SlideShare a Scribd company logo
1 of 29
Factors Influencing the Price of Florida Bell Peppers Presented by: BikramAulakh and Catherine Klasne
Introduction Imagine you are a typical South Florida farmer with a decision to make about what to plant on your 195 acres.
Should you grow bell peppers and, if so, how many should you aim to produce?  Beyond pure judgment, is there a way to make these choices, and what model-based formulas or equations would aid in decision-making? What factors influence the price of Floridabell peppers? Considerations
Statistics can help! Depending on how much data can be gathered by farms’ marketing and research personnel, and how much time they have to work with it – and on how well they recognize trends and use them to forecast – our models can provide insight.
Our data We found 37 complete observations we could manipulate with ordinary-least-squares software to arrive at useful models that should hold for any year under consideration.
The unit of observation  The state of Florida in one year.
The time period covered: 1960 to 2006.
Variables in the following classifications were tested:1. Supply U.S. annual production of bell peppers, U.S. annual imports and U.S. total annual supply, all in millions of pounds U.S. annual exports in millions of pounds U.S. acreage planted in bell peppers and U.S.acreage on which bell peppers were harvested annually Independent variables:
More supply-related variables: Total annual U.S. yield in hundredweights	 Florida acreage planted annually in bell peppers and acreage within the state on which bell peppers were harvested annually Florida’s total production in thousands of hundredweights (or yield in hundredweights) Independent variables:
2. Demand U.S. domestic consumption of bell peppers in millions of pounds annually Average U.S. population in millions U.S. annual per capita use in pounds Independent variables:
3. Related pricing Average U.S. price of bell peppers per hundredweight U.S. farm value per unit in dollars per hundredweight Average price of bell peppers in dollars per hundredweight in a competing state, such as California Independent variables:
Data sources:  Lucier & Toland, U.S. Bell and Chile Pepper Statistics, Stock #2007-001, 2008. This data set contains 67 tables concerning bell peppers, “also known as green, sweet or nonpungent,” in the U.S. and various states, covering acreage, yield, production, price, crop value and per capita use.
Regression estimations A brief trip through our model iterations
MINITAB eliminated three of the possible independent variables for which data existed, leaving us with an equation that contained a constant and 19 other X’s: FL farm value per unit ($/cwt) = 25.8 - 21.9 US production (Mlbs) - 21.9 US imports (Mlbs) + 21.9 US exports (Mlbs) +21.9US domestic use (Mlbs) + 2.17 US price ($/cwt)- 0.136 US population (Millions) - 11.4 US per capita use (lbs)+ 0.000121 US planted acreage + 0.000290 US harvested acreage+ 0.109 US yield (cwt) + 0.000596 FL planted acreage                             - 0.00137 FL harvested acreage  + 0.00452 FL production (1000 cwt) - 0.0683 FL yield (cwt) - 0.00255 Cal planted acreage +0.00253 Cal harvested acreage  + 0.00195 Cal production(1000 cwt) - 0.0135 Cal yield (cwt)  - 1.09 Cal farm valueper unit ($/cwt) Initial model
Comment The model passes the F-test with a p-value of 0.000, less than the tolerated maximum value of .05, probably meaning at least one of the independent variables affects the dependent variable. The adjusted R2is 99.4% -- variations in  the independent variables probably explain 99.4% of the variation in the dependent variable, Florida farm value in units of dollars perhundredweight. Only about 0.6% of the variation in Y is left to be explained. Coefficient signs indicate a (+) positive or direct relationship between an X and the dependent variable or a (-) negative or inverse relationship between the particular X and the Y, or dependent variable.
In reviewing the analysis based on p-values and rho, we decided to weed out 13 more variables. Refining the model
We plotted the 19 IV’s individually against the DV; most plots were nonlinear, with the exception of that for US price against FL farm value per unit. Correct functional form? Omitted variables?
Nonlinear v. Linear We successfully converted the other 18 relationships into linear or straight-line graphs.
Our correlation matrix helped us to decide to keep one of the variables. Multicollinearity
We realized that even 19 variables could be an unwieldy model for practical purposes and wanted to drop some of them. However, the variance inflation factor is greater than 10 for all of the independent variables, meaning they all could be considered for our final model. Multicollinearity
With a constant and 6 independent variables, the equation looks like this: FL farm value per unit ($/cwt) = 3.70+ 0.00366 US imports (Mlbs)+ 2.27 US price ($/cwt)+ 0.633 US per capita use (lbs)+ 0.000344 FL planted acreage- 0.000835 FL harvested acreage                                 - 1.21 Cal farm value per unit ($/cwt) Final model
The adjusted R2 of 97.3% means the variation in the independent variables for the final model probably explains 97.3% of the variation in the dependent variable; about 2.7% remains to be explained. Based on the equation and individual T-test p-values, we can say:+ 0.00366 US imports (Mlbs): For every increase of 1 million pounds of imported bell peppers, we can be 55.9% confident the price of purchasing a hundredweight (100 pounds) of bell peppers from a Florida farmer rises by about 4 cents on averagefor that year. Final model evaluation
Interpretation of coefficients continued:+ 0.633US per capita use (lbs): For every increase of  1 pound in per capita consumption of bell peppers in the United States, Florida farmers will be paid an average of 63 cents more for a hundredweight of bell peppers (75.3% confidence).+ 0.000344 FL planted acreage: For every additional Florida acre planted in bell peppers, Florida farmers will be paid about 1/3 cent more for a hundredweight of bell peppers (64.5% confidence). Final model evaluation
Interpretation of coefficients continued: - 0.000835 FL harvested acreage: For every 1-acre increase in Florida land on which bell peppers are harvested, the Florida farmers’ price falls by less than a tenth of a cent per hundredweight (93.4% confidence).- 1.21 Cal farm value per unit ($/cwt): For each additional dollar California farmers are paid for a hundredweight of bell peppers, Florida farmers’are paid $1.21 less (nearly 100% confidence). Final model evaluation
Additional analysis shows that we could further refine our model, but we believe the VIF values favor keeping all six remaining X’s. Final model evaluation
Summary A useful and highly relevant model
All of our models, generally, are highly relevant. By plugging the required values into our equations, Florida bell-pepper farmers can achieve results that will be of use in determining, for example: How many bell peppers to aim at growing and reasonable asking prices.  How many laborers to hire to harvest this quantity. How much fertilizer, pesticide and other supplies to purchase. The factors that influence the price of bell peppers produced on Florida farms. We have shown, in a quantifiable way, the direction and strength of these influences – instructive  information for the state’s major bell pepper growers.
Our study answered our major questions well and provided some additional insights.

