On the two SPSS outputs shown below, with \( a=.05 \), you should conclude... One-Sample T-Test ons smint \begin{tabular}{|l|r|c|r|r|} \hline & \( N \) & Nean & Sac. Devation & \begin{tabular}{c} Ss1 Errar \\ Nean \end{tabular} \\ \hline onesample & 10 & 450000 & 4.39697 & 1.39044 \\ \hline \end{tabular} One-Sample Test Independent T-Test \begin{tabular}{|c|c|c|c|c|c|} \hline \multicolumn{6}{|c|}{ Group Stariatics } \\ \hline & hr & \( \mathrm{N} \) & Mean & St1. Douration & \begin{tabular}{c} Sas Enor \\ Mean \end{tabular} \\ \hline ?r & 1.00 & 5 & 45000 & 114018 & 50990 \\ \hline & 200 & 5 & 7.0000 & 1.22474 & 54772 \\ \hline \end{tabular} Independent Samples Test \begin{tabular}{|c|c|c|c|c|c|c|} \hline & \multicolumn{2}{|c|}{\begin{tabular}{l} Leveney Testfor Equaity of \\ Varianc4s \end{tabular}} & \multirow[b]{2}{*}{\( \mathrm{t} \)} & \multirow[b]{2}{*}{ dt } & \multirow[b]{2}{*}{ sig. (2 taited) } \\ \hline & & \multirow[t]{2}{*}{ F } & \( \mathrm{sig} \) & & & \\ \hline\( d t \) & \begin{tabular}{l} Equal ranianc9s \\ assumed \end{tabular} & & \multirow[t]{2}{*}{.865} & -3.207 & 8 & .012 \\ \hline & \begin{tabular}{l} Equal varlances net \\ assumed \end{tabular} & & & -3.207 & 7.959 & .013 \\ \hline \end{tabular} FTR HO for the One-Sample ttest, FTR HO for the Independent-Samples t-test FTR HO for the One-Sample ttest, Reject HO for the Independent-Samples t-test Reject HO for the One-Sample ttest, Reject HO for the Independent-Samples t-test Reject HO for the One-Sample ttest, FTR HO for the Independent-Samples t-test
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- The first output is from a One-Sample T-Test, which tests whether the mean of a single sample differs significantly from a hypothesized value. The output shows the sample size (N = 10), the sample mean (450,000), the standard deviation (4.39697), and the Show more…
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Below is an SPSS output for an Independent Samples t Test. What can you conclude based on the value of .062 in this output? Independent-Samples Test Levene's Test for Equality of Variances t-Test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference VAR00001 Equal variances assumed 3.51 .062 1.622 434 .105 .847 Equal variances not assumed 1.661 422.35 .098 .847 Question 50 options: - Equal variances can be assumed for the t test - Equal variances cannot be assumed for the t test - The outcome of the t test is significant (reject H0) - None of these
Kirsty G.
Presented below are the results from an SPSS analysis of the facial composite study as if it were conducted as a within-groups design, with 26 participants creating composites both alone and when paired with another participant. Again, the researcher asks if the individually-created composites were rated significantly lower on similarity than those created when working in pairs. Note that the significance level provided in the SPSS output is for a 2-tailed test. Since you are doing a 1-tailed test, you must divide that significance level in half. T-TEST PAIRS=Paired WITH Alone (PAIRED) /CRITERIA=CI(.9500) /MISSING=ANALYSIS. T-Test Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pair 1 Paired 5.00 26 1.744 .342 Alone 4.27 26 1.485 .291 Paired Samples Correlations N Correlation Sig. Pair 1 Paired & Alone 26 .216 .289 Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Paired - Alone .731 2.031 .398 -.090 1.551 1.835 25 .078 Based on the information provided in the SPSS output, provide a concluding statement which includes the APA formatted notation regarding the obtained t value (to two decimal places) and its significance (exact probability value, to three decimal places). Although we have the same Mean Difference (0.731) as when this t-test was calculated as a between-groups design (in Question 5), the obtained t value is different (and the decision regarding the significance of the test may be different). Why does this difference exist? (Hint: compare the standard errors in each case, standard error not given). Does this reflect an advantage or a disadvantage of a repeated measures design over an independent groups design?
Sri K.
In a hypothesis test for ANOVA, you are interested in the significance of the difference between (samples, population variances, sample variances, population means). You assume that you have (unbiased, isolated, independent, dependent) (unequal, stratified, minimal, random) samples from populations that are (exponentially, unequally, normally, equally) distributed with (random, independent, equal, unequal) variances. The test is designed to be used with (nominal, interval-ratio, ordinal, numerical) level dependent variables. What is the null hypothesis in an ANOVA? a. H0: Not all population means are equal. b. H0: All population means are different. c. H0: All population means are equal. d. H0: Some of the population means are equal. What is the alternative hypothesis in an ANOVA? a. H1: At least one of the population means is different. b. H1: All population means are equal. c. H1: All population means are different. d. H1: Some of the population means are equal. The F-test statistic is formed by taking the (product, sum, ratio) of two separate estimates of (correlation, variance, standard deviation, mean), where the estimate in the numerator is derived from the (sum of the variables, overall average, variation between categories, variation within categories) and the estimate in the denominator is derived from the (sum of the variables, overall average, variation between categories, variation within categories). The sampling distribution is the (p, z, F, t) distribution with (N - k, N - 1, k - 1, N) degrees of freedom within categories and (N - k, N - 1, k - 1, N) degrees of freedom between categories. Once you compute the F(obtained) statistic for your data, you compare its value with F(obtained, dependent, alternative, critical) determined by the given alpha level and the degrees of freedom. If the test statistic is in the critical region, you (confirm, reject, support, reevaluate) the null hypothesis and conclude that there (is/is not) a significant difference between the means.
Dominador T.
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