Calculate the pooled estimate of variance

In summary, the data suggests that there is a difference in mean weight between boys and girls. The data suggests that the mean weight of boys is less than the mean weight of girls.
  • #1
chwala
Gold Member
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Homework Statement
See attached
Relevant Equations
stats
1648466908376.png

OK, Let me attempt part (i), first,
Here we have;
##s^2_p ##=##\dfrac{ (n_1-1)s_1^2+(n_2-1)s_2^2}{n_1+n_2-2}##

##s^2_p ##=##\dfrac{ (7-1)0.63953+(7-1)0.6148}{7+7-2}##

##s^2_p ##=##\dfrac{3.83718+3.6888}{12}##

##s^2_p ##=##\dfrac{3.83718+3.6888}{12}##

##s^2_p ##=##\dfrac{7.52598}{12}##

##s^2_p ##=##0.627165## its located between the two original variances... correct? i do not have markscheme nor solutions...

* Reading on this topic now...the literature is really confusing on the so called population data (presumably all the n values in a given data set) and sample data...
 
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  • #2
i) Your sample sizes are uniform, so there's no need for weighted averages here. Find the variance estimates for both populations and then take their arithmetic mean.
 
  • #3
nuuskur said:
i) Your sample sizes are uniform, so there's no need for weighted averages here. Find the variance estimates for both populations and then take their arithmetic mean.
Ok mate i.e ##\dfrac {0.63953+0.6148}{2}##= ##\dfrac {1.25433}{2}=0.627165## .

For part (ii), Let, ##μ_1## and##μ_2## be the mean for boys and girls respectively, then
We want to test the hypothesis; as per the question...

##H_0##: ##μ_1##=##μ_2## - Mean weight of boys is equal to mean weight of girls.
##H_A##: ##μ_1##<##μ_2## - Mean weight of boys is less than the mean weight of girls.

Using the t-statistic and also considering that dof =##12##and ##α=0.05## then it follows that the critical value = ##-1.782##
We shall therefore have,
##t##=##\dfrac {2.5429-3.18571}{\sqrt{(0.627165^2(\frac {1}{6}+\frac {1}{6})}}##=##\dfrac {2.5429-3.18571}{0.256040263}##=##-2.5105 < -1.782 ## We thefore Reject the Null hypothesis and accept the Alternative hypothesis.
 
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  • #4
Your notation is overloaded. You likely mean ##\mu _1## for boys and ##\mu _2## for girls. Hypotheses are logical negations of one another. This is not the case right now.
 
  • #5
nuuskur said:
Your notation is overloaded. You likely mean ##\mu _1## for boys and ##\mu _2## for girls. Hypotheses are logical negations of one another. This is not the case right now.
Amended ...sorry was a bit busy...
 
  • #6
nuuskur said:
Your notation is overloaded. You likely mean ##\mu _1## for boys and ##\mu _2## for girls. Hypotheses are logical negations of one another. This is not the case right now.
Is it now correct?
 

1. What is the purpose of calculating the pooled estimate of variance?

The pooled estimate of variance is used to combine the variances from multiple samples into a single, overall estimate. This is helpful in statistical analysis as it allows for a more accurate representation of the variability within a population.

2. How is the pooled estimate of variance calculated?

The pooled estimate of variance is calculated by taking the weighted average of the individual sample variances. The weights are determined by the sample sizes, with larger sample sizes having a greater influence on the overall estimate.

3. Why is the pooled estimate of variance preferred over individual sample variances?

The pooled estimate of variance is preferred because it takes into account the variability within each sample as well as the variability between samples. This provides a more accurate estimate of the overall population variance, as it is not heavily influenced by extreme values in any one sample.

4. Can the pooled estimate of variance be used for any type of data?

The pooled estimate of variance is most commonly used for continuous, numerical data. It assumes that the data follows a normal distribution and that the samples are independent of each other. It may not be appropriate for non-normal or categorical data.

5. How can the pooled estimate of variance be interpreted?

The pooled estimate of variance is typically expressed as the square of the standard deviation. This means that a smaller pooled estimate of variance indicates less variability within the population, while a larger estimate indicates more variability. It is important to consider the context of the data and the research question when interpreting the pooled estimate of variance.

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