Regression analysis refers to the set of statistical methods that are applied in the estimation of the dependent variable and one or more independent variables. Regression analysis can be applied to assess the strength of the correlation between variables and for modeling the future relationship that may be expected between independent and dependent variables. In regression analysis, there exist several variations such as multiple linear, linear, as well as nonlinear. Some of the most common models are multiple linear and simple linear (Kumari & Yadav, 2018). Non-linear regression analysis is usually applied for complicated data sets where the independent and dependent variables indicate a nonlinear relationship (Aggarwal & Ranganathan, 2017). There are numerous applications of regression analysis, including research processes as well as financial analysis. The purpose of this assignment is to predict an outcome using regression models through the application of the dataset given.
Before conducting regression analysis, it is necessary to understand the assumptions. One of the assumptions is that the independent variable is not always random. Some other assumptions include the value of residuals is zero, the independent and dependent variables often show a linear relationship between the intercept and the slope, the value of residual is always constant across all the observations made; finally, the values of residual are not always correlated across different observations (Montgomery et al., 2021). Besides, the residual values often follow the normal distribution.
Regression analysis
From the information given, the dependent variable is hospital costs, while the independent variables include patient age, risk factors, and patient satisfaction scores. Both the independent and dependent variables are continuous.
| Table 1: Descriptive Statistics | |||
| Mean | Std. Deviation | N | |
| Cost
|
14906.51 | 2614.346 | 185 |
| Age
|
73.25 | 6.430 | 185 |
| risk | 5.69 | 2.777 | 185 |
| satisfaction | 50.02 | 28.919 | 185 |
Table 1 indicates the descriptive statistics for both the dependent and independent variables. The means of variables, cost, age, risk, and satisfaction include $14906.51, 73.25 years, 5.69, and 50.02. The sample size used was 185.
| Table 2: Correlations | |||||
| cost | age | risk | satisfaction | ||
| Pearson Correlation | cost | 1.000 | .279 | .199 | -.071 |
| age | .279 | 1.000 | .152 | .094 | |
| risk | .199 | .152 | 1.000 | .037 | |
| satisfaction | -.071 | .094 | .037 | 1.000 | |
| Sig. (1-tailed) | cost | . | .000 | .003 | .169 |
| age | .000 | . | .019 | .101 | |
| risk | .003 | .019 | . | .307 | |
| satisfaction | .169 | .101 | .307 | . | |
| N | cost | 185 | 185 | 185 | 185 |
| age | 185 | 185 | 185 | 185 | |
| risk | 185 | 185 | 185 | 185 | |
| satisfaction | 185 | 185 | 185 | 185 | |
Table 2 shows the correlation between dependent and independent variables. The outcomes show that there is a weak positive correlat
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