Statistics for Data Science and Business Analysis - Practical Example

The video tutorial explores a real estate database from a company in California, focusing on statistical properties and data analysis. It covers data types, levels of measurement, and variable analysis, including age, price, and gender. The tutorial demonstrates data visualization techniques using charts and graphs to identify trends and insights. It concludes with a summary of findings, emphasizing the importance of understanding data types and relationships for effective marketing strategies.

What is the primary goal of analyzing the real estate database?

• To expand the real estate company to other states

• To reduce the cost of properties

• To identify potential buyers for targeted marketing

• To increase the number of properties listed

How is the ID variable in the dataset classified?

• Numerical and ratio

• Categorical and ordinal

• Categorical and nominal

• Numerical and ordinal

What is the level of measurement for the 'age' variable?

• Qualitative and ordinal

• Quantitative and ratio

• Quantitative and ordinal

• Qualitative and nominal

Which chart is preferred for representing the 'state' variable?

• Histogram

• Pareto diagram

• Scatter plot

• Line chart

What age group accounts for the majority of real estate purchases?

• 46 to 55 years

• 36 to 45 years

• 26 to 35 years

• 18 to 25 years

What is the mean age of customers who purchase real estate?

• 36.15 years

• 56.15 years

• 66.15 years

• 46.15 years

What is the correlation between age and real estate expenditure?

• No correlation

• Strong positive correlation

• Strong negative correlation

• Weak negative correlation

Which demographic is more likely to sign real estate contracts?

• Young adults

• Corporate clients

• Males

• Females

Which state contributes the most to the company's sales?

• Arizona

• Nevada

• Oregon

• California

What percentage of sales are made by customers aged 26 to 55?

• 71%

• 81%

• 61%

• 51%