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Identify the treatments or factors and levels. A grower wishes to compare three types of fertilizers as they affect crop yield.

Short Answer

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Answer: The treatments (factors) in the experiment are the different fertilizers, and there are three levels, which are the distinct types of fertilizers being compared to analyze their effects on crop yield.

Step by step solution

01

Identify the treatments (factors)

In this exercise, the treatments (factors) are the different fertilizers being compared because they are the main variables that the grower wants to analyze in terms of their effect on crop yield.
02

Identify the levels

The levels are the distinct types of fertilizers being tested in the experiment. The exercise states that the grower wishes to compare three types of fertilizers, so there are three fertilizer levels. In conclusion, the treatments (factors) in this experiment are the fertilizers, and there are three levels of fertilizer types being compared to analyze their effects on crop yield.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Treatments and Factors in Experiments
Understanding 'treatments' and 'factors' is crucial in experimental design. In the context of statistics, treatments refer to the different conditions applied to groups in an experiment. For example, when a grower wants to compare the efficacy of different fertilizers on crop yield, each type of fertilizer represents a different treatment.

Meanwhile, factors are the variables that are manipulated to determine if they cause an effect. In our case, the type of fertilizer is the factor. It is essential to identify these to establish a clear experiment where the outcomes can be attributed to the variations in treatments. By changing the type of fertilizer applied to separate plots of crops, researchers can observe how each treatment influences the growth and yield, allowing them to draw specific conclusions based on empirical evidence.
Levels in Experimental Design
In experimental design, levels are the specific conditions or settings under which a treatment is administered. These levels represent the different variants of a factor that are tested in an experiment. Going back to our fertilizer example, the three different types of fertilizers used are the specific levels within the single factor of 'fertilizer type'.

Choosing the right levels is as critical as selecting the factors. If levels are too similar, they may not show significant differences in the outcome. Conversely, if they are too diverse, the results might not be directly comparable. By appropriately setting levels, researchers can effectively measure the impact of each treatment on the experimental results, such as the yield of crops in agricultural studies.
Effects of Fertilizers on Crop Yield
The effects of fertilizers on crop yield is a significant area of study in agricultural science. Fertilizers contain nutrients that plants need for growth, and different types have varying compositions. By conducting experiments using different fertilizers as treatments, growers can determine which type yields the best results for their crops.

This not only helps in optimizing agricultural production but also in discovering more sustainable farming practices. Key aspects to observe in such experiments include the rate of growth, health of the plants, quality of produce, and the overall yield. The goal is to find a balance where crops receive the nutrients they need without over-fertilization, which can be harmful to both the plants and the environment.

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Most popular questions from this chapter

A builder wants to compare the prices per 1000 board meters of standard or better grade framing lumber. He randomly selects five suppliers in each of the four states where he is planning to begin construction. The prices are given in the table. $$ \begin{array}{rrrr} \hline& {\text { State }} \\ \hline 1 & 2 & 3 & 4 \\ \hline \$ 261 & \$ 236 & \$ 250 & \$ 265 \\ 255 & 240 & 245 & 270 \\ 258 & 225 & 255 & 258 \\ 267 & 233 & 248 & 275 \\ 270 & 240 & 260 & 275 \\ \hline \end{array} $$ a. What type of experimental design has been used? b. Construct the analysis of variance table for this data. c. Do the data provide sufficient evidence to indicate that the average price per 1000 board meters of framing lumber differs among the four states? Test using \(\alpha=.05\)

What is assumed about block and treatment effects in a randomized block design?

Physicians depend on laboratory test results when managing medical problems such as diabetes or epilepsy. In a test for glucose tolerance, three different laboratories were each sent \(n_{t}=5\) identical blood samples from a person who had drunk 50 milligrams (mg) of glucose dissolved in water. The laboratory results (in \(\mathrm{mg} / \mathrm{d} \mathrm{l}\) ) are listed here: $$ \begin{array}{lrl} \hline \text { Lab 1 } & \text { Lab 2 } & \text { Lab 3 } \\ \hline 120.1 & 98.3 & 103.0 \\ 110.7 & 112.1 & 108.5 \\ 108.9 & 107.7 & 101.1 \\ 104.2 & 107.9 & 110.0 \\ 100.4 & 99.2 & 105.4 \\ \hline \end{array} $$ a. Do the data indicate a difference in the average readings for the three laboratories? b. Use Tukey's method for paired comparisons to rank the three treatment means. Use \(\alpha=.05 .\)

Find the tabled values of \(q_{\alpha}(k, d f)\) using the information given. $$ \alpha=.05, k=4, d f=12 $$

A study was conducted to determine the effect of two factors on terrain visualization training for soldiers. \({ }^{5}\) The two factors investigated in the experiment were the participants' spatial abilities (abilities to visualize in three dimensions) and the viewing procedures-active viewing permitted participants to view computer-generated pictures of the terrain from any and all angles, while passive participation gave the participants only a set of preselected pictures of the terrain. Sixty participants were classified into three groups of 20 according to spatial ability (high, medium, and low), and 10 participants within each of these groups were assigned to each of the two training modes, active or passive. The accompanying tables are the ANOVA table computed by the researchers and the table of the treatment means. $$\begin{array}{lrrccc}\hline & & \multicolumn{4}{c} {\text { Error }} \\\\\text { Source } & \text { df } & \text { MS } & \text { df } & \text { F } & \text { p } \\ \hline \text { Main effects: } & & & & & \\\\\text { Training condition } & 1 & 103.7009 & 54 & 3.66 & .0610 \\\\\text { Ability } & 2 & 760.5889 & 54 & 26.87 & .0005 \\\\\text { Interaction: } & & & & & \\\\\text { Training condition } & & & & & \\\\\quad \times \text { Ability } & 2 & 124.9905 & 54 & 4.42 & .0167 \\ \text { Within cells } & 54 & 28.3015 & & & \\\\\hline\end{array}$$ $$\begin{array}{lcl}\hline \multicolumn{3}{c} {\text { Training Condition }} \\\\\hline \text { Spatial Ability } & \text { Active } & \text { Passive } \\\\\hline \text { High } & 17.895 & 9.508 \\\\\text { Medium } & 5.031 & 5.648 \\\\\text { Low } & 1.728 & 1.610 \\\\\hline\end{array}$$ a. Explain how the authors arrived at the degrees of freedom shown in the ANOVA table. b. Are the \(F\) -values correct? c. Interpret the test results. What are their practical implications? d. Use Table 6 in Appendix I to approximate the \(p\) -values for the \(F\) statistics shown in the ANOVA table.

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