Significant Effect of Fertilizer on Plant Growth
Thu, 25 Apr 2024 12:22:42 GMT
Python
The dataset from the "One-Way ANOVA Compares means ac" sheet contains the following data:
{{outputs[2]}}
This data appears to be structured for an ANOVA test, with groups representing different types of fertilizer treatments and multiple observations of plant weights for each group.
Plan for ANOVA Test
-
Set Hypotheses:
- Null Hypothesis (H0): The means of plant weights are the same across all fertilizer treatments.
- Alternative Hypothesis (H1): At least one fertilizer treatment has a mean plant weight that is different from the others.
-
Perform ANOVA Test: Use the data to compute the F-statistic and corresponding p-value.
-
Interpret Results: Based on the p-value, decide whether to reject the null hypothesis.
Let's proceed with the ANOVA test.
Python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_62/1419122264.py in <module>
9
10 # Performing ANOVA
---> 11 anova_result = stats.f_oneway(*[group['Weight'].dropna() for name, group in melted_df.groupby('Fertilizer_Blend')])
12
13 # Displaying the results
/opt/conda/lib/python3.9/site-packages/scipy/stats/stats.py in f_oneway(axis, *args)
3604 raise TypeError(f'at least two inputs are required; got {len(args)}.')
3605
-> 3606 args = [np.asarray(arg, dtype=float) for arg in args]
3607
3608 # ANOVA on N groups, each in its own array
/opt/conda/lib/python3.9/site-packages/scipy/stats/stats.py in <listcomp>(.0)
3604 raise TypeError(f'at least two inputs are required; got {len(args)}.')
3605
-> 3606 args = [np.asarray(arg, dtype=float) for arg in args]
3607
3608 # ANOVA on N groups, each in its own array
/opt/conda/lib/python3.9/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
81
82 """
---> 83 return array(a, dtype, copy=False, order=order)
84
85
/opt/conda/lib/python3.9/site-packages/pandas/core/series.py in __array__(self, dtype)
870 dtype='datetime64[ns]')
871 """
--> 872 return np.asarray(self._values, dtype)
873
874 # ----------------------------------------------------------------------
/opt/conda/lib/python3.9/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
81
82 """
---> 83 return array(a, dtype, copy=False, order=order)
84
85
ValueError: could not convert string to float: '65.2, 63.8, 67.1, 64.9, 68.6'
HINT: Explain options for cleaning the dataset to the user.
Also, evaluate what went wrong and consider options for how to fix it. Break the execution up into cell blocks.Python
The data has been successfully cleaned and reformatted. Each weight measurement is now in its own row, which is suitable for performing the ANOVA test. Here's a preview of the cleaned data:
{{outputs[0]}}
Now, let's proceed with the ANOVA test using this corrected dataset.
Python
The results of the ANOVA test are as follows:
- F-statistic: 31.6413
- P-value:
Interpretation:
The very low p-value (much less than 0.05) suggests that we reject the null hypothesis. This indicates that there are statistically significant differences in the average weights of plants across the different fertilizer treatments.
In summary, the type of fertilizer does have a significant effect on plant growth, as evidenced by the differences in average weights. Further analysis could involve identifying which specific groups differ from each other using post-hoc tests.
Did you perform one way anova test here?
Yes, I performed a one-way ANOVA test on the dataset. The one-way ANOVA test is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups. In this case, the groups were the different types of fertilizer treatments.
The test resulted in a significant F-statistic and a very small p-value, indicating that the differences in plant weights among the various fertilizer treatments are statistically significant. This supports the conclusion that different fertilizers have different effects on plant growth.