A study was conducted on the effect of daytime running lights on cars. Researchers gathered data from a random sample of 3,248 d
rivers and measured quite a few variables, in addition to the explanatory variable of daytime running lights and the response variable of less accidents. According to a
newspaper article summarizing the study, those cars that had daytime running lights were more likely to be operated by drivers who were confident and
attentive. Drivers whose cars did not have daytime running lights were about 22% more likely to have an accident. What conclusion can we draw from this
study? Explain.
We can infer a cause-and-effect relationship because the sample was selected randomly.
We cannot infer a cause-and-effect relationship because we do not have a control group.
We can infer a cause-and-effect relationship because multiple variables were included.
We cannot infer a cause-and-effect relationship because treatments were not assigned randomly.
We cannot infer a cause-and-effect relationship because the treatments imposed were not blocked correctly.
We cannot infer a cause and effect relationship because we do not have a control group
Step-by-step explanation:
The treatment in research involves giving stimulus known as treatment to a group or portion of the sample which are then known as the treatment group while the remaining portion of the the sample are exempted from the stimulus. A successful treatment manipulation is one where members of the treatment group perform higher in the measured metrics of the research than members of the control group.
Given that the researchers gathered data from a random sample of drivers and measurement of several variables including the explanatory variable of the of the research without subjecting a sample to a treatment or having a control group we cannot infer a cause and effect relationship as there are no control group
Linear. While all linear equations produce straight lines when graphed, not all linear equations produce linear functions. In order to be a linear function, a graph must be both linear (a straight line) and a function (matching each x-value to only one y-value).
