A Consumer Group Wanted to Determine If There Was a Difference

Question

Examlex · Accepted Answer

The Answer is Procedure:
Create a data set with two variables, Prices and Retailer. The data set should have 15 cases, each case representing one store.
Go to Analyze

ightarrow

General Linear Model

ightarrow

Univariate. Select Prices as the Dependent Variable and Retailer as the Fixed Factor. Go to Plot and select Retailer to the Horizontal Axis, then Add, to get profile plot. Go to Save and check Unstandardized under Residuals to save model residuals. Go to Options. Check Estimates of effect size to get effect size estimates. Check Homogeneity tests to examine the assumption of homoscedasticity.
To examine the assumption of independence, go to Graphs

ightarrow

Legacy Dialogs

ightarrow

Scatter/Dot

ightarrow

Simple Scatter

ightarrow

Define. Select RES_1 as the Y Axis, and Retailer as the X Axis. To examine the assumption of normality, go to Analyze

ightarrow

Descriptive Statistics

ightarrow

Explore. Select RES_1 to Dependent List. Go to Plots, and check Normality plots with tests.
Selected SPSS output:

ext { Tests of Between-Subjects Effects }

ext { Dependent Variable: Frices }

\begin{array}{ccccccc}\hline ext { Source } & \begin{array}{c} ext { Type III Sum of } \ ext { Squares }\end{array} & ext { df } & ext { Mean Square } & ext { F } & ext { Sig. } & \begin{array}{c} ext { Partial Eta } \ ext { Squared }\end{array} \\hline ext { Retailer } & 72.933 & 2 & 36.467 & 16.328 & .000 & .731 \ ext { Error } & 26.800 & 12 & 2.233 & & & \ ext { Corrected Total } & 99.733 & 14 & & & & \\hline\end{array}

ext { Levene's Test of Equality of Error Variances }

ext { Dependent } V ext { ariable: Prices }

\begin{array}{llll}\hline ext { F } & ext { dfl } & ext { df2 } & ext { Sig. } \\hline .467 & 2 & 12 & .638\\hline \end{array}

