ISYE 6414-REGRESSION ANALYSIS
PROJECT REPORT ACTUAL EXAM PAPER
2026 QUESTIONS WITH ANSWERS
GRADED A+
◍ The objective of multiple linear regression is.
Answer: To predict future new responsesTo model the association of
explanatory variables to a response variable accounting for controlling
factors.To test hypothesis using statistical inference on the model.
◍ the least squares (in multiple linear regression).
Answer: the square differences between the observed responses yi and the
expected responses,
◍ plots to to evaluate normality.
Answer: the quantile normal plot and the histogram
◍ What to look in Tukey output?.
Answer: 1) First, you must look at the lower and upper bounds, and identify
the confidence intervals that include the zero values. If includes zero,
meaning that the means of those two groups could be possibly equal to
zero2) identifying the confidence levels that have only positive values or
only negative values
◍ confidence band.
Answer: If we take several values of x* and we construct such confidence
intervals, we get ___
◍ What is the dependent variable in the example provided?.
Answer: Hours of sleep.
◍ What should be included in the conclusion of the write-up?.
, Answer: Final conclusions and take-aways, answering the research question.
◍ What is the significance of the F-test in regression?.
Answer: It tests the overall significance of the regression model.
◍ How should categorical variables be interpreted?.
Answer: By creating separate columns for each category.
◍ N-k.
Answer: The degree of freedom in the estimation in the pooled variance
estimator
◍ What is the purpose of baseline regression?.
Answer: To include all variables in the model.
◍ What should the written report include?.
Answer: Research statement, data introduction, technical details, hypothesis,
big take-aways, and clear interpretations.
◍ What should you do if a row has more than 3 missing values?.
Answer: Delete that row.
◍ What is the ideal condition for the regression spreadsheet?.
Answer: Each row should be filled in for all variables.
◍ The alternative hypothesis is that at least one of the regression coefficients is
not equalto zero,.
Answer: meaning that at least one of the predictors included in the model
has predictivepower. This is called the test for overall regression because it
indicates whether the overall regression has any predictive or explanatory
power for the response variable
◍ What is the first step in the project data process?.
Answer: Clean Data.
◍ What is the role of residuals in regression analysis?.
Answer: To assess the fit of the model and check assumptions.
◍ Partial F-test.
, Answer: This is an F-test that involves a decomposition of the total
regression sum of squares, SSreg, into components related to each
independent variable. if we reject the null hypothesis, we conclude that
some or all Z variables add predictive or explanatory power to the model
already including the X variables.
◍ What should you do if less than 10 rows of a single column are empty?.
Answer: Fill them with the average of that column.
◍ What does it mean if the adjusted R-squared is larger after variable
elimination?.
Answer: The new model is better than the baseline model.
◍ SSE.
Answer: is the sum of square differences between the observations and the
individual sample means
◍ positive value for ß1.
Answer: a direct relationshipbetween the predicting variable x and the
response variable y
◍ the within-variability to the between variability of the response data..
Answer: in ANOVA, we compare
◍ What does a p-value indicate in regression analysis?.
Answer: The significance of the relationship between variables.
◍ What is variable elimination?.
Answer: The process of removing variables based on their significance
(p-values).
◍ modeling framework for the simple linear regression:.
Answer: 1) identifying data structure2) clearly stating the model
assumptions
◍ Testing hypotheses.
Answer: of association relationships
◍ What should you check for after cleaning data?.
PROJECT REPORT ACTUAL EXAM PAPER
2026 QUESTIONS WITH ANSWERS
GRADED A+
◍ The objective of multiple linear regression is.
Answer: To predict future new responsesTo model the association of
explanatory variables to a response variable accounting for controlling
factors.To test hypothesis using statistical inference on the model.
◍ the least squares (in multiple linear regression).
Answer: the square differences between the observed responses yi and the
expected responses,
◍ plots to to evaluate normality.
Answer: the quantile normal plot and the histogram
◍ What to look in Tukey output?.
Answer: 1) First, you must look at the lower and upper bounds, and identify
the confidence intervals that include the zero values. If includes zero,
meaning that the means of those two groups could be possibly equal to
zero2) identifying the confidence levels that have only positive values or
only negative values
◍ confidence band.
Answer: If we take several values of x* and we construct such confidence
intervals, we get ___
◍ What is the dependent variable in the example provided?.
Answer: Hours of sleep.
◍ What should be included in the conclusion of the write-up?.
, Answer: Final conclusions and take-aways, answering the research question.
◍ What is the significance of the F-test in regression?.
Answer: It tests the overall significance of the regression model.
◍ How should categorical variables be interpreted?.
Answer: By creating separate columns for each category.
◍ N-k.
Answer: The degree of freedom in the estimation in the pooled variance
estimator
◍ What is the purpose of baseline regression?.
Answer: To include all variables in the model.
◍ What should the written report include?.
Answer: Research statement, data introduction, technical details, hypothesis,
big take-aways, and clear interpretations.
◍ What should you do if a row has more than 3 missing values?.
Answer: Delete that row.
◍ What is the ideal condition for the regression spreadsheet?.
Answer: Each row should be filled in for all variables.
◍ The alternative hypothesis is that at least one of the regression coefficients is
not equalto zero,.
Answer: meaning that at least one of the predictors included in the model
has predictivepower. This is called the test for overall regression because it
indicates whether the overall regression has any predictive or explanatory
power for the response variable
◍ What is the first step in the project data process?.
Answer: Clean Data.
◍ What is the role of residuals in regression analysis?.
Answer: To assess the fit of the model and check assumptions.
◍ Partial F-test.
, Answer: This is an F-test that involves a decomposition of the total
regression sum of squares, SSreg, into components related to each
independent variable. if we reject the null hypothesis, we conclude that
some or all Z variables add predictive or explanatory power to the model
already including the X variables.
◍ What should you do if less than 10 rows of a single column are empty?.
Answer: Fill them with the average of that column.
◍ What does it mean if the adjusted R-squared is larger after variable
elimination?.
Answer: The new model is better than the baseline model.
◍ SSE.
Answer: is the sum of square differences between the observations and the
individual sample means
◍ positive value for ß1.
Answer: a direct relationshipbetween the predicting variable x and the
response variable y
◍ the within-variability to the between variability of the response data..
Answer: in ANOVA, we compare
◍ What does a p-value indicate in regression analysis?.
Answer: The significance of the relationship between variables.
◍ What is variable elimination?.
Answer: The process of removing variables based on their significance
(p-values).
◍ modeling framework for the simple linear regression:.
Answer: 1) identifying data structure2) clearly stating the model
assumptions
◍ Testing hypotheses.
Answer: of association relationships
◍ What should you check for after cleaning data?.
