The correlations table below indicates that: Correlations $\begin{array}{|l|l|r|r|} \hline & & {\text { Sales }} & \begin{array}{r} \text { Advertising } \\ \text { expenditure } \end{array} \\ \hline \begin{array}{l} \text { Productivity } \\ \text { (average } \\ \text { salesper } \\ \text { month) } \end{array} & \text { Pearson correlation } & & \\ \hline & \text { Sig. (two-tailed) } & & 0.354\left(^{* *}\right) \\ \hline & N & 100 & 0.103 \\ \hline \begin{array}{l} \text { Months } \\ \text { employed } \end{array} & \text { Pearson correlation } & 0.843\left({ }^{* *}\right) & 100 \\ \hline & \text { Sig. (two-tailed) } & 0.000 & 1 \\ \hline & N & 100 & 100 \\ \hline \end{array}$ ** Correlation is significant at the 0.01 level (two-tailed).

A) about 35 per cent of variance in productivity can be explained by the variance in months employed. B) about 71 per cent of the variance in productivity can be explained by the variance in months employed. C) about 13 per cent of the variance in productivity can be explained by the variance in months employed. D) there is no association between productivity and months employed. A) about 35 per cent of variance in productivity can be explained by the variance in months employed. B) about 71 per cent of the variance in productivity can be explained by the variance in months employed. C) about 13 per cent of the variance in productivity can be explained by the variance in months employed. D) there is no association between productivity and months employed. D

In the regression equation, F1F1F1S1F1F1F10 is the: $Y = \alpha + \beta X$

A) residual error. B) independent variable. C) regression coefficient. D) standardised coefficient. A) residual error. B) independent variable. C) regression coefficient. D) standardised coefficient.

The regression output for sales and advertising spend is shown below. Model summary \[\begin{array} { | l | l | r | r | r | } \hline \text { Model } & \boldsymbol { R } & \boldsymbol { R } \text {-square } & \begin{array} { c } \text { Adjusted } \\ \boldsymbol { R } \text {-square } \end{array} & \begin{array} { r } \text { Std. error of } \\ \text { the estimate } \end{array} \\ \hline 1 & .445 \text { (a) } & .198 & .169 & 1229.780 \\ \hline \end{array}\] a Predictors: (Constant), advertising spend ANOVA(b) \[\begin{array} { | l | l | r | r | r | r | c | } \hline \text { Model } & & \begin{array} { c } \text { Sum of } \\ \text { squares } \end{array} & \text { df } & \text { Mean square } & { \boldsymbol { F } } & \text { Sig. } \\ \hline 1 & \text { Regression } & 10448599.647 & 1 & 10448599.647 & 6.909 & .014 ( \mathrm { a } ) \\ \hline & \text { Residual } & 42346067.019 & 28 & 1512359.536 & & \\ \hline & \text { Total } & 52794666.667 & 29 & & & \\ \hline \end{array}\] a Predictors: (Constant), advertising spend B Dependent variable: Sales (A$'000) Coefficients(a) \[\begin{array} { | l | l | cr | r | r | r |} \hline\text { Model } & & { \begin{array} { c } \text { Unstandardised } \\ \text { coefficients } \end{array} } && \begin{array} { c } \text { Standardised } \\ \text { coefficients } \end{array} & { \boldsymbol { t } } & { \text { Sig. } } \\ \hline & & \boldsymbol { B } & \text { Std. Error } & \text { Beta } & & \\ \hline 1& \text { (Constant) } & 2993.298 & 706.247 & & 4.238 & .000 \\ \hline & \text { Number of salespeople } & 42.928 & 16.332 & .445 & 2.628 & .014 \\ \hline \end{array}\] a Dependent variable: Sales (A$'000) The above shows that:

A) approximately 45 per cent of the variance in sales can be explained by advertising spend. B) approximately 43 per cent of the variance in Sales can be explained by advertising spend. C) approximately 14 per cent of the variance in sales can be explained by advertising spend. D) approximately 20 per cent of the variance in sales can be explained by advertising spend. A) approximately 45 per cent of the variance in sales can be explained by advertising spend. B) approximately 43 per cent of the variance in Sales can be explained by advertising spend. C) approximately 14 per cent of the variance in sales can be explained by advertising spend. D) approximately 20 per cent of the variance in sales can be explained by advertising spend.

Exam 14: Bivariate Statistical Analysis: Tests of Association

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:

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(Multiple Choice)

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Question 1

Correct Answer:

Verified

C

A researcher would like to predict sales volume against advertising dollar expenditure. Which of the following statistical tests would you suggest?

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(Multiple Choice)

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Question 2

Correct Answer:

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D

The correlations table below indicates that: Correlations Sales Advertising expenditure Productivity (average salesper month) Pearson correlation Sig. (two-tailed) 0.354 N 100 0.103 Months employed Pearson correlation 0.843 100 Sig. (two-tailed) 0.000 1 N 100 100 ** Correlation is significant at the 0.01 level (two-tailed).

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(Multiple Choice)

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Question 3

Correct Answer:

Verified

D

Which of the following statements is true?

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Question 4

To determine the proportion of variance in the dependent variable that is explained by the independent variable, which of the following needs to be derived?

