Data integrity refers to maintaining the accuracy and consistency of data in a database.

Suppose we run the following linear regression model: Sales = b0 + b1*AdvertisingSpend + b2*StoreSize, where AdvertisingSpend is a store’s monthly advertising spending and StoreSize is measured in square feet. We find that b1​ is positive (stores that spend more on advertising tend to have higher sales) and b2​ is positive (larger stores tend to have higher sales). Assume that AdvertisingSpend and StoreSize are positively correlated (larger stores tend to spend more on advertising). If StoreSize were omitted from the model and only AdvertisingSpend were included, would you expect the estimated coefficient on AdvertisingSpend to be larger or smaller than the b1​ estimated when StoreSize is included? Explain briefly. Note: Use your understanding of regression analysis to answer this question. When answering, be sure to first state whether you expect the coefficient to become larger or smaller, and then explain why in your own words.

At Reboot and Pray, the average time to resolve a customer’s issue is 36 minutes, with a standard deviation of 8 minutes. What resolution time (in minutes) corresponds to a z-score of -1.5? Round your answer to the nearest integer if necessary.

Which of the following datasets are examples of cross-sectional data? Select all that apply.

Use the Probit sheet. An online retailer aims to predict whether a customer will make a repeat purchase using a probit regression model based on customer data. As predictors, the model considers customer age (in years), annual spending (in hundreds of dollars), loyalty program membership (1 if a member, 0 otherwise), and customer tenure (measured by the number of years the customer has shopped with the retailer). The Probit sheet contains the descriptive statistics and probit regression results. What is the marginal effect of “customer tenure” at the mean? Report as a percent without the % sign, four decimal places.

Use the Logit sheet. An online retailer aims to predict whether a customer will make a repeat purchase using a logistic regression model based on customer data. As predictors, the model considers customer age (in years), annual spending (in hundreds of dollars), loyalty program membership (1 if a member, 0 otherwise), and customer tenure (measured by the number of years the customer has shopped with the retailer). The Logit sheet contains the descriptive statistics and logit regression results. What is the marginal effect of “age” at the mean? Report as a percent without the % sign, four decimal places.

Use the GuestSpending sheet. Develop a multiple linear regression model to predict total guest spending based on length of stay and room type. Use Standard as the baseline room type. What is the estimated guest spending for a guest who stays 2 nights in a Deluxe room? Please round your answer to the nearest integer.

At Late But Great Logistics, package delivery times average 48 hours with a standard deviation of 6 hours. Delivery times follow a normal distribution. Using the empirical rule, what percentage of deliveries take between 36 hours and 66 hours? Please answer in one decimal place without the % mark.

Given the following HTML, write a CSS selector to select the yellow highlighted elements. 1 2 3 4 5 6 7 8 Your selector should select all yellow highlighted elements and no additional elements on the page. Write just the selector so that your answer could be copy-pasted into the following code block replacing the green highlighted comment and the code would be valid: /* your selector */ { /* styles here … */}

Given the following HTML, write a CSS selector to select the yellow highlighted elements. 1 2 3 4 5 6 7 89 First Title Second Title Last Paragraph Third Title Your selector should select all yellow highlighted elements and no additional elements on the page. Write just the selector so that your answer could be copy-pasted into the following code block replacing the green highlighted comment and the code would be valid: /* your selector */ { /* styles here … */}