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Occupation's Impact on Willingness to Purchase from ICS: A Statistical Analysis

August 24, 2023
Allison Burgers
Allison Burgers
🇬🇧 United Kingdom
Statistical Analysis
Allison Burgers a seasoned Statistical Analysis assignment expert, holds a Master's degree from the University of Southampton. Her expertise extends to exploring the limitations of statistical analysis, and many more.
Key Topics
  • Problem Description
  • Conclusion

In the study, we delve into the intriguing relationship between an individual's occupation and their inclination to buy from ICS. Our comprehensive analysis, featuring well-structured hypothesis testing and chi-square tests, reveals a profound connection between these variables. The findings suggest that one's professional standing significantly influences their purchasing decisions, offering valuable insights for marketers and businesses to tailor strategies and engage their target audiences effectively. This investigation underscores the intricate interplay between occupation and consumer behavior in the dynamic market landscape.

Problem Description

The statistical analysis assignment at hand explores the relationship between individuals' occupations and their willingness to purchase from ICS. It seeks to determine whether these two variables are independent or if there is a significant association between them. Through rigorous hypothesis testing and chi-square analysis, the assignment provides compelling evidence that occupation and purchase intent from ICS are intricately linked, shedding light on valuable insights for marketing and business strategies.

Solution:

Part-a:Hypothesis Testing

  • Null Hypothesis (H0): Occupation (OCCUP) and willingness to purchase from ICS (PURPROB) are independent of each other.
  • Alternate Hypothesis (H1): Occupation (OCCUP) and willingness to purchase from ICS (PURPROB) are not independent of each other.

Part-b: Statistical Test We conducted a chi-square test with 2 degrees of freedom (df=2), and the results were as follows:

  • The p-value of the chi-square test is 0.000, which is less than the significance level of 0.05.
  • We have found that occupation and willingness to purchase from ICS are statistically related at the 0.05 significance level.

Part-c:Chi-Square Test Details

  • Pearson Chi-Square: The Pearson Chi-Square statistic is 19.477 with 2 degrees of freedom, and the p-value is 0.000, indicating a strong statistical association between the variables.
  • Likelihood Ratio: The Likelihood Ratio statistic is 20.869 with 2 degrees of freedom, and the p-value is 0.000, reinforcing the strong association between the variables.
  • Linear-by-Linear Association: The Linear-by-Linear Association statistic is 19.096 with 1 degree of freedom, and the p-value is 0.000, further supporting the relationship between the variables.
  • The total number of valid cases is 100.

Crosstabulation: Occupation vs. Purchase from ICS

The crosstabulation provides a clear breakdown of the relationship between occupation and the likelihood of purchasing from ICS.

OccupationPurchase from ICS?UnlikelySomewhat likelyVery likelyTotal
Junior LevelCount2120344
Expected Count12.322.09.744.0
Senior LevelCount7301956
Expected Count15.728.012.356.0
TotalCount285022100
Expected Count28.050.022.0100.0

Table 1:Observed counts and expected counts for each occupation and willingness to purchase from ICS

  • The "Expected Count" refers to the expected number of individuals falling into each category if there were no association between the two variables.
  • The observed counts indicate the actual number of individuals in each category.

The significant difference between the observed and expected counts in the crosstabulation further supports the rejection of the null hypothesis, demonstrating a strong relationship between occupation and the willingness to purchase from ICS.

Conclusion

Our in-depth analysis has shed light on the undeniable link between occupation and consumers' willingness to purchase from ICS. The rejection of the null hypothesis in our statistical examination has provided compelling evidence that occupation plays a pivotal role in shaping purchasing decisions. This newfound understanding holds immense potential for businesses and marketers to craft tailored strategies that resonate with various professional demographics. As the market landscape evolves, recognizing the intricate interplay between occupation and consumer behavior becomes an essential facet of effective marketing and business growth.

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