Simple linear regression.

This chapter covers the methods for obtaining and expressing these mathematical equations and their confidence bands. The methodology is linear regression analysis. Four types of regression analysis are presented, including: simple linear regression with no repeated measures or replication; simple linear regression with repeated measures; simple linear regression with replication; and polynomial regression. The effects of nitrogen rate on crop yield were presented as example.

The randomized complete block design.

This chapter focuses on randomized complete block design (RCBD). The RCBD can be simple, holding several levels of a single treatment, or complex, holding a complicated factorial. Field experiments may be blocked due to an observed or potential gradient in the field where the experiment will be performed. The yield of four lettuce cultivars was used as an example.

The split-plot and split-split-plot designs.

This chapter focuses on one of the most flexible and useful experimental designs for agricultural research, the split-plot and its variations. An experiment with strawberry production was used as an example.

Analyzing a series of experiments.

In many plant science experiments, normally there are certain factors that can't be control which may influence the outcome of the work. Such factors include weather and pest pressure. In order to get an accurate and reliable measure of crop performance, the experiment is repeated over time, location or both. These repeated experiments should be analyzed in such a fashion that the data from each individual experiment are combined into one grand analysis. This chapter will cover these types of experiments and their analysis. The influence of nitrogen fertilization on broccoli productivity during two seasons, spring and fall, was used as an example.

Regression diagnostics.

This chapter focuses on regression diagnostics. The development of a regression equation is only the first half of a regression analysis. The second, often overlooked part of a regression analysis is to make sure the assumptions underlying the analysis have been met. This is easily accomplished using the regression diagnostic procedures available in SAS® (Statistical Analysis System). Price per flat of strawberries and their availability on the open market were used as an example.

Mean separation (multiple comparison) techniques.

This chapter focuses on mean separation techniques. This technique is used to determine which means in an experiment are different from each other. The differences in yield of four cultivars of cabbage were used as an example.

The Latin square design.

This chapter focuses on Latin square design. The vegetative to floral transition of the apical meristem in main crowns of strawberry plants were investigated. Five treatments was considered: (1) a control; (2) long days (16 hours) at 25°C; (3) long days at 10°C; (4) short days (8 hours) at 25°C; and (5) short days at 10°C. Experiments were set-up as a Latin square dividing each day's work schedule into five segments, thus have five rows (days), five columns (time of day) and five treatments. Results indicates that there was not much variability associated with the day of the week or the time of day for dissection.

Procedures for non-normal data.

This chapter will serve as an introduction to nonparametric statistics with an emphasis on several methods supported by SAS® (Statistical Analysis System). The yield (dozens of ears per hectare) from a new sweetcorn cultivar was used as an example.

Sampling.

This chapter covers sampling techniques for plant science research. It provides information on some of the protocols for sampling from populations without an imposed experimental design as well as from designed experiments.

An introduction to SAS® OnDemand for Academics.

This chapter provide information on the registration and mechanics of SAS® (Statistical Analysis System). SAS® is a software system for data management, analysis and presentation. SAS® is available for different platforms and all versions basically perform the same procedures. The main differences among them are how the user interacts with the software via the operating system. Data set size used to be an issue, often limited by the computer's memory, but that problem hardly exists anymore with today's modern machines.