More Related Content

Similar to Factors influencing the price of florida bell peppers (for sharing)

Higher Input Prices Result in Greater Economic Incentives for Precision Agric...
Higher Input Prices Result in Greater Economic Incentives for Precision Agric...Higher Input Prices Result in Greater Economic Incentives for Precision Agric...
Higher Input Prices Result in Greater Economic Incentives for Precision Agric...nacaa
 
The Organic Advantage by pro cert-volume
The Organic Advantage by pro cert-volumeThe Organic Advantage by pro cert-volume
The Organic Advantage by pro cert-volumeCarrie Hamm
 
Copy of Theory of demand and Elasticity (2).pptx
Copy of Theory  of  demand  and Elasticity  (2).pptxCopy of Theory  of  demand  and Elasticity  (2).pptx
Copy of Theory of demand and Elasticity (2).pptxCeddiaTaylor1
 
Final Research Poster
Final Research PosterFinal Research Poster
Final Research PosterJade Harrison
 
Farm Bill presentation, University of Minnesota 11-10-16
Farm Bill presentation, University of Minnesota 11-10-16Farm Bill presentation, University of Minnesota 11-10-16
Farm Bill presentation, University of Minnesota 11-10-16Brad Jordahl Redlin
 
Julian R - Using the EcoCrop model and database to forecast impacts of cc
Julian R - Using the EcoCrop model and database to forecast impacts of ccJulian R - Using the EcoCrop model and database to forecast impacts of cc
Julian R - Using the EcoCrop model and database to forecast impacts of ccCIAT
 
