In relation to Business Intelligent, give practical examples of Statistical analysis techniques used in quantitative research
1. Regression analysis: This technique is used to examine the relationships between multiple variables by estimating the mathematical equation that best predicts the outcome variable based on the predictor variables. For example, a retail company may use regression analysis to determine how price, promotion, and customer demographics impact sales.
2. t-tests: A t-test is used to compare the means of two groups to assess if they are significantly different from each other. It is commonly used when comparing the effectiveness of two different marketing campaigns or when testing the impact of a new product version on customer satisfaction.
3. Chi-square test: This test is used to determine if there is a significant association or relationship between two categorical variables. For instance, a car manufacturer might use a chi-square test to study if customer preferences for car colors are influenced by gender.
4. ANOVA (Analysis of Variance): ANOVA is used to analyze the differences in means between two or more groups. It helps in determining if there are significant differences among group means. A clothing retailer might use ANOVA to examine if there are differences in customer satisfaction scores across various store locations.
5. Time series analysis: This technique is used to analyze data that is collected over time to identify patterns, trends, and forecast future values. For example, a financial institution might use time series analysis to predict future stock prices based on historical price patterns and other relevant factors.
6. Cluster analysis: Cluster analysis is used to identify groups or clusters of similar items within a large dataset. This technique can be used by an e-commerce company to segment their customer base into distinct groups based on purchasing behavior, demographics, or other variables.
7. Factor analysis: Factor analysis is employed to identify underlying factors or dimensions within a large set of correlated variables. It helps in understanding the interrelationships among variables. An educational institution may use factor analysis to identify the key factors influencing student performance based on various test scores, attendance, and other related variables.
These statistical analysis techniques provide businesses with valuable insights to make informed decisions, optimize processes, and achieve strategic objectives.