Profile plot
$Procedure: Create a data set with two variables, Prices and Retailer. The data set should have 15 cases, each case representing one store. Go to Analyze ightarrow General Linear Model ightarrow Univariate. Select Prices as the Dependent Variable and Retailer as the Fixed Factor. Go to Plot and select Retailer to the Horizontal Axis, then Add, to get profile plot. Go to Save and check Unstandardized under Residuals to save model residuals. Go to Options. Check Estimates of effect size to get effect size estimates. Check Homogeneity tests to examine the assumption of homoscedasticity. To examine the assumption of independence, go to Graphs ightarrow Legacy Dialogs ightarrow Scatter/Dot ightarrow Simple Scatter ightarrow Define. Select RES_1 as the Y Axis, and Retailer as the X Axis. To examine the assumption of normality, go to Analyze ightarrow Descriptive Statistics ightarrow Explore. Select RES_1 to Dependent List. Go to Plots, and check Normality plots with tests. Selected SPSS output: ext { Tests of Between-Subjects Effects } ext { Dependent Variable: Frices } \begin{array}{ccccccc} \hline ext { Source } & \begin{array}{c} ext { Type III Sum of } \ ext { Squares } \end{array} & ext { df } & ext { Mean Square } & ext { F } & ext { Sig. } & \begin{array}{c} ext { Partial Eta } \ ext { Squared } \end{array} \ \hline ext { Retailer } & 72.933 & 2 & 36.467 & 16.328 & .000 & .731 \ ext { Error } & 26.800 & 12 & 2.233 & & & \ ext { Corrected Total } & 99.733 & 14 & & & & \ \hline \end{array} ext { Levene's Test of Equality of Error Variances } ext { Dependent } V ext { ariable: Prices } \begin{array}{llll} \hline ext { F } & ext { dfl } & ext { df2 } & ext { Sig. } \ \hline .467 & 2 & 12 & .638\ \hline \end{array} Profile plot Residual plot by group Q-Q plot of residuals A one-way ANOVA was conducted to determine if the prices of a certain type of toy differed in three major retail stores. The Q-Q plot of residuals showed that the points clustered close to the diagonal line, suggesting that the assumption of normality was reasonable. According to Levene's test, the homogeneity of variance assumption was satisfied [F(2, 12) = .467, p = .638]. The scatterplot of residuals against the levels of the independent variable demonstrated a random display of points around 0, providing evidence to the assumption of independence being satisfied. From the ANOVA summary table, we see that the prices are significantly different across the three retailers (F = 16.328, df = 2,12, p <.001), the effect size is rather large ( \eta 2 = .731; suggesting about 73.1% of the variance in prices is accounted for by the differences in retailers). The profile plot suggested that the price is the lowest in W-Mart, higher in Tag, and the highest in URToy.$ Residual plot by group
$Procedure: Create a data set with two variables, Prices and Retailer. The data set should have 15 cases, each case representing one store. Go to Analyze ightarrow General Linear Model ightarrow Univariate. Select Prices as the Dependent Variable and Retailer as the Fixed Factor. Go to Plot and select Retailer to the Horizontal Axis, then Add, to get profile plot. Go to Save and check Unstandardized under Residuals to save model residuals. Go to Options. Check Estimates of effect size to get effect size estimates. Check Homogeneity tests to examine the assumption of homoscedasticity. To examine the assumption of independence, go to Graphs ightarrow Legacy Dialogs ightarrow Scatter/Dot ightarrow Simple Scatter ightarrow Define. Select RES_1 as the Y Axis, and Retailer as the X Axis. To examine the assumption of normality, go to Analyze ightarrow Descriptive Statistics ightarrow Explore. Select RES_1 to Dependent List. Go to Plots, and check Normality plots with tests. Selected SPSS output: ext { Tests of Between-Subjects Effects } ext { Dependent Variable: Frices } \begin{array}{ccccccc} \hline ext { Source } & \begin{array}{c} ext { Type III Sum of } \ ext { Squares } \end{array} & ext { df } & ext { Mean Square } & ext { F } & ext { Sig. } & \begin{array}{c} ext { Partial Eta } \ ext { Squared } \end{array} \ \hline ext { Retailer } & 72.933 & 2 & 36.467 & 16.328 & .000 & .731 \ ext { Error } & 26.800 & 12 & 2.233 & & & \ ext { Corrected Total } & 99.733 & 14 & & & & \ \hline \end{array} ext { Levene's Test of Equality of Error Variances } ext { Dependent } V ext { ariable: Prices } \begin{array}{llll} \hline ext { F } & ext { dfl } & ext { df2 } & ext { Sig. } \ \hline .467 & 2 & 12 & .638\ \hline \end{array} Profile plot Residual plot by group Q-Q plot of residuals A one-way ANOVA was conducted to determine if the prices of a certain type of toy differed in three major retail stores. The Q-Q plot of residuals showed that the points clustered close to the diagonal line, suggesting that the assumption of normality was reasonable. According to Levene's test, the homogeneity of variance assumption was satisfied [F(2, 12) = .467, p = .638]. The scatterplot of residuals against the levels of the independent variable demonstrated a random display of points around 0, providing evidence to the assumption of independence being satisfied. From the ANOVA summary table, we see that the prices are significantly different across the three retailers (F = 16.328, df = 2,12, p <.001), the effect size is rather large ( \eta 2 = .731; suggesting about 73.1% of the variance in prices is accounted for by the differences in retailers). The profile plot suggested that the price is the lowest in W-Mart, higher in Tag, and the highest in URToy.$ Q-Q plot of residuals
$Procedure: Create a data set with two variables, Prices and Retailer. The data set should have 15 cases, each case representing one store. Go to Analyze ightarrow General Linear Model ightarrow Univariate. Select Prices as the Dependent Variable and Retailer as the Fixed Factor. Go to Plot and select Retailer to the Horizontal Axis, then Add, to get profile plot. Go to Save and check Unstandardized under Residuals to save model residuals. Go to Options. Check Estimates of effect size to get effect size estimates. Check Homogeneity tests to examine the assumption of homoscedasticity. To examine the assumption of independence, go to Graphs ightarrow Legacy Dialogs ightarrow Scatter/Dot ightarrow Simple Scatter ightarrow Define. Select RES_1 as the Y Axis, and Retailer as the X Axis. To examine the assumption of normality, go to Analyze ightarrow Descriptive Statistics ightarrow Explore. Select RES_1 to Dependent List. Go to Plots, and check Normality plots with tests. Selected SPSS output: ext { Tests of Between-Subjects Effects } ext { Dependent Variable: Frices } \begin{array}{ccccccc} \hline ext { Source } & \begin{array}{c} ext { Type III Sum of } \ ext { Squares } \end{array} & ext { df } & ext { Mean Square } & ext { F } & ext { Sig. } & \begin{array}{c} ext { Partial Eta } \ ext { Squared } \end{array} \ \hline ext { Retailer } & 72.933 & 2 & 36.467 & 16.328 & .000 & .731 \ ext { Error } & 26.800 & 12 & 2.233 & & & \ ext { Corrected Total } & 99.733 & 14 & & & & \ \hline \end{array} ext { Levene's Test of Equality of Error Variances } ext { Dependent } V ext { ariable: Prices } \begin{array}{llll} \hline ext { F } & ext { dfl } & ext { df2 } & ext { Sig. } \ \hline .467 & 2 & 12 & .638\ \hline \end{array} Profile plot Residual plot by group Q-Q plot of residuals A one-way ANOVA was conducted to determine if the prices of a certain type of toy differed in three major retail stores. The Q-Q plot of residuals showed that the points clustered close to the diagonal line, suggesting that the assumption of normality was reasonable. According to Levene's test, the homogeneity of variance assumption was satisfied [F(2, 12) = .467, p = .638]. The scatterplot of residuals against the levels of the independent variable demonstrated a random display of points around 0, providing evidence to the assumption of independence being satisfied. From the ANOVA summary table, we see that the prices are significantly different across the three retailers (F = 16.328, df = 2,12, p <.001), the effect size is rather large ( \eta 2 = .731; suggesting about 73.1% of the variance in prices is accounted for by the differences in retailers). The profile plot suggested that the price is the lowest in W-Mart, higher in Tag, and the highest in URToy.$ A one-way ANOVA was conducted to determine if the prices of a certain type of toy differed in three major retail stores. The Q-Q plot of residuals showed that the points clustered close to the diagonal line, suggesting that the assumption of normality was reasonable. According to Levene's test, the homogeneity of variance assumption was satisfied [F(2, 12) = .467, p = .638]. The scatterplot of residuals against the levels of the independent variable demonstrated a random display of points around 0, providing evidence to the assumption of independence being satisfied. From the ANOVA summary table, we see that the prices are significantly different across the three retailers (F = 16.328, df = 2,12, p <.001), the effect size is rather large (

\eta

² = .731; suggesting about 73.1% of the variance in prices is accounted for by the differences in retailers). The profile plot suggested that the price is the lowest in W-Mart, higher in Tag, and the highest in URToy.

A Consumer Group Wanted to Determine If There Was a Difference

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A Consumer Group Wanted to Determine If There Was a Difference

Essay

Correct Answer:VerifiedShow Answer

Correct Answer:
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