(Multiple Choice)

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Question 5

Which type of statistical test is appropriate for testing whether or not there is an association between two ordinal variables?

(Multiple Choice)

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Question 6

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

(True/False)

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Question 7

A Spearman's rank-order correlation coefficient is a technique used when determining the correlation between two _______ scaled variables.

(Multiple Choice)

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Question 8

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

(True/False)

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Question 9

Regression is a statistical technique for measuring the curvilinear association between a dependent and independent variable.

(True/False)

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Question 10

'Test of association' is a general term that refers to a number of bivariate statistical techniques used to measure whether or not two variables are associated with each other.

(True/False)

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Question 11

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

(True/False)

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Question 12

What is the most common cut-off significance value for accepting or rejecting hypothesis?

(Multiple Choice)

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Question 13

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

(Multiple Choice)

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Question 14

An F-test can be applied to a regression to determine the residual error.

(True/False)

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Question 15

The regression output for sales and advertising spend is shown below. Model summary Model -square Adjusted -square Std. error of the estimate 1 .445 (a) .198 .169 1229.780 a Predictors: (Constant), advertising spend ANOVA(b) Model Sum of squares df Mean square Sig. 1 Regression 10448599.647 1 10448599.647 6.909 .014() Residual 42346067.019 28 1512359.536 Total 52794666.667 29 a Predictors: (Constant), advertising spend B Dependent variable: Sales (A$'000) Coefficients(a) Model Unstandardised coefficients Standardised coefficients Sig. Std. Error Beta 1 (Constant) 2993.298 706.247 4.238 .000 Number of salespeople 42.928 16.332 .445 2.628 .014 a Dependent variable: Sales (A$'000) The above shows that:

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Question 16

If the value of r = 0, there is a perfect positive relationship between the two variables under study.

(True/False)

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Question 17

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?

(Multiple Choice)

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Question 18

One of the simplest techniques for describing sets of relationships between two interval variables is the cross-tabulation.

(True/False)

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Question 19

A correlation analysis can be used to ascertain whether or not gender is related to brand awareness.

(True/False)

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Question 20

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:

A researcher would like to predict sales volume against advertising dollar expenditure. Which of the following statistical tests would you suggest?

Which of the following statements is true?

To determine the proportion of variance in the dependent variable that is explained by the independent variable, which of the following needs to be derived?

Which type of statistical test is appropriate for testing whether or not there is an association between two ordinal variables?

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

A Spearman's rank-order correlation coefficient is a technique used when determining the correlation between two _______ scaled variables.

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

Regression is a statistical technique for measuring the curvilinear association between a dependent and independent variable.

'Test of association' is a general term that refers to a number of bivariate statistical techniques used to measure whether or not two variables are associated with each other.

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

What is the most common cut-off significance value for accepting or rejecting hypothesis?

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

An F-test can be applied to a regression to determine the residual error.

If the value of r = 0, there is a perfect positive relationship between the two variables under study.

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?

One of the simplest techniques for describing sets of relationships between two interval variables is the cross-tabulation.

A correlation analysis can be used to ascertain whether or not gender is related to brand awareness.

The Role of Marketing Research and the Research Process

Problem Definition and the Research Process

Qualitative Research

Secondary Research and Big Data

Survey Research

Observation

Experimental Research and Test Marketing

Measurement

Questionnaire Design

Sampling: Sample Design and Sample Size

Data Preparation

Univariate Statistical Analysis: a Recap of Inferential Statistics

Bivariate Statistical Analysis: Tests of Differences

Multivariate Statistical Analysis

Communicating Research Results: Research Report, Oral Presentation and Research Follow-Up

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Exam 14: Bivariate Statistical Analysis: Tests of Association

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:

A researcher would like to predict sales volume against advertising dollar expenditure. Which of the following statistical tests would you suggest?

Which of the following statements is true?

To determine the proportion of variance in the dependent variable that is explained by the independent variable, which of the following needs to be derived?

Which type of statistical test is appropriate for testing whether or not there is an association between two ordinal variables?

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

A Spearman's rank-order correlation coefficient is a technique used when determining the correlation between two _______ scaled variables.

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

Regression is a statistical technique for measuring the curvilinear association between a dependent and independent variable.

'Test of association' is a general term that refers to a number of bivariate statistical techniques used to measure whether or not two variables are associated with each other.

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

What is the most common cut-off significance value for accepting or rejecting hypothesis?

In the regression equation,  is the: Y=α+βXY = \alpha + \beta XY=α+βX

An F-test can be applied to a regression to determine the residual error.

If the value of r = 0, there is a perfect positive relationship between the two variables under study.

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?

One of the simplest techniques for describing sets of relationships between two interval variables is the cross-tabulation.

A correlation analysis can be used to ascertain whether or not gender is related to brand awareness.

The Role of Marketing Research and the Research Process

Problem Definition and the Research Process

Qualitative Research

Secondary Research and Big Data

Survey Research

Observation

Experimental Research and Test Marketing

Measurement

Questionnaire Design

Sampling: Sample Design and Sample Size

Data Preparation

Univariate Statistical Analysis: a Recap of Inferential Statistics

Bivariate Statistical Analysis: Tests of Differences

Multivariate Statistical Analysis

Communicating Research Results: Research Report, Oral Presentation and Research Follow-Up

Filters

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