Traditional course condensed 2 Traditional course regular length 3 Online

Traditional course condensed 2 traditional course

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Traditional course condensed 2. Traditional course regular length 3. Online course condensed 4. Online course regular length Chap 11-53 DCOV A
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Excel Analysis Of Collapsed Data Chap 11-54 DCOV A Group is a significant effect. p-value of 0.0003 < 0.05 1. Traditional regular > Traditional condensed 2. Online condensed > Traditional condensed 3. Traditional regular > Online regular 4. Online condensed > Online regular If the course is take online should use the condensed version and if the course is taken by traditional method should use the regular.
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The Randomized Block Design Is Often Useful The randomized block design is an on-line topic Chap 11-55
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Chap 11-56 Chapter Summary In this chapter we discussed The one-way analysis of variance The logic of ANOVA ANOVA assumptions F test for difference in c means The Tukey-Kramer procedure for multiple comparisons The Levene test for homogeneity of variance The two-way analysis of variance Examined effects of multiple factors Examined interaction between factors
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RBD - 1 Online Topic The Randomized Block Design Statistics for Managers Using Microsoft Excel 7 th Edition
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RBD - 2 Learning Objective To learn the basic structure and use of a randomized block design
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RBD - 3 The Randomized Block Design Like One-Way ANOVA, we test for equal population means (for different factor levels, for example)... ...but we want to control for possible variation from a second factor (with two or more levels) Levels of the secondary factor are called blocks DCOV A
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RBD - 4 Partitioning the Variation Total variation can now be split into three parts: SST = Total variation SSA = Among-Group variation SSBL = Among-Block variation SSE = Random variation SST = SSA + SSBL + SSE DCOV A
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RBD - 5 Sum of Squares for Blocks Where: c = number of groups r = number of blocks X i. = mean of all values in block i X = grand mean (mean of all data values) r 1 i 2 i. ) X X ( c SSBL SST = SSA + SSBL + SSE DCOV A
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RBD - 6 Partitioning the Variation Total variation can now be split into three parts: SST and SSA are computed as they were in One-Way ANOVA SST = SSA + SSBL + SSE SSE = SST – (SSA + SSBL) DCOV A
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RBD - 7 Mean Squares 1 c SSA groups among square Mean MSA 1 r SSBL blocking square Mean MSBL ) 1 )( 1 ( c r SSE MSE error square Mean DCOV A
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RBD - 8 Randomized Block ANOVA Table Source of Variation df SS MS Among Groups SSA MSA Error (r–1)(c-1) SSE MSE Total rc - 1 SST c - 1 MSA MSE F c = number of populations rc = total number of observations r = number of blocks df = degrees of freedom Among Blocks SSBL r - 1 MSBL MSBL MSE DCOV A
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RBD - 9 Main Factor test: df 1 = c – 1 df 2 = (r – 1)(c – 1) MSA MSE c . .3 .2 .1 0 μ μ μ μ : H equal are means population all Not : H 1 F STAT = Reject H 0 if F STAT > F α Testing For Factor Effect DCOV A
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RBD - 10 Test For Block Effect Blocking test: df 1 = r – 1 df 2 = (r – 1)(c – 1) MSBL MSE r. 3. 2. 1. 0
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