Analysis of Regression Models and Hypothesis Testing in Predicting Dependent Variables
Explores regression models and hypothesis testing for predictive analysis.
Amelia Ward
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Analysis of Regression Models and Hypothesis Testing in Predicting
Dependent Variables
578Assignment-5 (Chs. 13 and 14)-solutions: Due by midnight of Sunday,
December 2nd, 2012: drop box 4): 70 points
True/False(One point each)
Chapter 13
1. The standard error of the estimate (standard error) is the estimated standard
deviation of the distribution of the independent variable (X).
FALSE it is the estimate of the standard deviation of the error term
2. In a simple linear regression model, the coefficient of determination only
indicates the strength of the relationship between independent and dependent
variable, but does not show whether the relationship is positive or negative.
TRUE R2 is greater than or equal to 0, no negative
3. When using simple regression analysis, if there is a strong correlation between
the independent and dependent variable, then we can conclude that an increase in
the value of the independent variable causes an increase in the value of the
dependent variable.
FALSEthe strong correlation could be negative
4. The error term is the difference between an individual value of the dependent
variable and the corresponding mean value of the dependent variable.
FALSE it is the difference between an individual value of the dependent
variable and the corresponding predicted value (not the mean value) :
residual and error term are the same thing
5. In bi-variate regression the Coefficient of Determination is always equal to the
square of the correlation coefficient. TRUE
Analysis of Regression Models and Hypothesis Testing in Predicting
Dependent Variables
578Assignment-5 (Chs. 13 and 14)-solutions: Due by midnight of Sunday,
December 2nd, 2012: drop box 4): 70 points
True/False(One point each)
Chapter 13
1. The standard error of the estimate (standard error) is the estimated standard
deviation of the distribution of the independent variable (X).
FALSE it is the estimate of the standard deviation of the error term
2. In a simple linear regression model, the coefficient of determination only
indicates the strength of the relationship between independent and dependent
variable, but does not show whether the relationship is positive or negative.
TRUE R2 is greater than or equal to 0, no negative
3. When using simple regression analysis, if there is a strong correlation between
the independent and dependent variable, then we can conclude that an increase in
the value of the independent variable causes an increase in the value of the
dependent variable.
FALSEthe strong correlation could be negative
4. The error term is the difference between an individual value of the dependent
variable and the corresponding mean value of the dependent variable.
FALSE it is the difference between an individual value of the dependent
variable and the corresponding predicted value (not the mean value) :
residual and error term are the same thing
5. In bi-variate regression the Coefficient of Determination is always equal to the
square of the correlation coefficient. TRUE
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Subject
Statistics