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Correlational Analysis and Linear Regression Practice Test, Exams of Advanced Education

Cheyney University of PennsylvaniaAdvanced Education

A practice test covering correlational analysis and linear regression. It includes questions and answers on topics such as measuring relationships between variables, understanding correlation coefficients, interpreting scatterplots, and applying linear regression equations. The test also covers parametric and nonparametric tests, hypothesis testing, and the limitations of correlational analyses. It is useful for students studying statistics or research methods, offering a comprehensive review of key concepts and applications in correlational analysis and linear regression. This practice test helps reinforce understanding and prepare for exams or further study in statistical analysis. It also includes information about statistical significance and apa formatting.

Typology: Exams

2025/2026

Available from 11/23/2025

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Module 7 Correlational Analysis and Linear
Regression practice test
1.
is performed to measure and describe how two quantitative
variables are
related: Correlational analysis
2. C
orrelational analysis is performed when variables are observed in
their
rather than being manipulated in a study: observed in their natural state
3.
describes the direction of a relationship, form of a
relationship (linear, curvilinear, quadratic, or cubic), and the
degree of a relationship.:
Correlation
4. describes the direction of the linear relationship between two
variables: -
Covariance
5. If two variables covary, one of two directions exist in the plot of
variables:
1.
pf3
pf4
pf5
pf8
pf9
pfa

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1 /

Module 7 Correlational Analysis and Linear

Regression practice test

  1. is performed to measure and describe how two quantitative variables are related: Correlational analysis
  2. C orrelational analysis is performed when variables are observed in their rather than being manipulated in a study: observed in their natural state
  3. describes the direction of a relationship, form of a relationship (linear, curvilinear, quadratic, or cubic), and the degree of a relationship.: Correlation
  4. describes the direction of the linear relationship between two variables: - Covariance
  5. If two variables covary, one of two directions exist in the plot of variables: 1.

2 / 2.: 1. Both variables increase or decrease together. OR

  1. As one variable increases the other decreases
  2. Covariance does not imply : Covariance does not imply causation
  3. is a limitation of correlational studies, however, correlation being estab- lished is the first step in determining : Causation, causality
  4. T he most common ways correlations are represented graphically are with , a graph in which two measurements are obtained from each individual in the study and paired variable scores are plotted as coordinate pairs.: scatterplots
  5. In scatterplots, the independent variable is on the and the dependent variable is on the : In scatterplots, the independent variable is on the x-axis and the dependent variable is on the y-axis.
  6. In scatterplots, the independent variable is called the and the dependent variable is called the :

4 /

  1. Researchers typically look for a correlation coefficient of what values before considering a relationship between two variables?: r<- 0.05 or r>0.
  2. r =0.7 to 0.9 indicates a: high correlation
  3. r= 0.5 to 0.7 indicates: moderate correlation
  4. r=0.3 to 0.5 indicates: low positive correlation
  5. r=0.0 to 0.3 indicates: very weak or negligible correlation
  6. As the r- values move closer to +1 and -1, the points resemble : straight lines
  7. results in smaller correlation coefficients and inaccurate relationship measures between two variables: Range restriction
  8. True/False: Correlational analysis is not impacted by outliers: False, correlational analysis is impacted by outliers
  9. is a correlation coefficient that is calculated to determine the degree and direction of the relationship between X and Y if a linear relationship exists.: Pear- son's r
  10. Pearson's r is a sample statistic, whereas is the population

5 / parameter: ρ "rho"

  1. Pearson's r is defined by what ratio?: r = degree to which X and Y vary together (covariability) / degree to which X and Y vary separately (separate variability)
  2. involves making specific assumptions about populations and are used when a population has a normal distribution, samples have equal variances, a linear relationship, and independent variables. Examples: ANOVA, t-tests, z-tests, Pearson's r: Parametric tests
  3. involves making no assumptions about the population parameters when data are not normal and a correlation needs to be determined: Nonparametric test aka distribution-free tests
  4. is a nonparametric test used to determine correlations, used for ordinal data or for non-normal continuous data, ie data converted to ranks, sometimes used with normal data as it is not sensitive to outliers: Spearman's ρ
  5. Outliers in correlation coefficients can cause: inaccurate conclusions to be drawn from researchers due to skewed results
  6. True/false Outliers can be a result of true values: True

7 /

  1. The best fit regression line always passes through what pair of coordiantes?- : (xK,ǰ) The mean for x, The mean for y
  2. True/false Linear regression creates a prediction, it does not guarantee exact values: True
  3. What is the regression equation?: y=bx+a b=slope a=y-intercept (x, y) = pair of coordinates
  4. In model coefficient statistical software output tables, where do you identify the slope and y-intercept? Where do you ID the statistical significance?: Under estimate, the first value is y-intecept, the second value is the slope. y=slopex+y-intercept Statistical significance is the bottom row P value

8 /

  1. In a Model Fit Measures model test, how do you identify the regression equation and statistical significance?: Calculated F ratio listed under F. determine if this exceeds the Fcrit use df1, df2. F(df1, df2) = Fcalc, p-value
  2. How do you report a regression equation in APA format?: The equation Y=slopex+y-in- tercept is/is not statistically significant in predicting Y from X. F(df1, df2) = Fcalc, p-value Example: The equation Y=32x+1 is statistically significant in predicting cost of a diamond from weight in carats. F(1,21)=32, p<0.
  3. is the amount of variation of y values that can be explained by changes in the x values. (the degree to which the variance of a variable is expressed by the best fit line_: Proportion of variance accounted for
  4. The coefficient of determination is represented by: r²
  5. The r² ratio compares: variance explained by the model/total variance, an ettect size
  6. A larger r² indicates: smaller error, more precise predictions
  7. A smaller r² indicates: larger error, less precise predictions

10 /

  1. An r-value of 0.77 and r² value of 0.604 value indicates: R value of 0.777 indicates a moderate to strong positive correlation, and r² value indicates 60.4% of the variability in the Y can be predicted from X, this is a large ettect size.
  2. involves finding the best-fit equation for three or more variables, name- ly, one response variable (dependent variable), and two or more explanatory variables (independent variables).: Multiple regression
  3. While is the parametric correlation test, is the non- parametric correlation test.: Pearson's; Spearman's
  4. What are limitations of correlational analyses?: Sensitive to outlier, cannot be used to determine causation, and require an unrestricted range
  5. If R=-.451, R^2=0.203, F=3.31, df1=1 ,df2=13, p =0. Interpret the results of this correlational analysis: Answer: There is a low positive correlation between X and Y, = 5 _ 0. 4 5 1. The ettect size is medium to large, 25 =_ 0. 2 0 3. Approximately 20.3% of the variability in Y can be predicted from the given X. This means that about 80% of the Y values are attributed to other variables. The equation is not statistically significant in

11 / predicting Y, F (1,13) = 3.31, p = 0.