Identifying and addressing missing values

How can you ensure that missing data is handled correctly with the use of Copilot in Excel's features? (Select all that apply.)

Analyze patterns in missing values and apply formulas suggested by Copilot to fill in gaps without introducing errors. Apply conditional formatting to visually inspect missing data, then use Copilot to evaluate and suggest further actions based on the dataset. Use copilot to suggest actions for each type of missing data and decide whether to apply those actions based on the dataset's purpose.

How can you use Copilot to handle duplicate data in a dataset? (Select all that apply.)

Ask Copilot to recommend whether to merge or delete rows depending on the context of the duplicated data. Use Copilot to highlight duplicate rows in the dataset based on specific columns like Subscriber ID. Use Copilot to guide you through merging rows where different information is present in duplicates to create a single, complete record

True or false, it's always better to delete duplicate rows rather than merge them when using Copilot to clean a dataset.

False: It's not always better to delete duplicates. In some cases, merging rows with different but valuable information ensures that no important data is lost. Merging creates a more complete record while maintaining the integrity of the dataset.

After applying conditional formatting to highlight missing data in your dataset, you should always remove rows with missing values to ensure accuracy in analysis.

False: Removing rows with missing data isn't always the best approach. Depending on the dataset, it may be more appropriate to impute values or apply formulas to fill in these gaps. Copilot in Excel can help you decide the best course of action.

You are working with an inventory dataset in Excel and notice some important data is missing, such as quantities and unit prices for certain items. What is the first step when using Copilot in Excel to help you identify these gaps?

Highlight cells with missing data using conditional formatting*

Why would you use trend imputation to fill in missing temperature values in a dataset?

The dataset shows a steady increase in temperature over time, and you want to estimate missing values based on this trend

Why would you choose to merge duplicate rows rather than delete them using Copilot in Excel?

The duplicated rows have different subscription dates for the same person, and you want to keep track of their history

You are analyzing a sales dataset for a retail store, tracking daily sales over several weeks, but some sales data is missing for a few random days. Why would you choose to use neighboring row imputation to fill in the missing sales data?

The sales data is missing for a few random days, and you want to estimate the missing values by averaging the sales from the surrounding days.

Your customer email list for a marketing campaign has several duplicate email entries in the dataset. You decide to use Copilot in Excel to delete all duplicate email entries, keeping only the first occurrence of each email. True or false, this is the best approach to handle duplicate email entries in your dataset.

True: Deleting duplicate entries and keeping only the first occurrence is a standard approach to handling duplicate data in email lists. This ensures that each customer only receives one communication, avoiding issues like spam or email blocking. Copilot can help you easily identify and remove these duplicates.

Your dataset tracks daily sales figures for a retail store. Several sales values are missing for random days. You decide to use Copilot in Excel to apply a moving average imputation to fill in the missing sales values based on surrounding data points. True or false, using a moving average imputation is an appropriate method for handling these missing sales values.

True: Moving average imputation is a suitable method when the data shows some degree of variability, as it smooths out fluctuations and fills in missing values based on surrounding data points. Copilot can assist in applying this method effectively.

True or false, trend imputation is the best method to use when the data follows a clear upward or downward pattern, such as increasing temperatures over time.

True: Trend imputation is particularly effective when the data shows a clear pattern, such as an upward or downward trend over time. It helps estimate missing values that are in line with the overall data trend.

You are working with a sales dataset for an online store and notice several Order Quantity entries are missing. You decide to use Copilot in Excel to fill in the missing quantities by calculating the average of the available quantities for similar orders. True or false, this is an appropriate method to handle missing Order Quantity entries in your dataset.

True: Using the average of available data from similar orders is a reasonable approach for estimating missing order quantities when there are no clear patterns or trends. Copilot can assist in identifying similar data points and applying imputation methods like averaging.

You are preparing a dataset for analysis, but there are numerous missing values in critical columns like Quantity on Hand and Unit Price. Using Copilot in Excel, what are the most effective strategies to ensure the dataset is complete and ready for accurate analysis? (Select all that apply)

Use conditional formatting to visually assess the scope of missing data before determining the next steps. Apply automated formulas with Copilot to ensure consistency and accuracy when filling in missing values, helping to streamline the data preparation process and ensuring the dataset is ready for analysis Evaluate Copilot's suggested actions for handling missing data and choose the most appropriate solution.

How can you use Copilot in Excel to handle missing data in a weather dataset where some temperature and visitor values are missing? (Select all that apply.)

Use copilot to suggest the best imputation method for each column, such as sing trend imputation for the temperature data. Ask copilot to calculate missing visitor data by averaging the neighboring values from the rows directly above and below. Use copilot to recommend methods like linear regression or interpolation to estimate missing data based on surrounding trends.

Which one of the following would be a good reason to use Copilot in Excel's "Suggest actions for missing data" feature when handling incomplete datasets?

You're unsure whether to remove rows with missing values in the Quantity on Hand column or impute these missing values, so you need guidance from Copilot