Explore BrainMass

Explore BrainMass

    20 Linear Regression Multiple Choice Questions

    Not what you're looking for? Search our solutions OR ask your own Custom question.

    This content was COPIED from BrainMass.com - View the original, and get the already-completed solution here!

    In a regression analysis, the error term ? is a random variable with a mean or expected value of

    zero

    one

    any positive value

    any value

    The equation that describes how the dependent variable (y) is related to the independent variable (x) is called

    the correlation model

    the regression model

    correlation analysis

    None of these alternatives is correct.

    For a given value of x, the estimation interval for an individual y observation is called the:

    confidence interval.

    residual.

    prediction interval.

    least-squares interval.

    standard error of estimate.

    A regression analysis between sales (in $1000) and advertising (in $100) resulted in the following least squares line: y hat = 75 +6x. This implies that if advertising is $800, then the predicted amount of sales (in dollars) is:

    $4875.

    $123,000.

    $487,500.

    $12,300.

    A least squares regression line

    may be used to predict a value of y if the corresponding x value is given

    implies a cause-effect relationship between x and y

    can only be determined if a good linear relationship exists between x and y

    None of these alternatives is correct.

    The value for SSE equals zero. This means that the coefficient of determination (r^2) must equal:

    0.0.

    -1.0.

    2.3.

    -2.3.

    1.0.

    Which of the following statements is true regarding the simple linear regression model y sub i = beta sub 0 + beta sub 1 * x sub i + epsilon sub i:

    y sub i is a value of the dependent variable (y) and x sub i is a value of the independent variable (x)

    beta sub 0 is the y-intercept of the regression line.

    beta sub 1 is the slope of the regression line.

    epsion i is a random error, or residual.

    All of the above are true statements.

    Correlation analysis is used to determine the:

    strength of the relationship between x and y.

    least squares estimates of the regression parameters.

    predicted value of y for a given value of x.

    coefficient of determination.

    An indication of no linear relationship between two variables would be:

    a coefficient of determination equal to 1.

    a coefficient of determination equal to -1.

    a coefficient of correlation of 0.

    a coefficient of correlation equal to -1.

    Both "A" and "B" are correct.

    Given the least squares regression line y hat = -2.88 + 1.77x, and a coefficient of determination of 0.81, the coefficient of correlation is:

    -0.88.

    +0.88.

    +0.90.

    -0.90.

    The residual is defined as the difference between the:

    actual value of y and the estimated value of y.

    actual value of x and the estimated value of x

    actual value of y and the estimated value of x.

    actual value of x and the estimated value of y.

    Regression analysis was applied between demand for a product (Y) and the price of the product (X), and the following estimated regression equation was obtained.
    = 120 - 10 X
    Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to

    increase by 120 units

    increase by 100 units

    increase by 20 units

    decease by 20 units

    Simple linear regression requires that the scales of measurement be expressed in either:

    nominal or ordinal.

    ordinal or ratio.

    interval or ratio.

    nominal or ratio.

    nominal or interval.

    If the coefficient of correlation is a positive value, then the regression equation

    must have a positive slope

    must have a negative slope

    could have either a positive or a negative slope

    must have a positive y intercept

    Correlation analysis is used to determine

    the equation of the regression line

    the strength of the relationship between the dependent and the independent variables

    a specific value of the dependent variable for a given value of the independent variable

    None of these alternatives is correct.

    In order to estimate with 95% confidence the expected value of y in a simple linear regression problem, a random sample of 10 observations is taken. Which of the following t-table values listed below would be used?

    2.228

    2.306

    1.860

    1.812

    If the sum of squares due to regression (SSR) is 60, which of the following must be true?

    The coefficient of correlation is 0.9.

    The total sum of squares (SST) is at least 60.

    The y-intercept is positive.

    The slope, b, is positive.

    The coefficient of determination is 0.81.

    In regression and correlation analysis, if SSE and SST are known, then with this information the

    coefficient of determination can be computed

    slope of the line can be computed

    Y intercept can be computed

    x intercept can be computed

    The regression line y hat = 3 + 2x has been fitted to the data points (4,8), (2,5), and (1,2). The residual sum of squares will be:

    10.

    15.

    13.

    22.

    The vertical spread of the data points about the regression line is measured by the:

    regression coefficient.

    standard error of estimate.

    y-intercept.

    homoscedasticity coefficient.

    t-ratio.

    © BrainMass Inc. brainmass.com March 5, 2021, 1:23 am ad1c9bdddf
    https://brainmass.com/statistics/normal-distribution/20-linear-regression-multiple-choice-questions-585954

    Solution Summary

    The solution gives detailed answers on 20 statistical multiple choice questions including the topics of regression analysis.

    $2.49

    ADVERTISEMENT