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Mashutka [201]
3 years ago
5

A study was conducted to compare the effect of three diet types on the milk yield of cows (in lbs). The sample size, sample mean

, and sample variance for each method are given below.
Diet A: n1 = 9, X1 = 39.1, s21 = 24.6
Diet B: n2 = 8, X2 = 29.9, s22 = 16.4
Diet C: n3 = 10, X3 = 45.9, s21 = 10.3
(a) Construct an ANOVA table including all relevant sums of squares, mean squares, and degrees of freedom.
(b) Perform an overall F test to determine whether the population means of milk yield are the same or not among the three diet types.

Mathematics
1 answer:
Vlad [161]3 years ago
6 0

Answer:

(a) Anova table is attached below.

(b) The population means of milk yield are different among the three diet types

Step-by-step explanation:

In this case we need to perform a One-way ANOVA to determine whether the effect of three diet types on the milk yield of cows are significantly different or not.

The hypothesis can be defined as follows:

<em>H</em>₀: The effect of three diet types on the milk yield of cows are same.

<em>Hₐ</em>: The effect of three diet types on the milk yield of cows are significantly different.

(a)

The formulas are as follows:

\text{Grand Mean}=\bar x=\frac{1}{3}\sum \bar x_{i}\\\\SSB=\sum n_{i}(\bar x_{i}-\bar x)^{2}\\\\SSW=\sum (n_{i}-1)S^{2}_{i}\\\\N=\sum n_{i}\\\\DF_{B}=k-1\\\\DF_{W}=N-k\\\\DF_{T}=N-1\\

The F critical value is computed using the Excel formula:

F critical value=F.INV.RT(0.05,2,24)

The ANOVA table is attached below.

(b)

The rejection region is defined as follows:

F > F (2, 24) = 3.403

The computed F statistic value is:

F = 34.069

F = 34.269 > F (2, 24) = 3.403

The null hypothesis will be rejected.

Thus, concluding that the population means of milk yield are different among the three diet types

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Answer:

Null hypothesis:p_{1} = p_{2}    

Alternative hypothesis:p_{1} \neq p_{2}    

z=\frac{0.410-0.350}{\sqrt{0.382(1-0.382)(\frac{1}{1000}+\frac{1}{900})}}=2.688    

p_v =2*P(Z>2.688)= 0.0072    

Comparing the p value with the significance level assumed \alpha=0.05 we see that p_v so we can conclude that we have enough evidence to to reject the null hypothesis, and we can say that the proportions analyzed are significantly different at 5% of significance.

Step-by-step explanation:

Data given and notation    

X_{1}=410 represent the number of people indicating that their financial security was more than fair  in 2012

X_{2}=315 represent the number of people indicating that their financial security was more than fair in 2010

n_{1}=1000 sample 1 selected  

n_{2}=900 sample 2 selected  

p_{1}=\frac{410}{1000}=0.410 represent the proportion estimated of people indicating that their financial security was more than fair in 2012

p_{2}=\frac{315}{900}=0.350 represent the proportion estimated of people indicating that their financial security was more than fair in 2010  

\hat p represent the pooled estimate of p

z would represent the statistic (variable of interest)    

p_v represent the value for the test (variable of interest)  

\alpha=0.05 significance level given  

Part a: Concepts and formulas to use    

We need to conduct a hypothesis in order to check if is there is a difference between the two proportions, the system of hypothesis would be:    

Null hypothesis:p_{1} = p_{2}    

Alternative hypothesis:p_{1} \neq p_{2}    

Hypothesis testing

We need to apply a z test to compare proportions, and the statistic is given by:    

z=\frac{p_{1}-p_{2}}{\sqrt{\hat p (1-\hat p)(\frac{1}{n_{1}}+\frac{1}{n_{2}})}}   (1)  

Where \hat p=\frac{X_{1}+X_{2}}{n_{1}+n_{2}}=\frac{410+315}{1000+900}=0.382  

z-test: Is used to compare group means. Is one of the most common tests and is used to determine whether the means of two groups are equal to each other.    

Calculate the statistic  

Replacing in formula (1) the values obtained we got this:    

z=\frac{0.410-0.350}{\sqrt{0.382(1-0.382)(\frac{1}{1000}+\frac{1}{900})}}=2.688    

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Since is a two sided test the p value would be:    

p_v =2*P(Z>2.688)= 0.0072    

Comparing the p value with the significance level assumed \alpha=0.05 we see that p_v so we can conclude that we have enough evidence to to reject the null hypothesis, and we can say that the proportions analyzed are significantly different at 5% of significance.

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