A student answered 86 problems on a test correctly and received a grade of 98%. How many problems were on the test, if all the p
1 answer:
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Answer:
The weight of an adult male who is 172 cm tall is 89.36 kg.
Step-by-step explanation:
The regression equation representing the relationship between height and weight of a person is:
Here
<em>y</em> = weight of a person
<em>x</em> = height of a person
The information provided is:
The hypothesis to test the significance of the correlation between height and weight is:
<em>H₀</em>: There is no relationship between the height and weight, i.e. <em>ρ</em> = 0.
<em>Hₐ</em>: There is a relationship between the height and weight, i.e. <em>ρ </em>≠ 0.
Decision rule:
If the <em>p</em>-value of the test is less than the significance level, then the null hypothesis will be rejected and vice-versa.
According to information provided:
<em>p</em>-value = 0.023 < <em>α</em> = 0.05
The null hypothesis was rejected at 5% level of significance.
Thus, concluding that there is a relationship between the height and weight.
Compute the weight of an adult male with height, <em>x</em> = 172 cm as follows:
Thus, the weight of an adult male who is 172 cm tall is 89.36 kg.
Answer:
a) For this case the histogram is not too skewed and we can say that is approximately symmetrical so then we can conclude that this dataset is similar to a normal distribution
b) For this case the data is skewed to the left and we can't assume that we have the normality assumption.
c) This last case the histogram is not symmetrical and the data seems to be skewed.
Step-by-step explanation:
For this case we have the following data:
(a)data = c(7,13.2,8.1,8.2,6,9.5,9.4,8.7,9.8,10.9,8.4,7.4,8.4,10,9.7,8.6,12.4,10.7,11,9.4)
We can use the following R code to get the histogram
> x1<-c(7,13.2,8.1,8.2,6,9.5,9.4,8.7,9.8,10.9,8.4,7.4,8.4,10,9.7,8.6,12.4,10.7,11,9.4)
> hist(x1,main="Histogram a)")
The result is on the first figure attached.
For this case the histogram is not too skewed and we can say that is approximately symmetrical so then we can conclude that this dataset is similar to a normal distribution
(b)data = c(2.5,1.8,2.6,-1.9,1.6,2.6,1.4,0.9,1.2,2.3,-1.5,1.5,2.5,2.9,-0.1)
> x2<- c(2.5,1.8,2.6,-1.9,1.6,2.6,1.4,0.9,1.2,2.3,-1.5,1.5,2.5,2.9,-0.1)
> hist(x2,main="Histogram b)")
The result is on the first figure attached.
For this case the data is skewed to the left and we can't assume that we have the normality assumption.
(c)data = c(3.3,1.7,3.3,3.3,2.4,0.5,1.1,1.7,12,14.4,12.8,11.2,10.9,11.7,11.7,11.6)
> x3<-c(3.3,1.7,3.3,3.3,2.4,0.5,1.1,1.7,12,14.4,12.8,11.2,10.9,11.7,11.7,11.6)
> hist(x3,main="Histogram c)")
The result is on the first figure attached.
This last case the histogram is not symmetrical and the data seems to be skewed.
