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Stats Exam | Questions And Answers Latest {} A+ Graded | 100% Verified
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Stats Exam | Questions And Answers Latest {2024- 2025} A+ Graded | 100% VerifiedThe proportion of the variation in the dependent variable y that is explained by the estimated regression
equation is measured by the - coefficient of determination
The least squares criterion is - min
In a residual plot against x that does not suggest we should challenge the assumptions of our regression
model, we would expect to see - a horizontal band of points centered near zero
The difference between the observed value of the dependent variable and the value predicted by using
the estimated regression equation is the - residual
As the goodness of fit for the estimated regression equation increases - the value of the coefficient of
determination increases
A measure of the strength of the relationship between two variables is the - correlation coefficient
The interval estimate of the mean value of y for a given value of x is the - confidence interval
Regression analysis is a statistical procedure for developing a mathematical equation that describes how
- one dependent and one or more independent variables are related
In regression analysis, the variable that is being predicted is the - dependent variable
In a regression analysis, the variable that is being predicted - is the dependent variable
In regression analysis, the independent variable is - used to predict the dependent variable
The equation that describes how the dependent variable (y) is related to the independent variable (x) is
called - the regression model
, In a simple regression analysis (where y is a dependent and x an independent variable), if the y intercept
is positive, then - None of these answers is correct.
A procedure used for finding the equation of a straight line that provides the best approximation for the
relationship between the independent and dependent variables is the - least squares method
Application of the least squares method results in values of the y intercept and the slope that minimizes
the sum of the squared deviations between the - observed values of the dependent variable and the
predicted values of the dependent variable
A least squares regression line - may be used to predict a value of y if the corresponding x value is given
In regression analysis if the dependent variable is measured in dollars, the independent variable - can be
any units
Regression analysis was applied between sales (in $1,000) and advertising (in $100), and the following
regression function was obtained.
= 80 + 6.2x - $700,000
Regression analysis was applied between sales (in $1000) and advertising (in $100) and the following
regression function was obtained.
= 500 + 4x - $900,000
A regression analysis between demand (y in 1000 units) and price (x in dollars) resulted in the following
equation
= 9 − 3x
The above equation implies that if the price is increased by $1, the demand is expected to - decrease by
3,000 units
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