TitleABC123 Version X1Part 3 Inferential Statist.docx
TitleABC123 Version X1Part 3 Inferential Statist.docxTitleABC123 Version X1Part 3 Inferential Statist.docx
TitleABC123 Version X1Part 3 Inferential Statist.docxherthalearmont
 
Food Miles: Background and Marketing
Food Miles: Background and MarketingFood Miles: Background and Marketing
Food Miles: Background and MarketingGardening
 
Low Emissions Development Strategies (Colombia Feb 20, 2014)
Low Emissions Development Strategies (Colombia Feb 20, 2014)Low Emissions Development Strategies (Colombia Feb 20, 2014)
Low Emissions Development Strategies (Colombia Feb 20, 2014)IFPRI-EPTD
 
2014 farm bill powerpoint bjr10-16-14
2014 farm bill   powerpoint bjr10-16-142014 farm bill   powerpoint bjr10-16-14
2014 farm bill powerpoint bjr10-16-14Brad Jordahl Redlin
 
Potential Economic Impact of Sustainable Biomass Feedstock for Pennsylvania
Potential Economic Impact of Sustainable Biomass Feedstock for PennsylvaniaPotential Economic Impact of Sustainable Biomass Feedstock for Pennsylvania
Potential Economic Impact of Sustainable Biomass Feedstock for PennsylvaniaEmily O'Coonahern
 
Dr. André Williamson - The Cost of Increased Regulation to Producers and Cons...
Dr. André Williamson - The Cost of Increased Regulation to Producers and Cons...Dr. André Williamson - The Cost of Increased Regulation to Producers and Cons...
Dr. André Williamson - The Cost of Increased Regulation to Producers and Cons...John Blue
 
Pratt_Michelle_Energy Data Analyst_PurdueExtension
Pratt_Michelle_Energy Data Analyst_PurdueExtensionPratt_Michelle_Energy Data Analyst_PurdueExtension
Pratt_Michelle_Energy Data Analyst_PurdueExtensionMichelle Merlis
 
Research: Welfare and Distributional Implications of Shale Gas
Research: Welfare and Distributional Implications of Shale GasResearch: Welfare and Distributional Implications of Shale Gas
Research: Welfare and Distributional Implications of Shale GasMarcellus Drilling News
 
Production Posibility Curve
Production Posibility CurveProduction Posibility Curve
Production Posibility CurveSomal Fatima
 
Climate change and agriculture in Central America and the Andean region
Climate change and agriculture in Central America and the Andean regionClimate change and agriculture in Central America and the Andean region
Climate change and agriculture in Central America and the Andean regionIFPRI-PIM
 
Economics 160 lecture_8_review-_fall_2012
Economics 160 lecture_8_review-_fall_2012Economics 160 lecture_8_review-_fall_2012
Economics 160 lecture_8_review-_fall_2012Yazminxx
 

Similar to Factors influencing the price of florida bell peppers (for sharing) (20)

Higher Input Prices Result in Greater Economic Incentives for Precision Agric...
Higher Input Prices Result in Greater Economic Incentives for Precision Agric...Higher Input Prices Result in Greater Economic Incentives for Precision Agric...
Higher Input Prices Result in Greater Economic Incentives for Precision Agric...
 
The Organic Advantage by pro cert-volume
The Organic Advantage by pro cert-volumeThe Organic Advantage by pro cert-volume
The Organic Advantage by pro cert-volume
 
Almonds
AlmondsAlmonds
Almonds
 
Copy of Theory of demand and Elasticity (2).pptx
Copy of Theory  of  demand  and Elasticity  (2).pptxCopy of Theory  of  demand  and Elasticity  (2).pptx
Copy of Theory of demand and Elasticity (2).pptx
 
Final Research Poster
Final Research PosterFinal Research Poster
Final Research Poster
 
