PART 1 DATA: Case Analysis 1: Basic Analytics & Regression The general manager o
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Question
PART 1 DATA:
Case Analysis 1: Basic Analytics & Regression
The general manager of the Cleveland Indians baseball team is in the process of determining which minor-league players to bring into the team so that later they would play as major-league players in his team. He is aware that his team needs players with high numbers of home-run (HR) hits and would like to find a way to predict the number of home runs a player will hit in major leagues. He gathers a random sample of players and records the number of home runs each player hit in his first two full years as a major-league player, the number of home runs he hits in his last full year in the minor leagues, his age, and the number of years of professional baseball.
PART 1: Use the data in sheet “data for part 1”
1-Looking at the range of values the variable number of home run hits in minor leagues can take,this variable is a ?
Select one:
a. Quantitative variable
b. Categorical variable
2-In this problem,number of hits in minor leagues is a …?
Select one:
a. predictor variable
b. response variable
c. none of the above
3-Which of the following equations properly explains the dependence of major home run hits on minor home run hits,age,and experience?
Select one:
a. Major HR = Minor HR + age + experience
b. Major HR = -0.206 + 7.640 Minor HR + 0.259 age + 1.754 experience
c. Major HR = -1.970 Minor HR + 0.666 age + 0.136 experience
d. Major HR = -1.970 + 0.666 Minor HR + 0.136 age + 1.176 experience
e. Major HR = 3.306 + 0.486 Minor HR + 0.320 age -1.073 experience
4-If the multiplier of age were 6.1 in the above model,it would best mean?
Select one:
a. If a player is one year older,he can hit 6.1 more major home runs on average.
b. For any given player,if one year passes with no baseball activity,he can hit 6.1 more major home runs.
c. If one year passes for a typical player with no baseball activity,he can hit 6.1 more major home runs on average.
d. If one year passes for a typical player who has been involved in professional baseball within the year,he can hit 6.1 more major home runs on average.
5-How much of the variation in data is explained by the above model?
Select one:
a. 35%
b. 59%
c. 5964
d. 6.99
6-How many major home runs a 25 year old novice with absolutely no professional experience in baseball or leagues is expected to have?
Select one:
a. -1.79
b. 1.42
c. 3.39
d. 0.14
7-Which of the following residual plots significantly violates the regression assumption that residuals should show no significant pattern?
Select one:
a. Plot of residuals versus minor home-runs
b. Plot of residuals versus experience
c. Both
d. None
8-Now create another regression model that would explain the dependence of major home run hits on only minor home run hits and experience (this is a model similar to question 3,with the difference that variable age is now excluded).What would be the equation of the regression line?
Select one:
a. Major HR = Minor HR + experience
b. Major HR = 4.485 + 0.658 Minor HR
c. Major HR = 3.306 + 0.486 Minor HR + 0.320 age -1.073 experience
d. Major HR = -1.970 + 0.666 Minor HR + 1.176 experience
e. Major HR = 0.453 + 0.668 Minor HR + 1.305 experience
9-Why is the adjusted R-squared in question 8 (with two predictors) more than that in question 3 (with three predictors)?
Select one:
a. Because the model with two predictors has a higher R-squared compared to the model with three predictors
b. Because the variable age was not explaining enough variability in the model with three predictors
c. This is the result of miscalculation by Excel regression
d. Because the variable age in the model with three predictors is much more influential than the other variables in explaining the response variable.
Going back to the model you developed in Question 3, we would like to investigate whether the developed model represents a good-fitting linear model. Assume a 5% significance level whenever needed.
10- Which of the following can represent the ALTERNATE hypothesis (H1) for testing the overall linear fit of the model you developed in mini-project 1 (as described above)?
Select one:
a. 123
b. 1=2=3=0
c. 10
d. At least two of the -values are different
e. At least one -value is not zero
11- What is the test statistic for testing the overall linear (the test outlined in question 10)?
Select one:
a. 1.86×1011
b. 22
c. 0.35
d. 7.64
e. -0.21
12- Which of the following conclusions can be made when testing for the overall linear fit (the test of question 10)?
Select one:
a. All predictor variables have a significant impact on the number of major home runs
b. Number of major home runs has a significant impact on at least one of the predictor variables
c. At least one of the predictor variables have a significant impact on the number of major home runs
d. None of the predictor variables have a significant impact on the number of major home runs
13- Which of the following predictor variables does NOT have a significant impact on the number of major home runs?
Select one:
a. Number of minor home runs
b. Years of professional experience
c. Both
d. None
14- Which of the following pairs of predictor variables are highly correlated?
Select one:
a. Number of minor home runs & age
b. Number of minor home runs & years of experience
c. Age & years of experience
d. None
15- Which of the following variables cannot be removed without significantly hurting the model?
