Basic Business Statistics, Student Value Edition
14th Edition
ISBN: 9780134685113
Author: Mark L. Berenson, David M. Levine, David F. Stephan, Kathryn Szabat
Publisher: PEARSON
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Chapter 11, Problem 35PS
To determine
Explain the method to determine the interaction in the two-factorial design.
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The interaction effect in a factorial design is considered, because it may modify the main effects of the independent variables.
True
False
video gamer believes that team composition greatly affects win rate. To test this idea, the player decides to play 100 matches with teams of 3 colors: Red, Green, and Blue. The player records the number of matches he wins with each team color.
Given anecdotal evidence, the gamer believes his red team is the best and therefore will win the most games. He has less experience with his blue and green team and is interested to see if there is a difference between the two. Because the gamer is more experienced with his red team, he plays more matches with that team. The total number of matches played per team color is: Red-50, Blue-23, and Green 27.
a) What is the IV of this experiment? What are the levels of the IV? What is the scale of measurement of the IV?
b) What is the DV of this experiment? What is the scale of measurement of the DV?
c) Is this a True experiment or not? Explain your reasoning.
d) What is the hypothesis of this experiment? Is it directional or nondirectional?
e)…
In a two factor factorial design with two design factors A and B, why do we generally include the interaction term in the model? What is the major concern if the model ignores the interaction effect?
Chapter 11 Solutions
Basic Business Statistics, Student Value Edition
Ch. 11 - An experiment has a single factor with five groups...Ch. 11 - You are working with the same experiment as in...Ch. 11 - You are working with the same experiment as in...Ch. 11 - Consider an experiment with three groups, with...Ch. 11 - Prob. 5PSCh. 11 - You are working the same experiment as in Problem...Ch. 11 - One of the steps involved in the processing of...Ch. 11 - The more costly and time-consuming it is to export...Ch. 11 - A hospital conducted a study of the waiting time...Ch. 11 - A manufacturer of pens has hired an advertising...
Ch. 11 - Prob. 11PSCh. 11 - Brand valuations are critical to CEOs, financial...Ch. 11 - A pet food company has a business objective of...Ch. 11 - A transportation strategist wanted to compare the...Ch. 11 - Consider a two-factor factorial design with three...Ch. 11 - Prob. 16PSCh. 11 - Prob. 17PSCh. 11 - Prob. 18PSCh. 11 - Given a two-way ANOVA with two levels for factor A...Ch. 11 - Given a two-factor factorial experiment and the...Ch. 11 - Given the results from Problem 11.20, a. at the...Ch. 11 - An experiment was conducted to study the extrusion...Ch. 11 - Prob. 23PSCh. 11 - Prob. 24PSCh. 11 - A glass manufacturing company wanted to...Ch. 11 - Prob. 26PSCh. 11 - Prob. 27PSCh. 11 - Prob. 28PSCh. 11 - Prob. 29PSCh. 11 - Prob. 30PSCh. 11 - Prob. 31PSCh. 11 - Prob. 32PSCh. 11 - Prob. 33PSCh. 11 - Prob. 34PSCh. 11 - Prob. 35PSCh. 11 - Prob. 36PSCh. 11 - Prob. 37PSCh. 11 - Prob. 38PSCh. 11 - Suppose that, when setting up the experiment in...Ch. 11 - A hotel wanted to develop a new system for...Ch. 11 - Refer to the room service experiment in Problem...Ch. 11 - Prob. 42PS
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- Can higher-order interactions be dropped to error in the analysis of fractional factorial designs? Why or why not ?arrow_forwardA 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?arrow_forwardA researcher wants to see if gender and / or income affects the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help. The independent variables are gender, income, and the interaction of gender and income. The dependent variable is total help. He wants to know if one or both factors – or the interaction of the two - affects the total amount of help offered. Because he is analyzing two independent variables (gender and income), he used a factorial ANOVA. His results show the main effect of each of the independent variables on the dependent variable (total help) and the interaction effect. The researcher is using a 95% confidence interval which means that he wants to be at least 95% sure that his independent variables affected total help if he rejects the null hypothesis. Below is the data set followed by the results:arrow_forward
- Explain the Graphs of outcomes of the two-factor experiment.arrow_forwardA researcher wants to examine the effect of humidity on amount of hair frizz. How could the researcher address this question using a multiple-groups design? What are the pros and cons of this design choice? What is/are the appropriate analysis/analyses? How could the researcher address this question using a dependent multiple-groups design? What are the pros and cons of this design choice? Address whatever design you select or consider both types of dependent multiple-groups designs. What is/are the appropriate analysis/analyses?arrow_forwardA dairy farmer thinks that the average weight gain of his cows depends on two factors: the type of grain that they are fed and the type of grass that they are fed. The dairy farmer has four different types of grain from which to choose and three different types of grass from which to choose. He would like to determine if there is a particular combination of grain and grass that would lead to the greatest weight gain on average for his cows. He randomly selects three one-year-old cows and assigns them to each of the possible combinations of grain and grass. After one year he records the weight gain for each cow (in pounds) with the following results. Is there sufficient evidence to conclude that there is a significant difference in the average weight gains among the cows for the different types of grain? Cow Weight Gain (Pounds) Grass A Grass B Grass B Grain A 359359 327327 232232 277277 250250 163163 191191 304304 216216 Grain B 331331 348348 176176 318318 205205…arrow_forward
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