Vizly: Differential and Associative Analysis with Data-Driven Recommendations

Vizly: AI-powered data analysis

Differential and Associative Analysis with Data-Driven Recommendations

MCOB Final Data (1).sav

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Differential analysis and a data-driven recommendation.

Associative analysis and a data-driven recommendation.

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The SPSS file has been successfully loaded, and the first few rows of the dataset have been displayed above. The dataset contains a variety of columns, including StartDate, EndDate, Progress, Duration__in_seconds_, and many others related to survey responses.

To perform a differential analysis, we can compare different groups within the dataset to identify significant differences. For example, we could compare the average Progress or Duration between different ethnic groups or year levels.

For an associative analysis, we can explore relationships between variables, such as the correlation between Progress and Duration__in_seconds_, or how different survey responses are associated with each other.

Let's start by performing these analyses. Please specify which variables or groups you would like to focus on for the differential analysis, and any specific relationships you are interested in for the associative analysis.

do differential analysis on cost variable and campus environment variable

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The dataset contains a large number of columns, but the specific columns related to "Cost" and "Campus Environment" are not immediately clear from the column names.

Could you please specify the exact column names or provide more details about how these variables are represented in the dataset? This will help in performing the differential analysis accurately.

do differential analysis on Q14_3 and Q14_9

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The differential analysis between the Q14_3 (Cost) and Q14_9 (Campus Environment) variables has been conducted using a t-test. Here are the results:

Mean of Cost (Q14_3): 3.67
Mean of Campus Environment (Q14_9): 3.30
T-statistic: 3.87
P-value: 0.00012

Interpretation:

The p-value is less than 0.05, indicating that there is a statistically significant difference between the means of the two groups.
The mean score for Cost is higher than that for Campus Environment, suggesting that respondents rated Cost higher on average.

Recommendation:

Given the significant difference, it might be beneficial to investigate further into why Cost is rated higher. Understanding the specific aspects of Cost that are valued by respondents could help in enhancing the Campus Environment to match or exceed these expectations.

Would you like to proceed with an associative analysis or explore any other aspect of the data?

do associative analysis on Q14_4 and Q14_14

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The associative analysis between Q14_4 and Q14_14 has been conducted using Pearson's correlation coefficient. Here are the results:

Correlation Coefficient: 0.225
P-value: 0.0003

Interpretation:

The correlation coefficient of 0.225 suggests a weak positive correlation between the two variables. This means that as the value of one variable increases, the other tends to increase slightly as well.
The p-value is less than 0.05, indicating that the correlation is statistically significant.

Recommendation:

Given the weak positive correlation, it might be beneficial to explore if there are underlying factors that influence both variables similarly. Further investigation into these factors could provide insights into improving both aspects simultaneously.

If you have any more questions or need further analysis, feel free to ask!