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Exam 14: Bivariate Statistical Analysis: Tests of Association

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Question 21

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When the correlation between two variables is +0.52 and its associated significance level (p-value) is 0.153, it is implied that:

A) there is no relationship between the variables.
B) there is a weak positive relationship between the variables.
C) there is a moderate positive relationship between the variables.
D) there is a strong positive relationship between the variables.

E) A) and B)
F) None of the above

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Question 22

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The regression outputs for sales and number of salespeople are shown below. Model summary $\begin{array} { | l | l | r | r | r | } \hline \text { Model } & \boldsymbol { R } & \boldsymbol { R } \text {-square } & \begin{array} { c } \text { Adjusted } \\\boldsymbol { R } \text {-square }\end{array} & \begin{array} { r } \text { Std. error of } \\\text { the estimate }\end{array} \\\hline 1 & .201 \text { (a) } & .04 & .342 & 56.823 \\\hline\end{array}$ a Predictors: (Constant) , number of salespeople ANOVA(b) $\begin{array} { | l | l | c | r | r | r | r | } \hline \text { Model } & & \begin{array} { c } \text { Sum of } \\\text { squares }\end{array} &\text{ df} & \text { Mean square } & \boldsymbol { F } & { \text { Sig. } } \\\hline 1 & \text { Regression } & 77152.238 & 1 & 77152.238 & 35.117 & .057 ( \mathrm { a } ) \\\hline & \text { Residual } & 61516.962 & 28 & 2197.034 & & \\\hline & \text { Total } & 138669.200 & 29 & & & \\\hline\end{array}$ a Predictors: (Constant) , number of salespeople B Dependent variable: Sales (A$'000) Coefficients(a) $\begin{array} { | l | l | c | r | r | r | r| } \hline \text { Model } & &{ \begin{array} { c } \text { Unstandardised } \\\text { coefficients }\end{array} } & \begin{array} { c } \text { Standardised } \\\text { coefficients }\end{array} & { \boldsymbol { t } } & { \text { Sig. } } \\\hline & & \boldsymbol { B } & \text { Std. Error } & \text { Beta } & & \\\hline 1 & \text { (Constant) } & 72.612 & 9.203 & & 2.565 & .013 \\\hline & \text { Number of salespeople } & 35.623 & 3.296 & .201 & 5.926 & .064 \\\hline\end{array}$ a Dependent variable: Sales (A$'000) The above shows that:

A) for every one-unit increase in number of salespeople, average sales will increase by approximately 73 units
B) the regression results suggested a good model fit
C) the observed results occurred as a result of sampling error
D) the regression coefficient is significant

E) A) and B)
F) A) and C)

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Question 23

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In a regression equation, if the average value of Y is 15.6, the average value of X is 5.3, and the y-intercept is 8.5, then the slope is approximately:

To compute the Chi-square value for the contingency table, the researcher must first identify an expected distribution for that table.

The standard format for reporting correlational results is a(n) ___________ ______.

In a regression equation, the slope of the line  is the change in Y that occurs due to a corresponding change of one unit of X.

The Chi-square test is typically used to test for association between two interval or ratio variables.

In correlation analysis, the alternative hypothesis is typically stated as ρ ≠ 1.

The square of the correlation coefficient is called the ___________ of _____________.

The statistical significance of a correlation can be tested using the t-test.

The least-squares regression line minimises the sum of the squared deviations of the actual values from the predicted values in the regression line.

In the regression equation, ? is the: $Y = \alpha + \beta X$

If the relationship between two variables is such that both variables are caused by a third variable, then the original relationship between the first two variables is said to be:

The Pearson correlation analysis is a statistical procedure that tests for differences between two interval variables.

In a regression equation, if the average value of X is 4.6, the average value of Y is 2.3, and the slope is -1.2, then the y-intercept is approximately:

All of the following statistical tests can be used to test for associations between variables, except:

A research hypothesis states that male university students are more likely to study STEM courses than female university students. Thus, the researcher would like test to see if an association exists between gender and area of study. Which statistical test is most appropriate?

In the regression equation, is the symbol for the: $Y = \alpha + \beta X , \alpha$

If the correlation coefficient is -0.36, then the coefficient of determination is approximately:

The Pearson's correlation coefficient is a standardised measure of effect size.