Farm Bill presentation, University of Minnesota 11-10-16
Farm Bill presentation, University of Minnesota 11-10-16Farm Bill presentation, University of Minnesota 11-10-16
Farm Bill presentation, University of Minnesota 11-10-16
 
Julian R - Using the EcoCrop model and database to forecast impacts of cc
Julian R - Using the EcoCrop model and database to forecast impacts of ccJulian R - Using the EcoCrop model and database to forecast impacts of cc
Julian R - Using the EcoCrop model and database to forecast impacts of cc
 
TitleABC123 Version X1Part 3 Inferential Statist.docx
TitleABC123 Version X1Part 3 Inferential Statist.docxTitleABC123 Version X1Part 3 Inferential Statist.docx
TitleABC123 Version X1Part 3 Inferential Statist.docx
 
Food Miles: Background and Marketing
Food Miles: Background and MarketingFood Miles: Background and Marketing
Food Miles: Background and Marketing
 
Low Emissions Development Strategies (Colombia Feb 20, 2014)
Low Emissions Development Strategies (Colombia Feb 20, 2014)Low Emissions Development Strategies (Colombia Feb 20, 2014)
Low Emissions Development Strategies (Colombia Feb 20, 2014)
 
2014 farm bill powerpoint bjr10-16-14
2014 farm bill   powerpoint bjr10-16-142014 farm bill   powerpoint bjr10-16-14
2014 farm bill powerpoint bjr10-16-14
 
Potential Economic Impact of Sustainable Biomass Feedstock for Pennsylvania
Potential Economic Impact of Sustainable Biomass Feedstock for PennsylvaniaPotential Economic Impact of Sustainable Biomass Feedstock for Pennsylvania
Potential Economic Impact of Sustainable Biomass Feedstock for Pennsylvania
 
Dr. André Williamson - The Cost of Increased Regulation to Producers and Cons...
Dr. André Williamson - The Cost of Increased Regulation to Producers and Cons...Dr. André Williamson - The Cost of Increased Regulation to Producers and Cons...
Dr. André Williamson - The Cost of Increased Regulation to Producers and Cons...
 
Day 5
Day 5Day 5
Day 5
 
O'sullivan ch03
O'sullivan ch03O'sullivan ch03
O'sullivan ch03
 
Pratt_Michelle_Energy Data Analyst_PurdueExtension
Pratt_Michelle_Energy Data Analyst_PurdueExtensionPratt_Michelle_Energy Data Analyst_PurdueExtension
Pratt_Michelle_Energy Data Analyst_PurdueExtension
 
Research: Welfare and Distributional Implications of Shale Gas
Research: Welfare and Distributional Implications of Shale GasResearch: Welfare and Distributional Implications of Shale Gas
Research: Welfare and Distributional Implications of Shale Gas
 
Production Posibility Curve
Production Posibility CurveProduction Posibility Curve
Production Posibility Curve
 
Climate change and agriculture in Central America and the Andean region
Climate change and agriculture in Central America and the Andean regionClimate change and agriculture in Central America and the Andean region
Climate change and agriculture in Central America and the Andean region
 
Economics 160 lecture_8_review-_fall_2012
Economics 160 lecture_8_review-_fall_2012Economics 160 lecture_8_review-_fall_2012
Economics 160 lecture_8_review-_fall_2012
 

Recently uploaded

microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introductionMaksud Ahmed
 
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptxContemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptxRoyAbrique
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Sapana Sha
 
URLs and Routing in the Odoo 17 Website App
URLs and Routing in the Odoo 17 Website AppURLs and Routing in the Odoo 17 Website App
URLs and Routing in the Odoo 17 Website AppCeline George
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxheathfieldcps1
 
Sanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfSanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfsanyamsingh5019
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Krashi Coaching
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxpboyjonauth
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxiammrhaywood
 
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxEmployee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxNirmalaLoungPoorunde1
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactPECB
 
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991RKavithamani
 
mini mental status format.docx
mini    mental       status     format.docxmini    mental       status     format.docx
mini mental status format.docxPoojaSen20
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)eniolaolutunde
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Celine George
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104misteraugie
 
How to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptxHow to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptxmanuelaromero2013
 

