Final answer:
To perform simple linear regression analysis, option (a) Numeric response and numeric predictor is correct. The correct answer is option a.
Step-by-step explanation:
To conduct a simple linear regression analysis, certain types of variables are needed: a numeric response variable and a numeric predictor variable. This corresponds to option (a) Numeric response and numeric predictor, which is the correct option for conducting simple linear regression.
Here is a breakdown of the process:
- Decide which variable should be the independent variable (predictor) and which should be the dependent variable (response).
- Draw a scatter plot of the data.
- Use regression to find the line of best fit and the correlation coefficient.
- Interpret the significance of the correlation coefficient to assess the strength of the relationship.
- Evaluate if there is a linear relationship between the variables based on the correlation coefficient and the scatter plot.
In simple linear regression, the linear equation is often written as ý = a + bx, where 'a' is the y-intercept and 'b' is the slope. The coefficient b indicates the rate of change between the independent and the dependent variables. The model aims to predict changes in the dependent variable based on different values of the independent variable and is visually represented by the line of best fit on the scatter plot.
For example, if we are interested in whether there is a relationship between the number of hours studied and the scores on an exam, we would assign 'number of hours studied' as the independent variable and 'exam score' as the dependent variable, and then proceed with the steps outlined above to conduct the analysis.