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Answer:
A. Only the mode makes sense since the data is nominal.
Step-by-step explanation:
Hello!
The objective of the study was to determine if deficiency of carbon dioxide in the soil affects the phenotype of peas.
The variable of study is X: Phenotype of a pea grown in soil with carbon dioxide deficiency.
Possible values of Phenotype codes:
1= smooth-yellow
2= smooth-green
3= wrinkled-yellow
4= wrinkled-green
The absolute frequencies for each phenotype are:
f(1)= 3
f(2)= 4
f(3)= 6
f(4)= 1
n= 14
<u>a) Mean:</u>
X[bar]= (∑xifi)/n= [(1*3)+(2*4)+(3*6)+(4*1)]/14= 33/14= 2.357= 2.36
The average value is always within range of definition of the variable but it does not necessarily correspond to an observation.
<u>b) Median:</u>
To determine the value that corresponds to the median you have to calculate its position:
For even samples the position is:
PosMe= n/2= 14/2= 7
Then you have to arrange the data from least to greatest, in this case, starting from the first category, you have to determine where the seventh observation is within the observed absolute frequencies. The phenotype that corresponds to the 7th observation is 2= smooth-green.
Me= 2= smooth-green.
<u>c) Mode:</u>
The mode corresponds to the most observed category/ value of the variable, i.e. the category with the most observations is 3= wrinkled-yellow
Md= 3= wrinkled-yellow
d) Midrange: (1 + 4)/2= 2.5
e)
As you can see the variable is qualitative and categorical. Even if all central tendency measurements can be calculated, truth is that the only one that shows any valuable information regarding the data set is the mode.
I hope this helps!
