Show code

Show code

Show code

Show code

Show code

The data from the 'practical 9 table' sheet, after skipping initial non-relevant rows, reveals a table related to the use of Facebook and students' academic performance. The table includes variables such as 'N' (sample size), 'df' (degrees of freedom), 'X2-Crit.' (Chi-square critical value), 'X2-Cal.' (Chi-square calculated value), and 'Decision' on the hypothesis. Here are the key points:

- The sample size (N) is 350.
- The degrees of freedom (df) for the analysis is 57.
- The critical value for Chi-square (X2-Crit.) is 7.81.
- The calculated Chi-square value (X2-Cal.) is 265.01.
- The decision based on the analysis is to reject the hypothesis.

This suggests a significant effect of Facebook use on students' academic performance, as indicated by the rejection of the null hypothesis (Ho1) that posits no significant effect.

Show code

The distribution of values in the 'practical 9 table' data, specifically for the Chi-square analysis of Facebook use on students' academic performance, was visualized. The critical Chi-square value (X2-Crit.) was 7.81, and the calculated Chi-square value (X2-Cal.) was significantly higher at 265.01. This visualization clearly illustrates the substantial difference between the critical and calculated values, supporting the decision to reject the null hypothesis.

To explore the relationship between variables in the 'practical 9 table' data, we'll need to identify the variables present and their types. Then, we can perform statistical analyses or create visualizations like scatter plots or correlation matrices to understand how these variables interact with each other. Let's start by examining the available variables in more detail.

Show code

AI-powered data analysis