Type Amount Eaten cm Volume Eaten Premium Blend Black Oiler...

Question

# Type Amount Eaten cm Volume Eaten Premium Blend Black Oiler...

Type
Amount Eaten cm
Volume Eaten
7.5
2944.9
0.5
208.0
0.5
211.9
0.5
208.0
1
417.3
0.5
210.6
3.43
1435.2
4.5
1848.6
2
839.8
1.5
631.8
1.5
612.3
Shelled Peanuts
4.5
1778.6
Shelled Peanuts
1
404.3
Shelled Peanuts
3.5
1392.4
Shelled Peanuts
0.5
201.5
Shelled Peanuts
3
1189.6
Shelled Peanuts
1.5
600.6
Shelled Peanuts
3.525
1439.2
Shelled Peanuts
4
1627.7
Shelled Peanuts
0.5
201.5
Shelled Peanuts
4.5
1825.3
Shelled Peanuts
2.5
994.6

{r load-packages, message=FALSE} library(tidyverse) library(openintro) library(nortest) library(BSDA) library(stats) library(effectsize) ```

### State the null and alternative hypotheses  Ho: Ha:  ###  Input your data into two columns. Column one will contain the categorical variable (Type) and column two will contain the numeric data (Amount Eaten cm). make a dataframe containing those two columns. (you may abbreviate the seed type name to shorten the amount of typing)   ```{r}  ```  ### Use descriptive statistics and data visualization (graphs) that you think are most appropriate to view the data in support of the aim of the study. (Some R commands that you might use are summary(), ggplot2::dotplot/boxplot for comparison)  ```{r}  ```   ### Test the assumption(s) if needed (normality/equal variances)  ```{r code-chunk-label}   ```  ### Run the appropriate statistical test  ```{r}  ```  ### What is your decision and interpretation?  Insert your answer here.   ### ==================================================================================================================== ### make a third column (Volume Eaten). Add this column to the dataframe that you created above. You will now repeat all the above with the new response variable Volumn Eaten. This Volume Eaten was calculated because Birdfeeder C was smaller than Birdfeeders A and B. So, birdfeeder C Amount Eaten was overestimated. Birdfeeder C contained the Supreme Blend Unshelled Sunflower seeds.   ```{r}  ```  ### Use descriptive statistics and data visualization (graphs) that you think are most appropriate to view the data in support of the aim of the study. (Some R commands that you might use are summary(), ggplot2::dotplot/boxplot for comparison)  ```{r}  ```   ### Test the assumptions if needed  ```{r code-chunk-label}   ```  ### Run the appropriate statistical test  ```{r}  ```  ### What is your decision and interpretation?  Insert your answer here.

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