SPSS Activity Ch 11
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MATH 220
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Mathematics
Date
Apr 3, 2024
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docx
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Uploaded by ChancellorCoyote1487
MATH 220
Korayem
Spring 2023
Name:
Jamie Del Cid Date:
2/7/24 Sec: 15
SPSS In-Class Activity Ch. 11: Correlation and Regression
The SPSS file “QB Height and Weight” on Canvas contains the heights (inches) and weights (pounds) of 11 National Football League quarterbacks from the 2009 season. Output for Activity 1
: (1 pts) Use the data to create a scatterplot that investigates how a quarterback’s height explains/affects the quarterback’s weight. Add a title that describes what is displayed and indicates who the individuals are. Copy and paste the edited Scatterplot below.
(1 pts) Use the graph to answer the following questions: What is the explanatory variable and what is the response variable?
Based on the Scatterplot
, describe the correlation between the two variables.
The height in inches of a quarterback is the explanatory variable. The weight in pounds of the quarterback is the
response variable. There is a moderate, positive, linear correlations between height in inches and weight in pounds.
MATH 220
Korayem
Spring 2023
Output for Activity 2
:
(1 pts) Use SPSS to obtain the correlation between a quarterback’s height and weight. Copy and paste the edited Correlations table below.
(2pts) Use the table to answer the following questions:
1.
Describe the relationship between these two variables in context as taught in class.
What is the correlation coefficient between the height and weight of the quarterbacks? What does it tell us about the relationship between the height of a quarterback and his weight in terms of form, direction, and strength?
The correlation coefficient r = 0.674, this indicates we have a strong, positive, linear relationship between a quarterback’s height(inches) and their weight (pounds)
2.
Which of the following is the best interpretation of the correlation coefficient?
i. If a quarterback gains weight, he will grow taller.
ii. Given two quarterbacks, the taller one is likely to be heavier than the shorter one.
iii. Given two quarterbacks, the heavier one is likely to be shorter than the lighter one.
MATH 220
Korayem
Spring 2023
Output for Activity 3
: (2 pts) In this activity, you will obtain and use the least square regression line for predicting the weight of a quarterback from their height.
Copy and paste the edited “Model Summary” and “Coefficients” tables below
. (3pts) Use the tables to answer the following questions:
1.
Find the equation of the least-squares regression line for predicting weight from height. Use appropriate notation and if you use the variables x, y in your equation, tell me what x and y stand for in context
. (Use this ‘ŷ’, or if it’s easier, you may type ‘Predicted y’ instead of ŷ.)
Bo
-121.667
B1
4.600 R = 0.674
ŷ= -121.667 +4.600x ŷ is the predicted weight in pounds of a quarterback X is the height in inches of a quarter back 2.
What does the slope of this equation represent? Interpret in context as taught in class.
The slope = 4.600. On averages, for every 1-inch increase in the height of a quarterback, their weight is expected to increase by 4.600 pounds
3.
Predict the weight of a QB with height of 100 inches. Is this a reliable prediction? Why?
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MATH 220
Korayem
Spring 2023
Y = -121.667 +4.600 (100) = 338.33 lbs. It is an extrapolation, the range is 71 to 77, therefore it’s not reliable.