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Refer to the ANOVA output below.  A business researcher is investigating the process inputs that influence late orders (measured as % late).  Which statement is FALSE?   1. Three factors are being researched simultaneously 2. There is a chance of a beta error in this analysis 3. Four different product types were considered in this research 4. Particular combinations (interactions) of Order Frequency and Quantity significantly influences late orders 5. The Order Frequency significantly influences whether an order is late

Refer to the ANOVA output below.  A business researcher is investigating the process inputs that influence late orders (measured as % late).  Which statement is FALSE?   1. Three factors are being researched simultaneously 2. There is a chance of a beta error in this analysis 3. Four different product types were considered in this research 4. Particular combinations (interactions) of Order Frequency and Quantity significantly influences late orders 5. The Order Frequency significantly influences whether an order is late

Algebra & Trigonometry with Analytic Geometry
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter3: Functions And Graphs
Section3.4: Definition Of Function
Problem 15E
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Refer to the ANOVA output below.  A business researcher is investigating the process inputs that influence late orders (measured as % late).  Which statement is FALSE?

 

1. Three factors are being researched simultaneously
2. There is a chance of a beta error in this analysis
3. Four different product types were considered in this research
4. Particular combinations (interactions) of Order Frequency and Quantity significantly influences late orders
5. The Order Frequency significantly influences whether an order is late
**General Linear Model: %LateOrder versus Quantity, Order Frequency, Product Type** ### Factors: - **Quantity** - **Type**: fixed - **Levels**: 3 - **Values**: >3, 1-3, Inquire only - **Order Frequency** - **Type**: fixed - **Levels**: 3 - **Values**: Daily, Monthly, Weekly - **Product Type** - **Type**: fixed - **Levels**: 4 - **Values**: Cable, Connector, Memory, U-joint ### Analysis of Variance for %LateOrder, using Adjusted SS for Tests: - **Source**: - **Quantity** - DF: 2 - Seq SS: 0.0004240 - Adj SS: 0.002663 - Adj MS: 0.0001331 - F: 0.35 - P: 0.716 - **Order Frequency** - DF: 2 - Seq SS: 0.0009681 - Adj SS: 0.005522 - Adj MS: 0.002761 - F: 0.72 - P: 0.513 - **Product Type** - DF: 3 - Seq SS: 0.0023840 - Adj SS: 0.004044 - Adj MS: 0.001348 - F: 0.50 - P: 0.779 - **Quantity*Order Frequency** - DF: 4 - Seq SS: 0.0058172 - Adj SS: 0.0058172 - Adj MS: 0.0014545 - F: 3.79 - P: 0.045 - **Error** - DF: 9 - Seq SS: 0.0034529 - Adj SS: 0.0034529 - Adj MS: 0.0003837 - **Total** - DF: 20 - Seq SS: 0.0111365 This table represents an analysis of variance (ANOVA)
Transcribed Image Text:**General Linear Model: %LateOrder versus Quantity, Order Frequency, Product Type** ### Factors: - **Quantity** - **Type**: fixed - **Levels**: 3 - **Values**: >3, 1-3, Inquire only - **Order Frequency** - **Type**: fixed - **Levels**: 3 - **Values**: Daily, Monthly, Weekly - **Product Type** - **Type**: fixed - **Levels**: 4 - **Values**: Cable, Connector, Memory, U-joint ### Analysis of Variance for %LateOrder, using Adjusted SS for Tests: - **Source**: - **Quantity** - DF: 2 - Seq SS: 0.0004240 - Adj SS: 0.002663 - Adj MS: 0.0001331 - F: 0.35 - P: 0.716 - **Order Frequency** - DF: 2 - Seq SS: 0.0009681 - Adj SS: 0.005522 - Adj MS: 0.002761 - F: 0.72 - P: 0.513 - **Product Type** - DF: 3 - Seq SS: 0.0023840 - Adj SS: 0.004044 - Adj MS: 0.001348 - F: 0.50 - P: 0.779 - **Quantity*Order Frequency** - DF: 4 - Seq SS: 0.0058172 - Adj SS: 0.0058172 - Adj MS: 0.0014545 - F: 3.79 - P: 0.045 - **Error** - DF: 9 - Seq SS: 0.0034529 - Adj SS: 0.0034529 - Adj MS: 0.0003837 - **Total** - DF: 20 - Seq SS: 0.0111365 This table represents an analysis of variance (ANOVA)
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