Recently uploaded (20)

microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptxContemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
 
URLs and Routing in the Odoo 17 Website App
URLs and Routing in the Odoo 17 Website AppURLs and Routing in the Odoo 17 Website App
URLs and Routing in the Odoo 17 Website App
 
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdfTataKelola dan KamSiber Kecerdasan Buatan v022.pdf
TataKelola dan KamSiber Kecerdasan Buatan v022.pdf
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
 
Staff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSDStaff of Color (SOC) Retention Efforts DDSD
Staff of Color (SOC) Retention Efforts DDSD
 
Sanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfSanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdf
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptx
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
 
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxEmployee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptx
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
 
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
Industrial Policy - 1948, 1956, 1973, 1977, 1980, 1991
 
mini mental status format.docx
mini    mental       status     format.docxmini    mental       status     format.docx
mini mental status format.docx
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
How to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptxHow to Make a Pirate ship Primary Education.pptx
How to Make a Pirate ship Primary Education.pptx
 

Factors influencing the price of florida bell peppers (for sharing)

  • 1. Factors Influencing the Price of Florida Bell Peppers Presented by: BikramAulakh and Catherine Klasne
  • 2. Introduction Imagine you are a typical South Florida farmer with a decision to make about what to plant on your 195 acres.
  • 3.
  • 4. Should you grow bell peppers and, if so, how many should you aim to produce? Beyond pure judgment, is there a way to make these choices, and what model-based formulas or equations would aid in decision-making? What factors influence the price of Floridabell peppers? Considerations
  • 5. Statistics can help! Depending on how much data can be gathered by farms’ marketing and research personnel, and how much time they have to work with it – and on how well they recognize trends and use them to forecast – our models can provide insight.
  • 6. Our data We found 37 complete observations we could manipulate with ordinary-least-squares software to arrive at useful models that should hold for any year under consideration.
  • 7. The unit of observation The state of Florida in one year.
  • 8. The time period covered: 1960 to 2006.
  • 9. Variables in the following classifications were tested:1. Supply U.S. annual production of bell peppers, U.S. annual imports and U.S. total annual supply, all in millions of pounds U.S. annual exports in millions of pounds U.S. acreage planted in bell peppers and U.S.acreage on which bell peppers were harvested annually Independent variables:
  • 10. More supply-related variables: Total annual U.S. yield in hundredweights Florida acreage planted annually in bell peppers and acreage within the state on which bell peppers were harvested annually Florida’s total production in thousands of hundredweights (or yield in hundredweights) Independent variables:
  • 11. 2. Demand U.S. domestic consumption of bell peppers in millions of pounds annually Average U.S. population in millions U.S. annual per capita use in pounds Independent variables:
  • 12. 3. Related pricing Average U.S. price of bell peppers per hundredweight U.S. farm value per unit in dollars per hundredweight Average price of bell peppers in dollars per hundredweight in a competing state, such as California Independent variables:
  • 13. Data sources: Lucier & Toland, U.S. Bell and Chile Pepper Statistics, Stock #2007-001, 2008. This data set contains 67 tables concerning bell peppers, “also known as green, sweet or nonpungent,” in the U.S. and various states, covering acreage, yield, production, price, crop value and per capita use.
  • 14. Regression estimations A brief trip through our model iterations
  • 15. MINITAB eliminated three of the possible independent variables for which data existed, leaving us with an equation that contained a constant and 19 other X’s: FL farm value per unit ($/cwt) = 25.8 - 21.9 US production (Mlbs) - 21.9 US imports (Mlbs) + 21.9 US exports (Mlbs) +21.9US domestic use (Mlbs) + 2.17 US price ($/cwt)- 0.136 US population (Millions) - 11.4 US per capita use (lbs)+ 0.000121 US planted acreage + 0.000290 US harvested acreage+ 0.109 US yield (cwt) + 0.000596 FL planted acreage - 0.00137 FL harvested acreage + 0.00452 FL production (1000 cwt) - 0.0683 FL yield (cwt) - 0.00255 Cal planted acreage +0.00253 Cal harvested acreage + 0.00195 Cal production(1000 cwt) - 0.0135 Cal yield (cwt) - 1.09 Cal farm valueper unit ($/cwt) Initial model
