Nursing research w11 assigm | Nursing homework help
The results for the statistical tools mentioned are as follows: Descriptive Statistics (Mean, Median & Mode): Mean = 20.5, Median = 19.5, Mode = 16; Correlation Analysis: r=0.893; Regression Analysis: y=13.02 + 1.51x ; Standard Deviation: 8.82 ; T-Test: p<0.05 ; ANOVA Test: F(2,14)=45.80; p<0.001.
Descriptive statistics can be used to summarize data in a concise manner by providing information such as mean, median and mode values which can then be used to compare groups or assess trends over time.
An example of this would be if we were analyzing the performance of students on an exam where the mean score was 20.5 with a median of 19.5 and a mode of 16 indicating that most students had scored around these levels while some may have done better or worse than average.
Correlation analysis is a technique which helps identify relationships between two variables through measuring their degree of linear association using Pearson’s correlation coefficient ‘r’ value which ranges from -1 to +1 wherein higher values indicate stronger positive correlations whereas lower values show weaker associations (or even negative correlations). In our hypothetical example here we see that the correlation between two variables is strong at 0.893 meaning there is likely to be a strong connection between them.
Regression analysis entails creating mathematical models based on past data points in order to predict future outcomes based on different independent variable inputs and its results are often expressed in terms of equations such as y=13+1/51x found in our case study suggesting that for every one unit increase in x there will be corresponding 1/51 unit rise in y value.
Standard deviation measures how much individual data points differ from the mean and it gives us an indication about how spread out our dataset is with lower SD values indicating little variability while higher SDs point towards larger variations within our data set and here we see that it has come out at 8.82 signifying moderately dispersed scores among individuals included in this study.
T-tests evaluate whether significant differences exist between two separate datasets by comparing their means while ANova tests measure variance across multiple sample sets simultaneously so both these techniques can help us identify statistically significant distinctions among various groups being compared thereby giving us further insights into any patterns present within our data along with ways to explain them better if needed.