Quiz For Faculty Trial

Reviewed by Editorial Team
The ProProfs editorial team is comprised of experienced subject matter experts. They've collectively created over 10,000 quizzes and lessons, serving over 100 million users. Our team includes in-house content moderators and subject matter experts, as well as a global network of rigorously trained contributors. All adhere to our comprehensive editorial guidelines, ensuring the delivery of high-quality content.
Learn about Our Editorial Process
| By Saad Raheel
S
Saad Raheel
Community Contributor
Quizzes Created: 1 | Total Attempts: 122
| Attempts: 122
Quiz Flashcard
SettingsSettings
Please wait...
  • 1/7 Questions

    Which one of the following is the most appropriate definition of a 99% confidence interval

    • 99% of the time in repeated samples, the interval would contain the true value of the parameter
    • 99% of the time in repeated samples, the interval would contain the estimated value of the parameter
    • 99% of the time in repeated samples, the null hypothesis will be rejected
    • 99% of the time in repeated samples, the null hypothesis will not be rejected when it was false
Please wait...
About This Quiz

This quiz evaluates knowledge on statistical concepts like regression analysis, coefficient determination, and confidence intervals, suitable for faculty trials.

Quiz For Faculty Trial - Quiz

Quiz Preview

  • 2. 

    A regression analysis is inappropriate when

    • You have two variables that are measured on an interval or ratio scale.

    • You want to make predictions for one variable based on information about another variable.

    • The pattern of data points forms a reasonably straight line

    • There is heteroscedasticity in the scatter plot.

    Correct Answer
    A. There is heteroscedasticity in the scatter plot.
    Explanation
    Heteroscedasticity refers to the unequal variability of the residuals, or the differences between the observed and predicted values, across the range of the independent variable in a regression analysis. When there is heteroscedasticity in the scatter plot, it violates one of the assumptions of linear regression, which assumes that the residuals have constant variance. This means that the variability of the residuals is not consistent across the range of the independent variable. As a result, the regression analysis may produce inaccurate and unreliable estimates and predictions. Thus, when there is heteroscedasticity in the scatter plot, regression analysis is inappropriate.

    Rate this question:

  • 3. 

    The coefficient of determination is

    • Simple Linear Regression  

    • The square root of the correlation coefficient

    • Usually less than zero

    • Equal to zero

    Correct Answer
    A. Simple Linear Regression  
    Explanation
    The coefficient of determination is a measure of how well the regression line fits the data points in a simple linear regression model. It represents the proportion of the total variation in the dependent variable that is explained by the independent variable(s). The coefficient of determination is always between 0 and 1, where 0 indicates no relationship between the variables and 1 indicates a perfect fit. Therefore, the correct answer is "Simple Linear Regression."

    Rate this question:

  • 4. 

    A residual is defined as

    • The difference between the actual Y values and the mean of Y

    • The difference between the actual Y values and the predicted Y values.

    • The predicted value of Y for the average X value.

    • The square root of the slope

    Correct Answer
    A. The difference between the actual Y values and the mean of Y
    Explanation
    A residual is a measure of the difference between the observed values of the dependent variable (Y) and the predicted values of Y. In this case, the correct answer states that a residual is the difference between the actual Y values and the mean of Y. This means that it represents the deviation of each data point from the average value of Y. By calculating and analyzing residuals, we can assess the accuracy and effectiveness of a predictive model.

    Rate this question:

  • 5. 

    Least square method calculates the best-fitting line for the observed data by minimizing the sum of the squares of the _______ deviations

    • Vertical

    • Horizontal

    • Both of these

    • None of these

    Correct Answer
    A. Vertical
    Explanation
    The least square method calculates the best-fitting line for the observed data by minimizing the sum of the squares of the vertical deviations. This means that it focuses on minimizing the vertical distance between the observed data points and the line of best fit. By minimizing these vertical deviations, the least square method aims to find the line that provides the best overall fit to the data.

    Rate this question:

  • 6. 

    The standard deviation is to the mean as the ____________ is to the regression line

    • Z-score

    • SSR

    • Coefficient of determination

    • Standard error of the estimate

    • Option 5

    Correct Answer
    A. Z-score
    Explanation
    The z-score is a measure of how many standard deviations a particular data point is away from the mean. Similarly, the regression line is a line that represents the relationship between the independent variable(s) and the dependent variable in a regression analysis. The z-score and the regression line both provide information about the relationship between variables and their deviation from the mean or predicted values. Therefore, the z-score is to the mean as the regression line is to the standard error of the estimate.

    Rate this question:

  • 7. 

    In regression analysis, the variable that is being predicted is;

    • The independent variable

    •  the dependent variable

    • Usually denoted by x

    • Usually denoted by r

    Correct Answer
    A. Usually denoted by x
    Explanation
    In regression analysis, the variable that is being predicted is usually denoted by x. This means that x represents the independent variable, which is the variable that is being used to predict or explain the values of another variable. The dependent variable, on the other hand, is the variable that is being predicted or explained by the independent variable. Therefore, in regression analysis, x is the variable that is being predicted, not the dependent variable.

    Rate this question:

Quiz Review Timeline (Updated): Mar 20, 2023 +

Our quizzes are rigorously reviewed, monitored and continuously updated by our expert board to maintain accuracy, relevance, and timeliness.

  • Current Version
  • Mar 20, 2023
    Quiz Edited by
    ProProfs Editorial Team
  • Jul 14, 2020
    Quiz Created by
    Saad Raheel
Back to Top Back to top
Advertisement
×

Wait!
Here's an interesting quiz for you.

We have other quizzes matching your interest.