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## STAT 200 PSU Quiz 11A+++

by: kimwood Notetaker

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# STAT 200 PSU Quiz 11A+++

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STAT 200 PSU Quiz 11A+++
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PAGES
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KARMA
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This 6 page Study Guide was uploaded by kimwood Notetaker on Friday November 6, 2015. The Study Guide belongs to a course at a university taught by a professor in Fall. Since its upload, it has received 30 views.

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Date Created: 11/06/15
1. Click one of the following links Minitab Data or Excel Data to open a data set. From this data, use  software to answer the following: 1. What is the correlation between SAT Math and SAT Verbal?2. What  is the slope of the regression equation when using SAT Math as the responsevariable (Y) and SAT  Verbal as the predictor variable (X)? A) Correlation = 0.709; Slope = 0.7375  B) Correlation = 0.709; Slope = 171.7  C) Correlation = 0.748; Slope = 141.9  D) Correlation = 0.621; Slope = 224.6  Feedback: For correlation: Use Stat > Basic Statistics > Correlation and enter SAT Math and SAT Verbal  into the Variables window. For slope: Go to Stat>Regression>Fitted Line Plot. Enter SAT Math for the  Response (Y) variable and SAT Verbal for the Predictor (X). Points Earned: 0.0/2.0 2. If the correlation between a response variable Y and explanatory variable X is ­ 0.3, what is the value that defines how much variation in Y is explained by X? A) 9%  B) 3%  C) ­ 9%  D) ­ 3%  Feedback: The phrase in the question "what is the value that explains how much variation in Y is  explained by X" refers to the Coefficient of Determination, or r . Since r = ­ 0.3 the r  would be equal to (­  0.3)*(­ 0.3) = 0.09 or 9% Points Earned: 1.0/1.0 3.   Describe the type of association shown in the scatterplot above: A) Positive linear association  B) Negative linear association  C) Positive curvilinear association  D) Negative curvilinear association  Feedback: The scatterplot clearly shows a negative relationship that is curvilinear Points Earned: 1.0/1.0 4.   Describe the type of association shown in the scatterplot above: A) Positive linear association  B) Negative linear association  C) Positive curvilinear association  D) Negative curvilinear association  Feedback: The scatterplot clearly shows a negative relationship that is linear Points Earned: 1.0/1.0 5. In the simple linear regression equation y = b0 + b1x, the term b1 represents the A) estimated or predicted response.  B) estimated intercept.  C) estimated slope.  D) explanatory variable.  Feedback: This is analogous to the algebra equivalent y = mx + b where m is the slope of the line. Points Earned: 1.0/1.0 6. A group of adults aged 20 to 80 were tested to see how far away they could first hear an ambulance  coming towards them. An equation describing the relationship between distance (in feet) and age was  found to be:  Distance = 600 ­ 3 × Age How much does the estimated distance change when age is increased by 1? A) It goes down by 1 foot.  B) It goes down by 3 feet.  C) It goes up by 1 foot.  D) It goes up by 3 feet.  Feedback: The slope gives the increase (or decrease) in the response as X increases by 1 unit. So if Age (x) increases by 1 year, then distance (y) decreases, since slope is negative by 3 feet. Points Earned: 1.0/1.0 7. The value of a correlation is reported by a researcher to be r = ­ 0.5. Which of the following statements is  correct? A) The x­variable explains 50% of the variability in the y­variable.  B) The x­variable explains ­50% of the variability in the y­variable.  C) The x­variable explains 25% of the variability in the y­variable.  D) The x­variable explains ­25% of the variability in the y­variable.  Feedback: The "explanation of..." refers to the Coefficient of Determination, or R . So ­0.5 squared is  25% Points Earned: 1.0/1.0 8. A group of adults aged 20 to 80 were tested to see how far away they could first hear an ambulance  coming towards them. An equation describing the relationship between distance (in feet) and age was  found to be  Distance = 600 ­ 3 × Age Based on the equation, what is the strength of the relationship between distance and age? A) There is a strong relationship.  B) There is a weak relationship.  C) There is no relationship.  D) Strength can’t be determined from the equation.  Feedback: You cannot tell the strength of a relationship simply from the equation. Points Earned: 0.0/1.0 9. Regression Analysis:  Weight versus Height The regression equation is:  Weight = 21.2 + 1.94 Height Predictor     Coef    SE Coef     T       P Constant    21.19     22.05     0.96   0.338 Height      1.9419   0.3247     5.98   0.000   S = 28.6963       R­Sq = 13.8% Based on the p­value for HEIGHT, we can reject the null hypothesis and conclude that there exists a  linear relationship between Height and Weight. A) True  B) False  Feedback: True since the reported p­value is ≈ 0, it is less than any common value. Thus we would  reject Ho that the slope is 0 and conclude that a statistically significant linear relationship exists between  Height and Weight. Points Earned: 1.0/1.0 10. Regression Analysis:  Weight versus Height The regression equation is:  Weight = 21.2 + 1.94 Height Predictor     Coef    SE Coef     T       P Constant    21.19     22.05     0.96   0.338 Height      1.9419   0.3247     5.98   0.000   S = 28.6963       R­Sq = 13.8% From the regression output above, what is the value of the correlation between Height and Weight? A) 0.3715  B) 0.612  C) ­ 0.612  D) 0.612 or ­ 0.612  Feedback: The correct answer is, 0.3715. We find this from taking the square root of R­sq = 0.138 and  the correlation is positive since the slope (1.94) in our regression equation is positive. Points Earned: 0.0/1.0

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