Statistical Analysis of Expected Counts in Chi-Square Test

Oct 29, 2024, 12:46 AM

264 cells have expected count less than 5.the minimum expected count is 11

It seems like you're dealing with a statistical analysis issue, possibly related to a chi-square test or another test involving expected counts.
In statistical tests like the chi-square test, having expected counts less than 5 can be problematic because it may violate the assumptions of the test, leading to inaccurate results.
If the minimum expected count is 11, it means that at least one of your categories or cells has an expected frequency of 11, which is above the threshold of 5. However, having 264 cells with expected counts less than 5 suggests that a significant portion of your data might not meet the test's assumptions.
To address this, you might consider:
  • Combining categories: If possible, combine some categories to increase the expected counts.
  • Using a different test: Consider using a test that does not have the same assumptions, such as Fisher's exact test for smaller sample sizes.
  • Collecting more data: If feasible, increasing the sample size can help meet the assumptions.
If you have more specific details or data, feel free to share, and I can provide more tailored advice or analysis.

Can you help me analyze my data for statistical tests?

Of course! I'd be happy to help you analyze your data for statistical tests.
To get started, could you please provide the dataset you're working with? If you have any specific tests in mind or particular questions you want to address with the data, let me know so I can assist you more effectively.

1. Seorang peneliti ingin mengkaji hubungan ukuran besar luka yang dinyatakan dalam lama hari rawat, diperoleh data sebagai berikut:

No Besar Luka Lama Rawat
1 3,8 8
2 5,2 10
3 10,6 6
4 6,3 7
5 10,1 8
6 7,6 10
7 8,2 10
8 9,2 11
9 5,5 12
10 7,5 13
11 12 13
12 7,7 10
13 8,3 9
14 5,4 8
15 4,3 8
16 8 7
17 9,5 15
18 3,6 5
19 4,4 9
20 4,8 9
21 5,6 10
22 8,9 12
23 11 15
24 4,5 2
25 7,6 5
Analisa data menggunakan SPSS, sertakan bukti Langkah-langkah SPSS dan analisih hasil uji tersebut!
To analyze the relationship between the size of the wound and the length of hospital stay, we can perform a correlation analysis. Since you mentioned using SPSS, I'll guide you through the steps to perform this analysis in SPSS and interpret the results.

Steps to Perform Correlation Analysis in SPSS:

  1. Enter the Data:
    • Open SPSS and create a new data file.
    • Enter the data for "Besar Luka" (Wound Size) and "Lama Rawat" (Length of Stay) in two separate columns.
  2. Perform Correlation Analysis:
    • Go to the menu bar and click on Analyze > Correlate > Bivariate.
    • Move "Besar Luka" and "Lama Rawat" into the Variables box.
    • Choose the correlation coefficient you want to use (e.g., Pearson).
    • Click OK to run the analysis.
  3. Interpret the Results:
    • SPSS will output a correlation matrix. Look for the Pearson correlation coefficient (r) between "Besar Luka" and "Lama Rawat".
    • The value of r ranges from -1 to 1. A value closer to 1 or -1 indicates a strong relationship, while a value closer to 0 indicates a weak relationship.
    • Check the significance (p-value) to determine if the correlation is statistically significant (commonly, p < 0.05 is considered significant).

Analysis of Results:

  • Positive Correlation: If r is positive, it indicates that as the wound size increases, the length of stay tends to increase.
  • Negative Correlation: If r is negative, it indicates that as the wound size increases, the length of stay tends to decrease.
  • No Correlation: If r is close to 0, it indicates no linear relationship between the variables.
If you have any specific questions about the results or need further assistance, feel free to ask!
The Pearson correlation coefficient between Besar Luka (Wound Size) and Lama Rawat (Length of Stay) is approximately 0.479. This indicates a moderate positive correlation, suggesting that as the wound size increases, the length of stay tends to increase as well.
The p-value for this correlation is approximately 0.015, which is less than the common significance level of 0.05. This means the correlation is statistically significant, indicating that the relationship between wound size and length of stay is unlikely to be due to random chance.
If you have any more questions or need further analysis, feel free to ask!

UAS Biostatistika Procyion.doc

Missing file

2. Buatlah Analisa dari hasil SPSS berikut ini

It seems like you've provided a file named "UAS Biostatistika Procyion.doc". However, the content appears to be binary or encoded, which makes it difficult to interpret directly here.
To analyze the results from SPSS, you would typically look for tables or outputs related to the statistical tests you performed, such as correlation coefficients, p-values, ANOVA tables, regression outputs, etc.
If you can provide a summary or screenshot of the SPSS output, I can help interpret the results for you. Alternatively, if you can convert the document to a more accessible format (like PDF or text), that would also help in providing a detailed analysis.
Could you provide more information or a different format of the SPSS output?
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