Stats Final 1 Questions and Answers 2024
If you suspect that you have an influential observation, the first thing you should do is:
1. increase the value of the slope.
2. re-record the data to see if the observation shows up again.
3. remove the influential observation from the data set.
4. check to make sure no error has been made in collecting or recording data. ** Answ** 4.
check to make sure no error has been made in collecting or recording data.
The model developed from sample data that has the ybar. = b0 + b1x form is known as the:
1. correlation equation.
2. simple linear regression equation.
3. simple linear regression model.
4. estimated simple linear regression equation. ** Answ** 4. estimated simple linear
regression equation.
The tests of significance in regression analysis are based on assumptions about the error term ɛ.
One such assumption is that the values of ɛ are:
1. categorical.
2. independent.
3. limited.
4. uniformly distributed. ** Answ** 2. independent.
If the coefficient of determination is a positive value, then the coefficient of correlation:
1. must also be positive.
2. must be zero.
3. can be either negative or positive.
4. must be larger than 1. ** Answ** 3. can be either negative or positive.
,The estimated regression equation, y^ = -10.42 + 0.79x , can be used to predict a company's sales
volume (y), in millions, based upon its advertising expenditure (x), in $10,000's. What is the
company's predicted sales volume if they spend $500,000 on advertising? ** Answ**
Approximately $29 million
Which of the following statements is false?
1. ŷ is the point estimator of E(y) , the mean value of y for a given value of x.
2. In the estimated simple linear regression equation, b0 is the y-intercept and b1 is the slope.
3. Regression analysis can be interpreted as a procedure for establishing a cause-and-effect
relationship between variables.
4. In practice, parameter values are not known and must be estimated using sample data. **
Answ** 3. Regression analysis can be interpreted as a procedure for establishing a cause-and-
effect relationship between variables.
If a residual plot of x versus the residuals, y - ŷ, shows a non-linear pattern, then we should
conclude that:
1. the regression model describes the relationship between x and y very well.
2. the regression model was not based upon a large enough sample size.
3. the regression model is not an adequate representation of the relationship between the
variables.
the regression model is useful for making predictions. ** Answ** 3. the regression model is
not an adequate representation of the relationship between the variables.
When studying the relationship between two quantitative variables, an interval estimate of the
mean value of y for a given value of x is called a(n):
a. prediction interval.
b. confidence interval.
c. determination interval.
d. estimation interval. ** Answ** b. confidence interval.
An observation that has a strong influence or effect on the regression results is called a(n):
, 1. mistake.
2. outlier.
3. residual.
4. influential observation. ** Answ** 4. influential observation.
In a simple linear regression model, the error term ε accounts for the variability in ______ that
cannot be explained by the linear relationship between x and y ** Answ** y
When studying the relationship between two quantitative variables, whenever we want to predict
an individual value of y for a new observation corresponding to a given value of x, we should
use a(n):
1. prediction interval.
2. confidence interval.
3. determination interval.
4. estimation interval. ** Answ** 1. prediction interval.
Observations with extreme values for the independent variables are called:
1. mistakes.
2. outliers.
3. high leverage points.
4. influential observations. ** Answ** 3. high leverage points.
When working with regression analysis, an outlier is:
1. any value that falls more than 1.5(IQR) above Q3 or below Q1
2. any observation that does not fit the trend shown by the remaining data.
3. any value that has a small residual.
4. any observation that is extreme in the x direction. ** Answ** 2. any observation that does
not fit the trend shown by the remaining data
In regression analysis, the variable that is being predicted is the:
1. independent variable.
If you suspect that you have an influential observation, the first thing you should do is:
1. increase the value of the slope.
2. re-record the data to see if the observation shows up again.
3. remove the influential observation from the data set.
4. check to make sure no error has been made in collecting or recording data. ** Answ** 4.
check to make sure no error has been made in collecting or recording data.
The model developed from sample data that has the ybar. = b0 + b1x form is known as the:
1. correlation equation.
2. simple linear regression equation.
3. simple linear regression model.
4. estimated simple linear regression equation. ** Answ** 4. estimated simple linear
regression equation.
The tests of significance in regression analysis are based on assumptions about the error term ɛ.
One such assumption is that the values of ɛ are:
1. categorical.
2. independent.
3. limited.
4. uniformly distributed. ** Answ** 2. independent.
If the coefficient of determination is a positive value, then the coefficient of correlation:
1. must also be positive.
2. must be zero.
3. can be either negative or positive.
4. must be larger than 1. ** Answ** 3. can be either negative or positive.
,The estimated regression equation, y^ = -10.42 + 0.79x , can be used to predict a company's sales
volume (y), in millions, based upon its advertising expenditure (x), in $10,000's. What is the
company's predicted sales volume if they spend $500,000 on advertising? ** Answ**
Approximately $29 million
Which of the following statements is false?
1. ŷ is the point estimator of E(y) , the mean value of y for a given value of x.
2. In the estimated simple linear regression equation, b0 is the y-intercept and b1 is the slope.
3. Regression analysis can be interpreted as a procedure for establishing a cause-and-effect
relationship between variables.
4. In practice, parameter values are not known and must be estimated using sample data. **
Answ** 3. Regression analysis can be interpreted as a procedure for establishing a cause-and-
effect relationship between variables.
If a residual plot of x versus the residuals, y - ŷ, shows a non-linear pattern, then we should
conclude that:
1. the regression model describes the relationship between x and y very well.
2. the regression model was not based upon a large enough sample size.
3. the regression model is not an adequate representation of the relationship between the
variables.
the regression model is useful for making predictions. ** Answ** 3. the regression model is
not an adequate representation of the relationship between the variables.
When studying the relationship between two quantitative variables, an interval estimate of the
mean value of y for a given value of x is called a(n):
a. prediction interval.
b. confidence interval.
c. determination interval.
d. estimation interval. ** Answ** b. confidence interval.
An observation that has a strong influence or effect on the regression results is called a(n):
, 1. mistake.
2. outlier.
3. residual.
4. influential observation. ** Answ** 4. influential observation.
In a simple linear regression model, the error term ε accounts for the variability in ______ that
cannot be explained by the linear relationship between x and y ** Answ** y
When studying the relationship between two quantitative variables, whenever we want to predict
an individual value of y for a new observation corresponding to a given value of x, we should
use a(n):
1. prediction interval.
2. confidence interval.
3. determination interval.
4. estimation interval. ** Answ** 1. prediction interval.
Observations with extreme values for the independent variables are called:
1. mistakes.
2. outliers.
3. high leverage points.
4. influential observations. ** Answ** 3. high leverage points.
When working with regression analysis, an outlier is:
1. any value that falls more than 1.5(IQR) above Q3 or below Q1
2. any observation that does not fit the trend shown by the remaining data.
3. any value that has a small residual.
4. any observation that is extreme in the x direction. ** Answ** 2. any observation that does
not fit the trend shown by the remaining data
In regression analysis, the variable that is being predicted is the:
1. independent variable.
