A factorial experiment involving two levels of factor A and three levels of factor B resulted in the following data. Factor A Level 1 Level 2 Level 1 131 161 129 99 Factor B Level 2 94 70 123 101 Level 3 75 93 120 136 Test for any significant main effects and any interaction. Use a = 0.05. Find the value of the test statistic for factor A. (Round your answer to two decimal places.) Find the p-value for factor A. (Round your answer to three decimal places.) p-value = State your conclusion about factor A. O Because the p-value > a = 0.05, factor A is significant. O Because the p-value > a = 0.05, factor A is not significant. O Because the p-value < a = 0.05, factor A is not significant. O Because the p-value ≤ a = 0.05, factor A is significant.

mc

what can we say about how the number of children compare for these two groups?

In a research study to test a new drug on patients with brain cancer, patients were divided into 2 groups randomly. One group received the drug and the other group did not receive the drug. After 6 weeks, the measured results showed reduction in symptoms among the first group who received the drug. Is this an example of an observational study or experimental study? If it is an experiment, what is the controlled factor?

None

A researcher would like to use a factorial study to compare two programs designed to help people stop smoking. The smoking behavior of each participant will be measured at the beginning of the program, at the end of the program, and again 4 months after the program has ended. Thus, the two treatment program make up one factor, three measurement times make up the second factor. For this study, which factor (s) should be between subjects and which should be within subjects?

A 2 X 3 factorial design has     two variables that divide the groups.     six variables that divide the groups.     hree variables that divide the groups.     "could be any of the above, depending on the number of measured variables"

The interaction effect in a factorial design is considered, because it may modify the main effects of the independent variables.  True  False

Basketball players can take shots worth 3 points, 2 points, or 1 point. A scout is assessing two players- Tabitha and Lauren-who play for different teams in different leagues. The scout wonders if they have similar or different shot selections. They take a random sample of Tabitha's games and a separate random sample of Lauren's games. They tally how many of each type of shot the players attempted in those games. Here is a summary and the results of a chi-square test: Chi-square test: Shot vs. player Tabitha Lauren 3-point 8 12 Expected 10.34 9.66 2-point 40 37 Expected 39.83 37.17 1-point 12 Expected 9.83 9.17 x² = 2.097, DF = 2, P-value = 0.350 %3D Assume that all conditions for inference were met. At the a = 0.05 significance level, what is the most appropriate conclusion to draw from this test? Choose 1 answer: This is convincing evidence that the distribution of shot type differs between Tabitha and Lauren.

Basketball players can take shots worth 3 points, 2 points, or 1 point. A scout is assessing two players- Tabitha and Lauren-who play for different teams in different leagues. The scout wonders if they have similar or different shot selections. They take a random sample of Tabitha's games and a separate random sample of Lauren's games. They tally how many of each type of shot the players attempted in those games. Here is a summary and the results of a chi-square test: Chi-square test: Shot vs. player Tabitha Lauren 3-point 8 12 Expected 10.34 9.66 2-point 40 37 Expected 39.83 37.17 1 point 12 7. Expected 9.83 9.17 X=2.097, DF 2, P-value 0.350 Assume that all conditions for inference were met. At the a=0.05 significance level, what is the most appropriate conclusion to draw from this test? O This is convincing evidence that the distribution of shot type differs between Tabitha and Lauren. O They lack evidence to say that shot type and player are not independent. O This is convincing…