Experimental Data Statistical Analysis -- 2
Budget: £20 – £250 GBP
I'm looking for a data analyst with expertise in handling experimental data. The data is currently in an Excel spreadsheet. You will need to conduct a regression analysis and T-tests on the data.
Ideal Skills:
- Proficient in statistical analysis
- Experienced with experimental data
- Excel spreadsheet expertise
I need to analyze the variables and assessed their association with stabilization times using statistical methods:
1. Data Presentation:
o Continuous Variables: Expressed as mean ± standard deviation (SD) or median
o Categorical Variables: Reported as frequency and percentages.
2. Statistical Tests:
o Categorical Variables: Differences assessed using Chi-squared or Fisher’s exact tests.
o Continuous Variables: Parametric data tested with independent sample t-tests; non-parametric data tested with Mann-Whitney U tests.
3. Risk Estimation:
o Odds ratios (OR) and 95% confidence intervals (CI) calculated to estimate prolonged stabilization time risk associated with various factors.
4. Predictor Analysis:
o Binary logistic regression identified significant predictors for prolonged stabilization time.
5. Outcome Stratification:
o Infants were stratified into two gestational-age groups: SBC, LNU and NICU
6. Significance Level:
o A p-value <0.05 was considered statistically significant
Ideal Skills:
- Proficient in statistical analysis
- Experienced with experimental data
- Excel spreadsheet expertise
I need to analyze the variables and assessed their association with stabilization times using statistical methods:
1. Data Presentation:
o Continuous Variables: Expressed as mean ± standard deviation (SD) or median
o Categorical Variables: Reported as frequency and percentages.
2. Statistical Tests:
o Categorical Variables: Differences assessed using Chi-squared or Fisher’s exact tests.
o Continuous Variables: Parametric data tested with independent sample t-tests; non-parametric data tested with Mann-Whitney U tests.
3. Risk Estimation:
o Odds ratios (OR) and 95% confidence intervals (CI) calculated to estimate prolonged stabilization time risk associated with various factors.
4. Predictor Analysis:
o Binary logistic regression identified significant predictors for prolonged stabilization time.
5. Outcome Stratification:
o Infants were stratified into two gestational-age groups: SBC, LNU and NICU
6. Significance Level:
o A p-value <0.05 was considered statistically significant
Related categories:
Data Entry
Statistics
Statistical Analysis
Linear Regression
Regression Analysis