being compared are significantly different. By including this option, the syntax will run the hovtest procedure to test the assumption of homogeneity of variance in the GLM analysis.

The correct answer is "Only XL and 2XL are not significantly different from each other." This conclusion is justified because the control plot shows that all other groups are significantly different from each other, indicating that XL and 2XL are the only groups that are not significantly different.

The Adjusted R-Squared statistic justifies this rationale because it takes into account the number of predictors in the model. It adjusts the R-Squared value by penalizing for the inclusion of additional predictors that may not significantly contribute to the model fit. Therefore, a higher Adjusted R-Squared value indicates a better model fit, even when compared to a model with more predictors.

The total number of the sample size is 100, as stated in the answer. This means that there were 100 participants or observations in the sample.

Program B is likely to detect collinearity in a multiple regression application. The question does not provide specific details about the programs, but based on the context, it can be inferred that Program B is designed to identify collinearity. Collinearity refers to the situation where two or more predictor variables in a regression model are highly correlated, which can lead to unstable and unreliable estimates of the regression coefficients. Therefore, Program B is the most appropriate choice for detecting collinearity in a multiple regression application.

Model B should be selected because it has a higher accuracy than model A on the validation data. Additionally, model B is simpler, which suggests that it may be more robust and less prone to overfitting.

The most likely reason that the model fails to converge is that there is quasi-complete separation in the data. Quasi-complete separation occurs when there is a predictor that perfectly predicts the outcome variable, resulting in a division of the data into distinct groups. This can cause issues in logistic regression because it leads to infinite parameter estimates. In this case, the number of defaulted loans per agent may be perfectly predicting whether or not a client will default on a loan, causing the model to fail to converge.

Quasi-complete separation occurs when a predictor variable perfectly predicts the outcome variable, resulting in extreme coefficients and standard errors in logistic regression. In this case, the probability of purchasing the item is 1 when Region = A, indicating a perfect separation between the predictor and the outcome variable. This can lead to convergence issues in the logistic regression model and make it difficult to estimate accurate coefficients and standard errors.

The intercept estimate is interpreted as the predicted value of the response when all predictors are at their minimum values. This means that when all predictors are set to 0, the intercept estimate represents the expected value of the response variable.

In partitioning data for model assessment, both simple random sampling without replacement and stratified random sampling without replacement are acceptable methods. Simple random sampling without replacement involves randomly selecting a subset of data without replacement, ensuring that each sample is unique. Stratified random sampling without replacement involves dividing the data into distinct groups or strata based on certain characteristics and then randomly selecting samples from each stratum without replacement. Sequential random sampling with replacement and simple random sampling with replacement are not acceptable methods for partitioning data for model assessment.

The two most effective ways to address the problem of having too many levels for the storeId predictor are to cluster the stores using Greenacre's method to combine similar stores and to use subject matter expertise to combine similar stores. Clustering allows for grouping of stores based on similarities, while subject matter expertise allows for a more nuanced understanding of the stores and their similarities. Both approaches help to reduce the number of levels for the storeId predictor, making it more feasible for the model.

Including redundant input variables in a regression model can destabilize parameter estimates because these variables do not contribute any additional information to the model. This can lead to unstable and unreliable estimates of the parameters. Additionally, including redundant variables increases the risk of overfitting, where the model becomes too complex and fits the noise in the data rather than the underlying relationship. Overfitting can result in poor generalization to new data and decreased model performance.

When data is oversampled due to a rare target, it means that the rare target is artificially increased in the dataset to balance the class distribution. Sensitivity and specificity are performance metrics that measure the accuracy of a classification model and are calculated based on the true positive, true negative, false positive, and false negative values. Since oversampling does not change the true positive and true negative values, sensitivity and specificity remain unaffected.

The correct answer is "Gender should not be removed due to its involvement in the significant interaction." This statement suggests that gender should not be removed from the analysis because it plays a significant role in the interaction between variables. This implies that gender has an impact on the outcome being studied and should therefore be considered in the analysis.

As you move along the ROC curve, the probability cutoff for scoring changes. The ROC curve is a graphical representation of the trade-off between the true positive rate (sensitivity) and the false positive rate (1-specificity) for different probability cutoffs. As the cutoff changes, the sensitivity and specificity values also change, causing the points on the ROC curve to shift. The probability cutoff determines the threshold for classifying an observation as positive or negative, and adjusting it can affect the balance between correctly identifying true positives and minimizing false positives.

The correct answer is A because the marketing manager wants to determine the customers most likely to purchase additional products, which suggests a predictive modeling problem. The manager has a historical dataset with a target variable (Respond) and predictor variables (Income and Homeowner). To perform this analysis, the manager would need to use a predictive modeling technique, such as logistic regression or decision tree, which can be implemented using SAS programming.

The ROC curve summarizes the performance of the logistic regression model in predicting whether a customer would make a purchase. The fact that 25% of the customers who did not make a purchase are incorrectly classified as purchasers indicates that there is a certain level of misclassification. From the graph, it can be concluded that about 85% of the customers who did make a purchase are correctly classified as making a purchase. This suggests that the model has a relatively high accuracy in predicting customers who make a purchase.

