Regression Lise vth Siepe and eept Pne nal 16. The correct interpretation of the intercept in the plot is: a) as height increases, weight will decrease by 143 b) as height increases, weight will definitively increase by 3.899 c) as height inereases by a single unit, height will increase by 143 units d) when height is equal to 0, the value of the intercept is equal to e) when height is equal to 0, the value of the intercept is equal to approximately 70 f) when height is equal to 0, the predicted value for weight is equal to -143 17. The best (i.e., the “most correct") interpretation of the slope in the plot is: a) for a one-unit increase in height, weight will definitely increase by 3.899 units b) for a one-unit increase in height, weight, on average, increases by 3.899 units c) for a one-unit decrease in height, weight, on average, increases by 3.899 units d) for a one-unit decrease in height, weight, on average, increases by 3.899 units e) for a one-unit increase in height, weight, on average, decreases by 3.899 units 18. The above plot: a) reveals sure evidence that the population slope is unequal to 0 b) revcals sure evidence that the sample slope is unequal to 0 c) can be used alone to reject the null hypothesis that the population slope is unequal to 0 d) can be used alone to reject the null hypothesis that the population slope is equal to 0 e) a and e f) a and d
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
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A moodle.umt.edu
*** Questions 16 through 20 refer to the regression plot
below.
Regression Lise vth ipe nd e
16. The correct interpretation of the intercept in the plot is:
a) as height increases, weight will decrease by 143
b) as height increases, weight will definitively increase by 3.899
c) as height increases by a single unit, height will increase by
143 units
d) when height is equal to 0, the value of the intercept is equal to
e) when height is equal to 0, the value of the intercept is equal to
approximately 70
f) when height is equal to 0, the predicted value for weight is
equal to -143
17. The best (i.e., the “most correct") interpretation of the slope
in the plot is:
a) for a one-unit increase in height, weight will definitely
increase by 3.899 units
b) for a one-unit increase in height, weight, on average,
increases by 3.899 units
c) for a one-unit decrease in height, weight, on average,
increases by 3.899 units
d) for a one-unit decrease in height, weight, on average,
increases by 3.899 units
e) for a one-unit increase in height, weight, on average,
decreases by 3.899 units
18. The above plot:
a) reveals sure evidence that the population slope is unequal to 0
b) reveals sure evidence that the sample slope is unequal to 0
c) can be used alone to reject the null hypothesis that the
population slope is unequal to 0
d) can be used alone to reject the null hypothesis that the
population slope is equal to 0
e) a and c
f) a and d
19. In the above plot, a rejection of the null hypothesis that the
population regression slope is equal to 0 implies:
a) the correlation coefficient in the population is likely equal to
b) the correlation coefficient in the population is likely unequal
to 0
c) the standard deviation of height is likely very large
d) the standard deviation of weight is likely very small
e) the correlation coefficient must be close to 1.0
f) a and c
g) b and e
h) b, d and e
20. Based on only the above plot, one can conclude:
a) height causes an increase in weight
b) weight causes an increase in height
c) taller people are more likely to weigh more than shorter
people, at least in the sample on which this data is based
d) a statistically significant predictive relationship between
height and weight
e) c and d"
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