of each column on the previous ot the study would provide convincing evidence ab
ID: 3067905 • Letter: O
Question
of each column on the previous ot the study would provide convincing evidence about cause and effect. In the space below, explain why you labeled each study as provides convincing evidence page indicate whether or about cause and effect or does NOT provide convincing evidence about couse and effect Match your top two research questions to the corresponding study in the table on the previous page (write the research question in the first box at the top of the appropriate column). c) 7) In each of the statistical studies listed on the previous page, what are the variables (note- the variables are the same in both studies)? Which is the explanatory variable and which is the response variable? Explain. 8) Taking a little sidetrack from the "smoking statistical studies. Suppose you're part of a group of 10 friends that get together on Sundays to play basketball. Two people in your group are really good players and two people in your group are pretty bad at the game (but they've got heart and they are the glue that keeps the group together). Everyone else is fairly average. Now suppose that one team gets both of the really good players and each team gets one bad player. What do you suspect will likely happen when the two teams play each other? Explain and try to use the word "bias" in your explanation. a) really good player. What do you suspect will ikely happen when the two teams play each other? Explain and try to use the word "bias" in your explanation. b) Now suppose that one team gets both of the bad players and each team gets one c) How could we control for the bias in part a) and/or part b)? Ca Patty tn weExplanation / Answer
Answer:
By using ,given data
(7)
Now ,we have to consider a linear model,
Y=b0+b1x + error
Here , the variable y which is dependent on the variable x ,as any changes in variable x results in change in y ,thus it is called Dependent variable or Response variable.
And the variable x which is independent of variable y is called independent variables or explanatory variables or concommitant variable as it increases or decreases independently in the model .And it doesn't depend on other ,but other depends on it.
(8)
(A)
When one of the team got both good player ,then there is a great chance that this team will win the match as this team has both the good player in fact the two bad player has not effect on the team as they are equally distributed in both the team .Here we have " biasness" in selection of player ,due to which ,we get biased results. i.e ,The team with both good player score more hoalg and won the match.
(B)
In this case also there is biasness in the selection of player as one team got both of the bad player .here the two good player are equally distributed among both the teams so the overall effect occurs due to selection of both bad players. The team with both bad players has more and more chances of loosing match.so here too error in results occurred due to biased sampling.
(C)
To reduce or avoid bias in the above part a) and part b) one should have to use unbiased sampling techniques or player should be selected at random and there should be equal chances of selection of both good and both bad players in both the teams so that error in results minimised to some extent and we get a unbiased results.
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