In the LOGISTIC procedure, a concordant pair of observations refers to a situation where an observation with the event (outcome of interest) has a higher predicted probability than an observation without the event. This implies that the model is correctly predicting the occurrence of the event, as the observation with the event has a higher likelihood of experiencing it compared to the observation without the event.

The default method in the LOGISTIC procedure to handle observations with missing data is to only use cases with variables that are fully populated. This means that any observation with missing data will be excluded from the analysis.

A linear model with a dependent variable, one continuous variable with a quadratic term, one categorical predictor variable, and an interaction term would have a total of 7 parameters. These parameters include the intercept, the coefficients for the continuous variable and its quadratic term, the coefficients for the categorical variable's three levels, and the coefficient for the interaction term.

The significant interaction between REGION and VALUE indicates that the relationship between the average purchases for medium and high value customers varies depending on the region. This suggests that the impact of customer value on purchases differs across different regions, indicating that the regional factor plays a role in influencing customer behavior and purchase patterns.

The given answer suggests that there is a significant difference in percentage of body fat between individuals with large wrist size and individuals with small wrist size. This conclusion is based on the information provided in the question, which states that the percentage of body fat varies significantly depending on wrist size. Therefore, it can be inferred that individuals with larger wrist sizes have a significantly different percentage of body fat compared to individuals with smaller wrist sizes.

The assumption placed on the errors in linear regression is that they are independent, normally distributed with zero mean and constant variance. This means that the errors are not correlated with each other, they follow a normal distribution, their average value is zero, and their variance remains constant across all levels of the predictor variable.

Performing data cleansing on raw data prior to partitioning the data for honest assessment means that the cleansing methods cannot be compared for their effectiveness. This is because the data is already cleaned before it is divided into training and test sets, so there is no way to evaluate how well each cleansing method performs on different subsets of the data. This drawback limits the ability to make informed decisions about which cleansing methods are most effective for improving the quality of the data.

The correct answer is constant variance because the interquartile ranges are different in different ad campaigns. In ANOVA, one of the assumptions is that the variances of the groups being compared are equal. The interquartile range is a measure of dispersion, and if the interquartile ranges are different between the ad campaigns, it suggests that the variances are not equal. Therefore, the assumption of constant variance is violated.

Option B is the correct answer because it is the only option that mentions the use of backward elimination selection criterion within the REG procedure in SAS. Backward elimination is a variable selection method where variables are removed from the model one by one based on their significance level. The REG procedure in SAS is used for regression analysis, and backward elimination is one of the variable selection methods available in this procedure. Therefore, option B is the correct choice for using backward elimination in the REG procedure.

The purpose of the test data set is to provide an unbiased measure of assessment for the final model. This means that the test data set is used to evaluate the performance of the model that has been built using the training and validation data sets. By using a separate test data set, it ensures that the evaluation of the model is not influenced by the data that was used to build and fine-tune the model. This helps to provide a more accurate assessment of how the model will perform on new, unseen data.

When a non-contributing predictor variable is added to an existing multiple linear regression model, it means that the variable does not significantly contribute to the prediction of the dependent variable. Therefore, adding this variable will not improve the model's predictive power. As a result, there will be no change in the R-Square value, which measures the proportion of variance in the dependent variable that is explained by the independent variables.

The GLM procedure output indicates that 76% of the customers' intent to purchase is explained by the market segment. This means that the customers' likelihood or willingness to purchase the product is significantly influenced by the market segment they belong to. The analyst divided the customers into different groups based on the company's pre-assigned market segments and found that these segments have a strong impact on the customers' intent to purchase.

The reference line at lift = 1 corresponds to the predicted lift if the entire population is scored as event cases. This means that if all individuals in the population are classified as event cases, the lift will be equal to 1. Lift is a measure of the effectiveness of a predictive model in identifying the target variable, and a lift value of 1 indicates that the model is not performing better than random chance.

This answer describes a discordant pair of observations in the LOGISTIC procedure where an observation with the event has a lower predicted probability than the observation without the event. This suggests that the presence of the event is not strongly associated with a higher probability of occurrence, which may indicate a contradiction or inconsistency in the data.

The Spearman statistics in the CORR procedure are useful for screening for irrelevant variables by investigating the association between rank-ordered values of the variables. This means that the Spearman statistics measure the strength and direction of the monotonic relationship between variables, which can help identify variables that are not relevant or do not have a significant impact on the outcome. By ranking the values of the variables and calculating the Spearman correlation coefficient, researchers can determine if there is a consistent pattern or trend between the variables, which can inform further analysis and variable selection.

Candidate 2 is the champion model because the area outside the curve is greater. This indicates that Candidate 2 has a higher true positive rate and a lower false positive rate compared to Candidate 1. A larger area under the curve suggests better overall performance and accuracy in distinguishing between positive and negative cases. Therefore, Candidate 2 is considered the better model based on the comparative ROC plot.

