Biostatistics 5 QA Exams Training PDF

**Biostatistics 5 QA Exams Training** 1\. What does GPT stand for in ChatGPT? A\) Generative Pre-trained Transformer B\) Generalized Processing Tool\ C) Graphical Programming Technique D\) Generalized Prompt Translator 2\. Which library is commonly used to deploy ChatGPT models in Python? A\)...

**Biostatistics 5 QA Exams Training** 1\. What does GPT stand for in ChatGPT? A\) Generative Pre-trained Transformer B\) Generalized Processing Tool\ C) Graphical Programming Technique D\) Generalized Prompt Translator 2\. Which library is commonly used to deploy ChatGPT models in Python? A\) TensorFlow B\) OpenAI API C\) PyTorch\ D) NumPy 3\. The assumption of linearity ensures: A\) Predictor and response variables have a linear relationship B\) Residuals are normally distributed\ C) Predictors are independent of one another\ D) Variance is constant across observations 4\. How does ChatGPT assist in summarizing biostatistics papers? A\) It automatically validates statistical results\ B) It creates concise summaries based on provided abstracts\ C) It extracts graphs and charts from the paper\ D) It runs the code directly for analysis 5\. What type of errors are common when using ChatGPT for generating statistical code? A\) Syntax errors\ B) Contextual misunderstandings C\) Hardware dependencies\ D) Mathematical calculation errors 6\. What is a key ethical consideration when using ChatGPT in academia? A\) Avoiding sensitive prompts\ B) Properly acknowledging AI assistance\ C) Limiting the use of advanced models\ D) Testing generated code for performance 7\. What does a well-written ChatGPT prompt achieve? A\) Reduces model overfitting\ B) Avoids errors and vague responses\ C) Optimizes neural network performance D\) Guarantees accurate citations 8\. The Durbin-Watson test is used to check for autocorrelation in the residuals of a regression model. A\) A value around 2 indicates no autocorrelation B\) A value closer to 0 suggests negative autocorrelation\ C) A value closer to 4 indicates positive autocorrelation D\) It evaluates homoscedasticity 9\. What is the main advantage of using ChatGPT for coding tasks? A\) It generates error-free code B\) It provides rapid prototyping and suggestions C\) It eliminates the need for human expertise D\) It guarantees performance optimization 10\. Which task is ChatGPT least effective at? A\) Debugging simple R code\ B) Designing complete biostatistical studies C\) Summarizing lengthy texts\ D) Generating sample datasets 11\. How can you validate code generated by ChatGPT? A\) Run it as-is without testing\ B) Cross-check against documentation and test in software C\) Assume it's error-free if it runs\ D) Use the ChatGPT debugging mode 12\. What is the main difference between GPT and BERT? A\) GPT is generative, while BERT is analytical B\) GPT focuses on coding, while BERT focuses on text generation C\) GPT is bidirectional, while BERT is unidirectional D\) GPT is for summarization, while BERT is for classification 13\. What does the term \'prompt engineering\' refer to? A\) Adjusting GPT\'s internal algorithms\ B) Designing effective input questions to guide AI responses C\) Training GPT models from scratch\ D) Debugging ChatGPT for advanced tasks 14\. Why are iterative prompts recommended when using ChatGPT? A\) They improve tokenization speed B\) They refine and focus AI-generated outputs C\) They train the model faster D\) They automatically summarize large datasets 15\. What type of language does ChatGPT-generated text often lack? A\) Informal language\ B) Domain-specific terminology C\) Contextual relevance\ D) Nuanced critical arguments 16\. What should be avoided when summarizing a paper using ChatGPT? A\) Providing clear instructions\ B) Asking for focus on methods and results\ C) Assuming the summary is 100% accurate\ D) Comparing ChatGPT outputs with the original text 17\. What role can ChatGPT play in experimental design? A\) Developing complete experimental protocols B\) Suggesting ideas for study designs\ C) Verifying statistical assumptions automatically D\) Avoiding human review of the design 18\. Dunnett's test is used to compare: A\) Multiple treatments to each other\ B) Multiple treatments to a single control group\ C) Treatment groups based on non-parametric criteria D\) Mean variances across groups 19\. The role of a covariate in ANCOVA is to: A\) Adjust for categorical group effects B\) Control for variability in a continuous variable C\) Replace independent predictors in the model D\) Test for interaction effects 20\. The assumption of homogeneity of regression slopes in ANCOVA ensures: A\) Identical mean values across groups B\) Consistent relationships between the covariate and dependent variable across groups C\) Normality of residuals across different treatment groups D\) Independence of observations 21\. Cross-validation is useful because it: A\) Splits data to evaluate model generalizability\ B) Reduces bias by using fewer predictors\ C) Tests residuals for normality and homoscedasticity D\) Simplifies model comparison 22\. LASSO regression differs from traditional regression by: A\) Increasing coefficients to improve accuracy B\) Shrinking coefficients to zero for feature selection C\) Using quadratic penalties to avoid multicollinearity D\) Transforming predictors into uncorrelated components 23\. When comparing regression models, a lower AIC value: A\) Indicates a more complex but less accurate model B\) Suggests the model is overfitting\ C) Balances goodness-of-fit and model complexity D\) Shows independence of residuals 24\. Random effects in mixed-effects models account for: A\) Fixed treatment-level effects\ B) Variability specific to individual subjects or clusters C\) Residual errors unrelated to predictors\ D) Interaction effects among predictors 25\. PCR is advantageous in datasets with high multicollinearity because it: A\) Uses uncorrelated principal components as predictors B\) Identifies irrelevant variables and eliminates them C\) Reduces overfitting by increasing residual variance D\) Balances variance and bias optimally 26\. Scheffé's method is ideal for: A\) Comparing treatment means to a control\ B) Exploring all possible contrasts in group means\ C) Testing for interaction effects in regression models D\) Analyzing within-group variability 27\. Homoscedasticity in regression refers to: A\) The absence of autocorrelation in residuals B\) Constant variance of residuals across predictor levels C\) The linearity between predictors and response variables D\) Normality of residuals 28\. Independence of residuals is critical because: A\) Dependent residuals can inflate Type I errors\ B) It ensures multicollinearity is minimized\ C) It guarantees constant variance across observations D\) It eliminates outliers 29\. Q-Q plots help evaluate: A\) Homoscedasticity of residuals B\) Normality of residuals\ C) Independence of predictors\ D) Correlation among predictors 30\. The Durbin-Watson test assesses: A\) Multicollinearity among predictors\ B) Autocorrelation in residuals\ C) Goodness-of-fit for linear regression models D\) Variance of predictors 31\. Multicollinearity affects regression models by: A\) Inflating standard errors of coefficients B\) Increasing residual variance\ C) Improving model generalizability\ D) Enhancing predictor independence 32\. VIF values indicate multicollinearity when: A\) VIF = 1 suggests correlation among predictors B\) VIF \> 5 suggests no correlation\ C) VIF \> 10 suggests high multicollinearity\ D) VIF \< 1 suggests perfect independence 33\. Residual plots are used to detect: A\) Patterns indicating non-linearity or heteroscedasticity B\) Independence among residuals C\) Correlations between predictors D\) Normality of residuals 34\. Outliers can be identified by: A\) Large residual values in a residual plot\ B) High correlation coefficients in predictor analysis C\) Low p-values in coefficient estimates\ D) High VIF values 35\. A regression model\'s R-squared value measures: A\) The percentage of variance explained by the model B\) The significance of coefficients\ C) The independence of predictors\ D) The normality of residuals Answer : 1\. A 2\. B 3\. A 4\. B 5\. B 6\. B 7\. B 8\. A 9\. B 10\. B 11\. B 12\. A 13\. B 14\. B 15\. D 16\. C 17\. B 18\. B 19\. B 20\. B 21\. A 22\. B 23\. C 24\. B 25\. A 26\. B 27\. B 28\. A 29\. B 30\. B 31\. A 32\. C 33\. A 34\. A 35\. A