Question

True or False:

the t statistic for an individual coefficient measures the contribution of the corresponding independent predictor after controlling for the other independent predictors in the m

Answer #1

The t statistic for an individual coefficient measures the contribution of the corresponding independent predictor after controlling for the other independent predictors in the multiple regression model.

Answer: True

Explanation: We know that the significance of the regression coefficient is checked by using the t test for the regression coefficient and this test measures the contribution of the corresponding independent predictor after controlling for the other independent variables. We check whether the given regression coefficient is statistically significant or not, and if this is significant, then we can find the contribution of this coefficient in the entire model.

True or False: If the correlation coefficient between response
variable and the independent variable (or predictor) is close to
zero, then the predictor makes almost no contribution in the
variation of the response. Briefly justify your conclusion.

Answer True or False to the statements below.
1) The coefficient of determination measures the variation in
the dependent variable that is explained by the regression
model
2) A student might expect that there is a positive correlation
between the age of his or her computer and its resale value.
3) The variable that is being predicted in regression analysis
is the independent variable.
4) If there is no correlation between the independent and
dependent variables, then the value of...

TRUE or FALSE: A correlation coefficient distinguishes between
independent and dependent variables.

True or False. Taxpayer (“T”) an individual calendar year
taxpayer can take a deduction for qualifying medical expenses and
the standard deduction in the same year?

True or false
1. Researchers should be careful to control for colliders and
confounders, but avoid controlling for mediators.
2. The only way to control for a possible confounder is to
measure it and include it as a predictor in your regression
model.
3. Homoskedasticity in regression is the same assumption as
Homogeneity of Variance in ANOVA
4. Multilevel models offer one solution to violations of the
assumption of independent observations.
5.The assumption of normality states that the values of...

TRUE OR FALSE:
1. When the correlation coefficient is close to -1, that means
that there is no relationship between the independent variable and
the dependent variable.
2. When the correlation coefficient is close to -1, that means
that there is no relationship between the independent variable and
the dependent variable.
3. A correlation coefficient of -0.96 would mean that the values
of y decrease as the values of x increase.

True or False: Assuming a linear relationship between X and Y,
if the coefficient of correlation (r) equals 0.50, this means that
50% of the variation in the dependent variable (Y) is due to
changes in the independent variable (X).

True or False: Assuming a linear relationship between X
and Y, if the coefficient of correlation (r)
equals 0.50, this means that 50% of the variation in the dependent
variable (Y) is due to changes in the independent variable (X).

A is TRUE for a Wilcoxon’s rank-sum test. B is FALSE for a
repeated measures t-test.
A: Assumes that the data is skewed. B: Does not require
assumptions of normality.
A: Sample variance is used to estimate population variance. B:
Assumes the data is skewed.
A: Ranking reduces the effects of extreme outliers. B: Assumes
the use of only interval scales.
A: Does not require assumptions of normality. B: Sample variance
is used to estimate population variance.

A is TRUE for a Wilcoxon’s rank-sum test. B is FALSE for a
repeated measures t-test. A: Assumes that the data is skewed. B:
Does not require assumptions of normality. A: Sample variance is
used to estimate population variance. B: Assumes the data is
skewed. A: Ranking reduces the effects of extreme outliers. B:
Assumes the use of only interval scales. A: Does not require
assumptions of normality. B: Sample variance is used to estimate
population variance

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