When mean imputation is performed on data after the data is partitioned for an honest assessment, the most appropriate method for handling the mean imputation is to apply the sample means from the training data set to the validation and test data sets. This ensures that the imputation is based on the patterns and characteristics of the training data, which is the most accurate representation of the overall dataset. Applying the sample means from the validation or test data sets to the other sets would introduce bias and potentially distort the assessment of the model's performance.

A trend plot of empirical logit versus a predictor variable can be used to screen for non-linearity in binary logistic regression. This plot helps to visualize the relationship between the predictor variable and the log odds of the binary response. If the trend plot shows a non-linear pattern, it suggests that there may be a non-linear relationship between the predictor variable and the log odds, indicating the need for non-linear modeling techniques or transformations of the predictor variable.

Adjusted R Square is a statistic that indicates the proportion of the variance in the dependent variable that is explained by the independent variables in a regression model. As Adjusted R Square increases, it suggests that the model is better at explaining the variation in the dependent variable. Therefore, a larger value of Adjusted R Square indicates a better model.

A predictive model that uses a dataset with missing values can lead to two problems. First, complete case analysis means that fewer observations will be used in the model building process. This can result in a loss of valuable information and potential bias in the model. Second, new cases with missing values on input variables cannot be scored without extra data processing. This means that the model may not be able to make accurate predictions for new data points that have missing values, requiring additional steps to handle missing data before scoring.

The champion model is the one with the lowest AIC statistic. Since the AIC statistic is not provided in the given information, it is not possible to determine which model is the champion model.

The given SAS program calculates the sensitivity. Sensitivity is a measure of the proportion of actual positive cases that are correctly identified as positive by the model. In this case, the program uses logistic regression to calculate probabilities of events and nonevents, and then applies a decision rule to classify data as an event if the probability is greater than 0.5. Sensitivity measures how well the model identifies the actual positive cases correctly.

The varclus procedure in SAS provides a viable solution for regression applications with variable clusters in the input variables. This procedure helps in identifying groups of variables that have high correlation with each other, allowing for the creation of clusters. By using varclus, researchers can reduce the dimensionality of their data and select representative variables from each cluster, which can then be used in regression analysis.

Excluding redundant input variables in a regression model can stabilize parameter estimates by removing unnecessary variables that do not contribute significantly to the model's prediction. This helps to reduce the variability in the estimated coefficients, making them more reliable. Additionally, removing redundant variables can decrease the risk of overfitting, which occurs when a model fits the training data too closely and performs poorly on new, unseen data. By simplifying the model and focusing on the most relevant variables, the risk of overfitting is reduced, leading to better generalization and predictive accuracy.

The correct answer is the last option. This is because it first sorts the data set by county using the PROC SORT step. Then, it uses the PROC SURVEYSELECT step to divide the data set into a training set and a validation set, with a sampling rate of 0.6 (60%). The stratification is done by county, ensuring that the two sets have a proportional representation of each county in the original data set. The OUT= option is used to create a new data set named "sample" that contains the selected observations. The OUTALL option is used to include all observations in the output data set, not just the selected ones.

The best data to use for model assessment is the validation data. This is because the validation data is not used during the training process and is therefore unseen by the model. It serves as an unbiased measure of the model's performance and can be used to fine-tune the model's hyperparameters or compare different models. The test data, on the other hand, should be kept separate and only used at the very end to provide a final evaluation of the model's performance.

The correct answer is that the association between the continuous predictor and the log-odds is quadratic. This means that as the continuous predictor increases, the log-odds of the binary response also increase, but at a decreasing rate. In other words, the relationship between the continuous predictor and the log-odds is not linear, but instead follows a quadratic pattern. This suggests that there may be a curvilinear relationship between the continuous predictor and the binary response.

This means that if we select the top 10% of the population based on the model's score, we can expect to see 3.14 times more events compared to randomly selecting 10% of the population. In other words, the model is able to accurately identify a higher proportion of events when selecting from the top 10% based on its score.

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The selection criterion used in the forward selection method in the REG procedure is SLE, which stands for significance level of entry. This criterion is used to determine the level of significance at which a predictor variable should be included in the regression model. The forward selection method starts with an empty model and iteratively adds variables that have the lowest p-values (significance levels) until no more variables meet the predetermined significance level for entry. This helps to identify the most significant predictor variables for the regression model.

Option D is not a correct way to create dummy variables because it does not include any code to create dummy variables. The other options (A, B, and C) may include code that creates dummy variables based on the values in the "Inc_Group" variable. Without seeing the code in each option, it is not possible to determine which option is the correct way to create dummy variables.

The correct combination of statement and option to generate sensitivity and specificity statistics on a validation data set for a variety of cutoff values is "Score data=valid1 outroc=roc". This statement will score the validation data set and output the receiver operating characteristic (ROC) curve, which can be used to calculate sensitivity and specificity at different cutoff values.