Statistics Chapter 10 – Flashcards
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If we find that there is a linear correlation between the concentration of carbon dioxide in our atmosphere and the global temperature, does that indicate that changes in the concentration of carbon dioxide cause changes in the global temperature?
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No. The presence of a linear correlation between two variables does not imply that one of the variables is the cause of the other variable.
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For a sample of eight bears, researchers measured the distances around the bears' chests and weighed the bears. Minitab was used to find that the value of the linear correlation coefficient is
r =0.996 Using α=0.05,
determine if there is a linear correlation between chest size and weight. What proportion of the variation in weight can be explained by the linear relationship between weight and chest size?
Critical Values for the Coefficient
n a=0.05 a=0.01
4 0.950 0.990
5 0.878 0.959
6 0.811 0.917
7 0.754 0.875
8 0.707 0.834
9 0.666 0.798
10 0.632 0.765
11 0.602 0.735
12 0.576 0.708
13 0.553 0.684
14 0.532 0.661
15 0.514 0.641
16 0.497 0.623
17 0.482 0.606
18 0.468 0.590
19 0.456 0.575
20 0.444 0.561
25 0.396 0.505
30 0.361 0.463
35 0.335 0.430
40 0.312 0.402
45 0.294 0.378
50 0.279 0.361
60 0.254 0.330
70 0.236 0.305
80 0.220 0.286
90 0.207 0.269
100 0.196 0.256
NOTE: To test Ho_p=0, against H1:p# 0, reject Ho if the absolute value of r is greater than the critical value in thee table.
answer
A.)Yes, because the absolute value of the test statistic exceeds the critical value of 0.707.
B.)What proportion of the variation in weight can be explained by the linear relationship between weight and chest size?
0.996 squared = 0.992
Therefore, 99.2% of the variation in weight can be explained by the linear relationship between weight and chest size.
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The heights (in inches) and pulse rates (in beats per minute) for a sample of 1919 women were measured. Using technology with the paired height/pulse data, the linear correlation coefficient is found to be 0.501
Is there sufficient evidence to support the claim that there is a linear correlation between the heights and pulse rates of women? Use a significance level of
α=0.05
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Because l 0.501 l GREATER than the critical value, there IS sufficient evidence to support the claim that there is a linear correlation between the heights and pulse rates of women for a significance level of α=0.05.
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The heights (in inches) and pulse rates (in beats per minute) for a sample of 55 women were measured. Using technology with the paired height/pulse data, the linear correlation coefficient is found to be 0.923.
Is there sufficient evidence to support the claim that there is a linear correlation between the heights and pulse rates of women? Use a significance level of
α=0.01
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Because l 0.923 l is LESS than the critical value, there IS NOT sufficient evidence to support the claim that there is a linear correlation between the heights and pulse rates of women for a significance level of α=0.01
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Which of the following is NOT true for a hypothesis test for correlation?
A.) If|r|>critical value, we should fail to reject the null hypothesis and conclude that there is not sufficient evidence to support the claim of a linear correlation.Your answer is correct.
B.) If l r l ≤critical value, we should fail to reject the null hypothesis and conclude that there is not sufficient evidence to support the claim of a linear correlation.
C.) If the P-value is less than or equal to the significance level, we should reject the null hypothesis and conclude that there is sufficient evidence to support the claim of a linear correlation.
D.) If the P-value is greater than the significance level, we should fail to reject the null hypothesis and conclude that there is not sufficient evidence to support the claim of a linear correlation.
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A.) If |r|>critical value, we should fail to reject the null hypothesis and conclude that there is not sufficient evidence to support the claim of a linear correlation.
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Which of the following is NOT one of the three common errors involving correlation?
A.)The conclusion that correlation implies causality
B.)Mistaking no linear correlation with no correlation
C.)The use of data based on averages
D.)Correlation does not imply causality
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D.)Correlation does not imply causality
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Which of the following is NOT a property of the linear correlation coefficient r?
A.)The value of r is always between minus−1 and 1 inclusive.
B.)The linear correlation coefficient r is robust. That is, a single outlier will not affect the value of r.
C.)The value of r is not affected by the choice of x or y.
D.)The value of r measures the strength of a linear relationship.
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B.)The linear correlation coefficient r is robust. That is, a single outlier will not affect the value of r.
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Which of the following is NOT a requirement in determining whether there is a linear correlation between two variables?
A.)A scatterplot should visually show a straight-line pattern.
B.)Any outliers must be removed if they are known to be errors.
C.)If r >1, then there is a positive linear correlation.Your answer is correct.
D.)The sample of paired data is a simple random sample of quantitative data.
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C.)If r >1, then there is a positive linear correlation.Your answer is correct.
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The ______________ measures the strength of the linear correlation between the paired quantitative x- and y-values in a sample.
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The linear correlation coefficient r
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Which of the following statements about correlation is true?
A.)We say that there is a positive correlation between x and y if there is no distinct pattern in the scatterplot.
B.)We say that there is a positive correlation between x and y if the x-values increase as the corresponding y-values decrease.
C.)We say that there is a negative correlation between x and y if the x-values increase as the corresponding y-values increase.
D.)We say that there is a positive correlation between x and y if the x-values increase as the corresponding y-values increase.
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D.)We say that there is a positive correlation between x and y if the x-values increase as the corresponding y-values increase.
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When determining whether there is a correlation between two variables, one should use a ____________ to explore the data visually.
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Scatterplot
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A __________ exists between two variables when the values of one variable are somehow associated with the values of the other variable.
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Correlation
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A ______________ is a scatterplot of the (x,y) values after each of the y-coordinate values has been replaced by the residual value y-y
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Residual Plot
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A straight line satisfies the __________________ if the sum of the squares of the residuals is the smallest sum possible.
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Least-squares Property
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For a pair of sample x- and y-values, the ______________ is the difference between the observed sample value of y and the y-value that is predicted by using the regression equation.
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Residual
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Paired sample data may include one or more ___________, which are points that strongly affect the graph of the regression line
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Influential Points
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In a scatterplot, a(n) ______________ is a point lying far away from the other data points
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Outlier
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In working with two variables related by a regression equation, the _________________ in a variable is the amount that it changes when the other variable changes by exactly one unit.
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Marginal Change
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When making predictions based on regression lines, which of the following is not listed as a consideration?
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Use the regression line for predictions only if the data go far beyond the scope of the available sample data.
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Which of the following is not equivalent to the other three?
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Dependent Variable
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Given a collection of paired sample data, the ____________________ y =b0+b1x algebraically describes the relationship between the two variables, x and y.
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Regression Equation
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Which of the following is not a requirement for regression analysis?
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The method for regression analysis line is not robust. It is seriously affected by a small departure from a normal distribution.