Match the cell part with its corresponding part om the cell.

Arrange the steps in order

Compound eyes are found in which groups? Select all that apply

Accessory feeding appendages include all except:

Data Set Background  “Personal Financial Wellness Dataset” This dataset contains 1,000 individuals and is designed to study how demographic characteristics, income, expenses, debt, investments, and financial habits influence a person’s savings rate. Savings_Rate_Percent : Represents the percentage of income that an individual saves (Response Variable) Numerical (Continuous) Age:  Age of the individual in years (Numeric) Employment_Status :   Current employment situation of the individual. (Categorical) Education_Level:    Highest level of education completed. (Categorical) Marital_Status:    Current marital status of the individual. (Categorical) Housing_Type:    Type of housing arrangement the individual lives in. (Categorical) Annual_Income_USD:    Total yearly income earned in U.S. dollars. Monthly_Expenses_USD:    Average monthly spending on living and household expenses. Debt_Amount_USD:    Total outstanding debt owed by the individual. Investment_Amount_USD:    Total amount invested in financial assets and accounts. Credit_Score: Numerical measure of the individual’s creditworthiness. Financial_Literacy_Score: Score representing the individual’s financial knowledge and skills. Monthly_Discretionary_Spending_USD : Monthly spending on non-essential goods and services. Emergency_Fund_Months: Number of months of expenses covered by emergency savings.

Question 1 Exploratory Data Analysis (6 points) Use dataset “Personal_Financial_Wellness” for this question a) (2 points)i) (1 point) Which category of Employment_Status is most common, and what percentage of the dataset does it represent?ii) (1 point) Using scatterplot and correlation coefficient, does a higher Financial_Literacy_Score consistently correspond to a higher savings rate? b) (2 points)i) (1 point)How does average Financial_Literacy_Score vary across Employment_Status categories?ii) (1 point) Which combination of Employment_Status and Education_Level has the highest average income? c) (2 points)i) (1 point) After dividing individuals into income quartiles, which quartile has the highest median savings rate and the largest spread in savings?ii) (1 point) Using boxplots, which Housing_Type has the highest debt burden, and are debt levels more variable within some housing categories than others?

Question 5 Prediction (8 points) Use testData for this question a) (4 points) Using testData, predict the Savings_Rate_Percent with both model1 and model2.i) Show the predictions of both models along with the true values.ii) Calculate the mean squared prediction error (MSPE) of each model. Which model predicts better on the test data? b) (4 points) Consider a new individual who owns their home (Housing_Type = “Own”), has monthly expenses of 4000 USD and a credit score of 700. Using model1:i) Compute the 95% confidence interval for the mean response and the 95% prediction interval for this new individual.ii) Provide an interpretation of each interval in the context of the problem. Why is the prediction interval wider than the confidence interval?

Question 4 Inference on Coefficients and Baseline (6 points) Use trainData for this question a) (3 points) Calculate the 95% confidence intervals for all the coefficients of model2. Based on these intervals, which predictors are statistically significant at the 0.05 significance level? b) (3 points) In the pre-processing of the data, Employment_Status was converted with as.factor(). Explain, using both code and a description, how you can tell what the baseline category is and how you can change the baseline to “Unemployed”. Refit the full model with the new baseline. What changes in the model output and what stays the same?

Midterm Exam – Part 2 Instructions The R/Python Jupyter Notebook files include the questions , the empty code chunk sections for your code, and the text blocks for your responses. Answer the questions below by completing the R/Python Jupyter Notebook file. You may make slight adjustments to get the file to convert but otherwise keep the formatting the same.   Once you’ve finished answering the questions, submit your exam in pdf format, BOTH to Canvas and to Gradescope. Please use the Gradescope link for the submission. The Gradescope link expires after 10 minutes, so make sure you submit PDF to Gradescope within 10 minutes! Resubmission within the 10-minute window is allowed.  Please make your submission within the exam window as there are penalties for late submissions.   To maintain the integrity of this course: 1. Do not plagiarize (even if it is a particular question).  2. Do not use any AI tools such as chatGPT or CoPilot. Students violating the Honor Code will be reported to Georgia Tech’s Office of Student Integrity. Ready? Let’s begin…   Data Set: Personal_Financial_Wellness.csv Starter Templates: Summer2026_midterm_R_Starter_template.ipynb Summer2026_midterm_Python_Starter_template.ipynb

True or False: If the interest rate is greater than 0%, the present discounted value of receiving $1,000,000 per year for 10 years is less than receiving $10,000,000 today.