Find the correlation coefficient: r=r= Round to 2 decimal places. The null and alternative hypotheses for correlation are: H0:H0: ? ρ r μ == 0 H1:H1: ? r ρ μ ≠≠ 0 The p-value is: (Round to four decimal places) Use a level of significance of α=0.05α=0.05 to state the conclusion of the hypothesis test in the context of the study.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
A study was done to look at the relationship between number of vacation days employees take each year and the number of sick days they take each year. The results of the survey are shown below.
Vacation Days | 12 | 10 | 13 | 12 | 8 | 6 | 3 | 11 |
---|---|---|---|---|---|---|---|---|
Sick Days | 0 | 0 | 0 | 0 | -0 | 3 | 8 | 0 |
- Find the
correlation coefficient : r=r= Round to 2 decimal places. - The null and alternative hypotheses for correlation are:
H0:H0: ? ρ r μ == 0
H1:H1: ? r ρ μ ≠≠ 0
The p-value is: (Round to four decimal places) - Use a level of significance of α=0.05α=0.05 to state the conclusion of the hypothesis test in the context of the study.
- There is statistically significant evidence to conclude that an employee who takes more vacation days will take more sick days than an employee who takes fewer vacation days.
- There is statistically insignificant evidence to conclude that there is a correlation between the number of vacation days taken and the number of sick days taken. Thus, the use of the regression line is not appropriate.
- There is statistically significant evidence to conclude that an employee who takes more vacation days will take fewer sick days than an employee who takes fewer vacation days .
- There is statistically significant evidence to conclude that there is a correlation between the number of vacation days taken and the number of sick days taken. Thus, the regression line is useful.
- r2r2 = (Round to two decimal places)
- Interpret r2r2 :
- 77% of all employees will take the average number of sick days.
- Given any group with a fixed number of vacation days taken, 77% of all of those employees will take the predicted number of sick days.
- There is a large variation in the number of sick days employees take, but if you only look at employees who take a fixed number of vacation days, this variation on average is reduced by 77%.
- There is a 77% chance that the regression line will be a good predictor for the number of sick days taken based on the number of vacation days taken.
- The equation of the linear regression line is:
ˆyy^ = + xx (Please show your answers to two decimal places) - Use the model to predict the number of sick days taken for an employee who took 5 vacation days this year.
Sick Days = (Please round your answer to the nearest whole number.) - Interpret the slope of the regression line in the context of the question:
- For every additional vacation day taken, employees tend to take on average 0.73 fewer sick days.
- The slope has no practical meaning since a negative number cannot occur with vacation days and sick days.
- As x goes up, y goes down.
- Interpret the y-intercept in the context of the question:
- If an employee takes no vacation days, then that employee will take 8 sick days.
- The average number of sick days is predicted to be 8.
- The y-intercept has no practical meaning for this study.
- The best prediction for an employee who doesn't take any vacation days is that the employee will take 8 sick days.
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