Sample Prompts for Descriptive Analysis of Data:

 

Sample Prompts for Descriptive Analysis of Data:

Central Tendency:

• What is the average (mean) value of [variable]? How does it compare to other relevant data sets or benchmarks?
• What is the median value of [variable]? Is it significantly different from the mean, indicating a possible skewed distribution?

Spread and Variability:

• What is the standard deviation of [variable]? Does it suggest high or low variability in the data?
• What is the range (difference between minimum and maximum values) of [variable]? Does it offer insights into the potential outliers or extreme values?

Frequency and Distribution:

• How are the data points distributed for [variable]? Is it symmetrical (normal distribution) or skewed towards specific values?
• What percentage of data points fall within one, two, or three standard deviations from the mean of [variable]?

Categorical Data:

• What is the frequency of occurrence for each category in [variable]? Are there any dominant categories or unusual patterns?
• Is there a relationship between [variable 1] and [variable 2]? Create a crosstabulation table to analyze the frequency of different combinations.

Time-Series Data:

• How does [variable] change over time? Are there any seasonal trends or cyclical patterns evident?
• Compare the values of [variable] across different time periods. Are there any significant increases, decreases, or fluctuations?

Specific Examples:

• Analyze the average customer age and purchase amount in a retail store. Are there any differences between weekdays and weekends?
• Describe the distribution of student exam scores in a class. Are there any outliers or underperforming groups requiring additional support?
• Investigate the frequency of different causes of hospital admissions. Are there any specific conditions requiring further public health interventions?

Remember: These prompts are starting points, and you can adapt them to the specific data set and research questions you are exploring. Remember to consider the data type (numerical vs. categorical) and choose analysis methods appropriate for each type.