**PlantGrowth Dataset Analysis Using R****Introduction**The "PlantGrowth" dataset is a built-in dataset in R, which can be referred to for more information using the R help documentation. This dataset allows for the statistical analysis of plant yields in different groups. The following guide explains how to perform a t-test to compare two sets of data: `ctrl` (the plant yield in the control group) and `trt2` (the plant yield in the treatment group).**Code Explanation**Here's the code to perform the t-test:```rdata("PlantGrowth")ctrl = PlantGrowth$weight[PlantGrowth$group =="ctrl"]trt2 = PlantGrowth$weight[PlantGrowth$group =="trt2"]t.test(ctrl, trt2)```**Steps to Analyze the Output**1. **Test Type**: Determine whether the test is one-sample or two-sample. In this case, the test is a two-sample test comparing two independent groups: `ctrl` and `trt2`.2. **Paired or Unpaired**: Decide if the test should be paired or unpaired. Here, it is an unpaired test as the samples in `ctrl` and `trt2` are independent of each other.3. **One-sided or Two-sided**: Decide if the hypothesis should be one-sided or two-sided. This depends on whether the research question involves a direction (e.g., greater than) or simply a difference (e.g., not equal).4. **Hypothesis Writing**: - Null Hypothesis (H0): There is no difference in plant yield between the control and treatment groups. - Alternative Hypothesis (H1): There is a difference in plant yield between the control and treatment groups.5. **P-value Interpretation**: Run the above code and interpret the p-value from the output. - If the p-value is less than the significance level (typically 0.05), reject the null hypothesis. - Conclusion: If H0 is rejected, conclude that there is a significant difference in plant yield between the groups.Use this guide to understand how to apply a t-test to the "PlantGrowth" dataset and interpret the results effectively.

3. PlantGrowth is a dataset contained in R. You can refer to the R help document for its information. The following code performs a t-test to compare two vectors, ctrl (the plant yield in the control group) and trt2 (the plant yield in the treatment group). Run the code and interpret the output (one/two sample test? Paired or unpaired ? one/two sided test? Write the hypothesis, read pvalue -> conclusion ,etc...) assuming significance threshold 0.05 data("PlantGrowth") ctrl = PlantGrowth$weight[PlantGrowth$group =="ctrl"] PlantGrowth$weight[PlantGrowth$group=="trt2"] trt2 = t.test(ctrl, trt2)

3. PlantGrowth is a dataset contained in R. You can refer to the R help document for its information. The following code performs a t-test to compare two vectors, ctrl (the plant yield in the control group) and trt2 (the plant yield in the treatment group). Run the code and interpret the output (one/two sample test? Paired or unpaired ? one/two sided test? Write the hypothesis, read pvalue -> conclusion ,etc...) assuming significance threshold 0.05 data("PlantGrowth") ctrl = PlantGrowth$weight[PlantGrowth$group =="ctrl"] PlantGrowth$weight[PlantGrowth$group=="trt2"] trt2 = t.test(ctrl, trt2)

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Question

**PlantGrowth Dataset Analysis Using R**

**Introduction**

The "PlantGrowth" dataset is a built-in dataset in R, which can be referred to for more information using the R help documentation. This dataset allows for the statistical analysis of plant yields in different groups. The following guide explains how to perform a t-test to compare two sets of data: `ctrl` (the plant yield in the control group) and `trt2` (the plant yield in the treatment group).

**Code Explanation**

Here's the code to perform the t-test:

```r
data("PlantGrowth")

ctrl = PlantGrowth$weight[PlantGrowth$group =="ctrl"]
trt2 = PlantGrowth$weight[PlantGrowth$group =="trt2"]

t.test(ctrl, trt2)
```

**Steps to Analyze the Output**

1. **Test Type**: Determine whether the test is one-sample or two-sample. In this case, the test is a two-sample test comparing two independent groups: `ctrl` and `trt2`.

2. **Paired or Unpaired**: Decide if the test should be paired or unpaired. Here, it is an unpaired test as the samples in `ctrl` and `trt2` are independent of each other.

3. **One-sided or Two-sided**: Decide if the hypothesis should be one-sided or two-sided. This depends on whether the research question involves a direction (e.g., greater than) or simply a difference (e.g., not equal).

4. **Hypothesis Writing**:
- Null Hypothesis (H0): There is no difference in plant yield between the control and treatment groups.
- Alternative Hypothesis (H1): There is a difference in plant yield between the control and treatment groups.

5. **P-value Interpretation**: Run the above code and interpret the p-value from the output.
- If the p-value is less than the significance level (typically 0.05), reject the null hypothesis.
- Conclusion: If H0 is rejected, conclude that there is a significant difference in plant yield between the groups.

Use this guide to understand how to apply a t-test to the "PlantGrowth" dataset and interpret the results effectively.

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