  • 16. Comment The model passes the F-test with a p-value of 0.000, less than the tolerated maximum value of .05, probably meaning at least one of the independent variables affects the dependent variable. The adjusted R2is 99.4% -- variations in the independent variables probably explain 99.4% of the variation in the dependent variable, Florida farm value in units of dollars perhundredweight. Only about 0.6% of the variation in Y is left to be explained. Coefficient signs indicate a (+) positive or direct relationship between an X and the dependent variable or a (-) negative or inverse relationship between the particular X and the Y, or dependent variable.
  • 17. In reviewing the analysis based on p-values and rho, we decided to weed out 13 more variables. Refining the model
  • 18. We plotted the 19 IV’s individually against the DV; most plots were nonlinear, with the exception of that for US price against FL farm value per unit. Correct functional form? Omitted variables?
  • 19. Nonlinear v. Linear We successfully converted the other 18 relationships into linear or straight-line graphs.
  • 20. Our correlation matrix helped us to decide to keep one of the variables. Multicollinearity
  • 21. We realized that even 19 variables could be an unwieldy model for practical purposes and wanted to drop some of them. However, the variance inflation factor is greater than 10 for all of the independent variables, meaning they all could be considered for our final model. Multicollinearity
  • 22. With a constant and 6 independent variables, the equation looks like this: FL farm value per unit ($/cwt) = 3.70+ 0.00366 US imports (Mlbs)+ 2.27 US price ($/cwt)+ 0.633 US per capita use (lbs)+ 0.000344 FL planted acreage- 0.000835 FL harvested acreage - 1.21 Cal farm value per unit ($/cwt) Final model
  • 23. The adjusted R2 of 97.3% means the variation in the independent variables for the final model probably explains 97.3% of the variation in the dependent variable; about 2.7% remains to be explained. Based on the equation and individual T-test p-values, we can say:+ 0.00366 US imports (Mlbs): For every increase of 1 million pounds of imported bell peppers, we can be 55.9% confident the price of purchasing a hundredweight (100 pounds) of bell peppers from a Florida farmer rises by about 4 cents on averagefor that year. Final model evaluation
  • 24. Interpretation of coefficients continued:+ 0.633US per capita use (lbs): For every increase of 1 pound in per capita consumption of bell peppers in the United States, Florida farmers will be paid an average of 63 cents more for a hundredweight of bell peppers (75.3% confidence).+ 0.000344 FL planted acreage: For every additional Florida acre planted in bell peppers, Florida farmers will be paid about 1/3 cent more for a hundredweight of bell peppers (64.5% confidence). Final model evaluation
  • 25. Interpretation of coefficients continued: - 0.000835 FL harvested acreage: For every 1-acre increase in Florida land on which bell peppers are harvested, the Florida farmers’ price falls by less than a tenth of a cent per hundredweight (93.4% confidence).- 1.21 Cal farm value per unit ($/cwt): For each additional dollar California farmers are paid for a hundredweight of bell peppers, Florida farmers’are paid $1.21 less (nearly 100% confidence). Final model evaluation
  • 26. Additional analysis shows that we could further refine our model, but we believe the VIF values favor keeping all six remaining X’s. Final model evaluation
  • 27. Summary A useful and highly relevant model
  • 28. All of our models, generally, are highly relevant. By plugging the required values into our equations, Florida bell-pepper farmers can achieve results that will be of use in determining, for example: How many bell peppers to aim at growing and reasonable asking prices. How many laborers to hire to harvest this quantity. How much fertilizer, pesticide and other supplies to purchase. The factors that influence the price of bell peppers produced on Florida farms. We have shown, in a quantifiable way, the direction and strength of these influences – instructive information for the state’s major bell pepper growers.
  • 29. Our study answered our major questions well and provided some additional insights.

Editor's Notes

  1. Departing from the research paper format a bit for compression