Select one:
a. Number of minor home runs
b. Age
c. Years of professional experience
d. Each one of the above can be removed
PART 2 DATA
PART 2: Use the data in sheet “data for part 2”
In addition to the data set that was available before, the general manager has now acquired more information on the type of the baseball player when hitting the ball. Each player is classified to be either a “power hitter” or a “contact hitter”. Power hitters tend to exert more power when hitting the ball and therefore tend to strike out more often than contact hitters. The full data set is available in the second spreadsheet of the data file.
16 - Create a pivot table to display how does the type of hitter and years of experience influence the number of major home runs. What is the average number of major home runs for power hitters that have 2 or less years of experience (0, 1 or 2 years of experience)? (hint: “grouping” would be helpful)
Select one:
a. 10
b. 17.78
c. 13.89
d. 15.6
e. 13.15
17- Including the new variable for type of the hitter inside the regression model, which of the following is true when comparing power hitters with contact hitters,when other predictor variables are held constant?
Select one:
a. The difference in major home run hits between the two types of hitters is not significant
b. Power hitters have 3.58 MORE major home run hits than contact hitters on average
c. Power hitters have 3.58 LESS major home run hits than contact hitters on average
d. Power hitters have 4.5 MORE major home run hits than contact hitters on average
e. Power hitters have 4.5 LESS major home run hits than contact hitters on average
Major HR Minor HR Years Pro Age 19 13 3 19 23 15 3 21 6 4 5 22 6 12 3 21 7 21 2 19 18 19 3 21 3 0 2 19 7 8 1 20 20 20 3 21 9 12 4 22 11 16 6 23 7 10 5 21 25 19 3 22 4 11 3 19 35 19 3 20 13 12 1 18 18 11 3 21 6 14 2 21 8 10 2 19 12 1 2 19 20 18 5 24 4 22 1 18 11 13 2 18 32 20 5 23 2 4 2 19 22 16 4 20 2 2 2 19 2 2 2 21 9 9 2 20 32 19 2 19 3 6 1 19 10 9 6 23 5 6 4 21 24 18 2 20 10 12 3 21 10 11 3 22 19 12 3 21 2 1 4 23 16 10 3 21 11 26 4 19 28 15 3 23 20 13 7 24 18 12 4 24 9 14 3 21 0 5 0 18 10 12 2 22 20 29 2 19 11 10 2 20 12 10 3 22 8 19 4 20 12 9 5 23 21 13 4 21 11 11 2 21 28 24 3 21 4 7 2 20 38 22 2 19 8 7 5 23 7 8 4 21 4 6 1 18 15 11 6 23 12 12 1 18 3 3 3 19 8 12 1 18 3 0 2 21 24 22 4 22 23 14 5 23 17 12 1 18 22 17 4 23 23 12 6 24 12 11 5 23 6 8 2 19 34 23 2 20 5 15 4 20 21 13 3 22 13 24 2 21 4 5 3 24 8 17 4 23 20 11 4 21 17 10 4 20 11 19 4 23 23 25 3 23 7 28 3 23 5 2 3 23 25 12 4 24 12 25 2 20 6 7 1 20 21 17 5 22 28 26 2 23 7 5 5 23 21 11 3 19 5 13 3 20 22 21 2 20 7 6 3 21 3 6 3 21 7 8 4 22 13 14 2 18 15 12 7 24 26 20 2 20 18 10 4 24 4 7 1 19 19 14 4 22 16 33 3 22 12 21 2 21 10 14 1 18 23 12 4 20 6 9 3 23 16 15 2 19 10 24 3 22 3 0 4 22 2 7 2 19 17 12 6 24 6 11 2 19 19 12 3 19 6 5 4 24 10 9 2 20 6 18 3 21 3 2 2 20 11 11 2 20 18 10 1 19 4 6 2 21 6 12 3 21 29 31 2 21 12 16 2 18 7 22 2 20 8 35 1 20 30 23 3 23 moodle-2017 Titanium 2017-2018 -Looking at the range of values the varable number of home run hitsin minor leagues can take,thia variable is a ? Points out of Select one a. Quanttative variable b.Categorical variable 2-ln this problem,number of hits in minor eagues is Paints out of Select one predictor varabie b, ponse variable c none of the above -Which of the tollowing equations propery explains the Paints aut of Select one a Major HRMinorHR+ ae experence b. Major HR 0.206+7.640 Mnor HR+0.259 ag 1.754 expenence 4-the munpier er age wore 6.1 r, the above modeGweid best mear? MacBook Air 5 7Explanation / Answer
Solution:
a) Since the no of hits is number range between 0 and 35 thus its a Quantative Variable
b) The Minor.HR variable is not depandent on any other variable, thus it will be a predicter variable.
c) d. Major HR = -1.970 + 0.666 Minor HR + 0.136 age + 1.176 experience
Explanation:
As seen from the output of cofficients option D is the correct answer
4) option D
5) option A 35% (Refer Multiple R-square from the above output)
R- square is the Metric which explain how well the variables is explained by our model.
6) Option C (-1.970 + 0.666 + 0.136 * 25 + 1.176)
7)
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